{"type":"system","subtype":"init","cwd":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign","session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","tools":["Task","TaskOutput","Bash","Glob","Grep","ExitPlanMode","Read","Edit","Write","NotebookEdit","WebFetch","TodoWrite","WebSearch","TaskStop","AskUserQuestion","Skill","EnterPlanMode","ToolSearch","mcp__snowflake-mcp__test_connection","mcp__snowflake-mcp__list_databases","mcp__snowflake-mcp__list_schemas","mcp__snowflake-mcp__list_tables","mcp__snowflake-mcp__list_views","mcp__snowflake-mcp__describe_table","mcp__snowflake-mcp__read_data","mcp__snowflake-mcp__get_system_health","mcp__snowflake-mcp__describe_query","mcp__snowflake-mcp__execute_async","mcp__snowflake-mcp__get_query_status","mcp__snowflake-mcp__get_async_results","mcp__snowflake-mcp__list_async_queries","mcp__snowflake-mcp__read_data_paginated","mcp__snowflake-mcp__read_data_pandas"],"mcp_servers":[{"name":"Context7","status":"failed"},{"name":"snowflake-mcp","status":"connected"}],"model":"claude-opus-4-6","permissionMode":"bypassPermissions","slash_commands":["keybindings-help","debug","frontend-design","sc:analyze","sc:build","sc:cleanup","sc:design","sc:document","sc:estimate","sc:explain","sc:git","sc:implement","sc:improve","sc:index","sc:load","sc:spawn","sc:task","sc:test","sc:troubleshoot","sc:workflow","compact","context","cost","init","pr-comments","release-notes","review","security-review","insights"],"apiKeySource":"none","claude_code_version":"2.1.32","output_style":"default","agents":["Bash","general-purpose","statusline-setup","Explore","Plan","code-reviewer","documentation-specialist","performance-optimizer","medicines-intelligence-analyst","project-analyst","team-configurator","tech-lead-orchestrator","data-scientist","postgres-expert","sql-pro","django-api-developer","django-backend-expert","django-orm-expert","laravel-backend-expert","laravel-eloquent-expert","actix-expert","android-expert","angular-expert","angularjs-expert","ansible-expert","aspnet-core-expert","astro-expert","auth0-expert","ava-expert","braintree-expert","bullmq-expert","bun-expert","c-expert","cassandra-expert","celery-expert","circleci-expert","clojure-expert","cockroachdb-expert","cpp-expert","csharp-expert","css-expert","cypress-expert","dart-expert","deno-expert","django-expert","docker-expert","dynamodb-expert","elasticsearch-expert","electron-expert","elixir-expert","elk-expert","erlang-expert","expo-expert","express-expert","fastapi-expert","fastify-expert","fiber-expert","flask-expert","flutter-expert","flyway-expert","gin-expert","github-actions-expert","gitlab-ci-expert","go-expert","grafana-expert","graphql-expert","grpc-expert","haskell-expert","html-expert","ios-expert","jasmine-expert","java-expert","javascript-expert","jenkins-expert","jest-expert","jquery-expert","jwt-expert","kafka-expert","keycloak-expert","knex-expert","kotlin-expert","kubernetes-expert","langchain-expert","laravel-expert","liquibase-expert","loki-expert","lua-expert","mariadb-expert","mocha-expert","mongodb-expert","mongoose-expert","mqtt-expert","mssql-expert","mysql-expert","nats-expert","neo4j-expert","nestjs-expert","nextjs-expert","nodejs-expert","numpy-expert","oauth-oidc-expert","ocaml-expert","openai-api-expert","openapi-expert","opensearch-expert","opentelemetry-expert","owasp-top10-expert","pandas-expert","perl-expert","phoenix-expert","php-expert","playwright-expert","prisma-expert","prometheus-expert","pulumi-expert","puppeteer-expert","python-expert","pytorch-expert","rabbitmq-expert","rails-expert","react-expert","react-native-expert","redis-expert","remix-expert","rest-expert","rollup-expert","ruby-expert","rust-expert","scala-expert","scikit-learn-expert","selenium-expert","sequelize-expert","sidekiq-expert","sns-expert","solidjs-expert","spring-boot-expert","sql-expert","sqlite-expert","sqs-expert","stripe-expert","svelte-expert","swift-expert","swiftui-expert","tailwind-expert","tauri-expert","tensorflow-expert","terraform-expert","testcafe-expert","trpc-expert","typeorm-expert","typescript-expert","vector-db-expert","vitest-expert","vue-expert","webpack-expert","websocket-expert","rails-activerecord-expert","rails-api-developer","react-component-architect","react-nextjs-expert","vue-component-architect","vue-nuxt-expert","api-architect","backend-developer","frontend-developer","tailwind-frontend-expert"],"skills":["keybindings-help","debug","frontend-design"],"plugins":[],"uuid":"70fb7fb3-a35a-4954-a255-238886012b3e"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AmHJeFkVboeNcrtAuYLvsq","type":"message","role":"assistant","content":[{"type":"text","text":"\n\nI'll start by reading the required files in order."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":69693,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":69693},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"0194f08b-0e05-4d93-b9d5-d96aa0e6cada"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AmHJeFkVboeNcrtAuYLvsq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01F4x3S4QUyLD3XGDze33rDL","name":"Read","input":{"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\progress.txt"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":69693,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":69693},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"7550d9fe-2b37-4877-b106-dbc5d1c342be"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F4x3S4QUyLD3XGDze33rDL","type":"tool_result","content":"     1# Progress Log - Drug-Aware Indication Matching\n     2\n     3## Project Context\n     4\n     5This project extends the indication-based pathway charts (Phase 1-5 complete) with drug-aware matching.\n     6\n     7**Previous state**: Patients get ONE indication based on their most recent GP diagnosis match (SNOMED cluster codes). This ignores which drugs the patient is taking.\n     8\n     9**New goal**: Match each drug to an indication by cross-referencing the patient's GP diagnoses AND the drug's Search_Term mapping from DimSearchTerm.csv.\n    10\n    11## Key Data/Patterns\n    12\n    13### DimSearchTerm.csv\n    14- Located at `data/DimSearchTerm.csv`\n    15- Columns: Search_Term, CleanedDrugName (pipe-separated), PrimaryDirectorate\n    16- ~165 rows mapping clinical conditions to drug name fragments\n    17- Drug fragments are substrings that match standardized drug names from HCD data\n    18- Some entries have generic fragments: INHALED, CONTINUOUS, STANDARD-DOSE, PEGYLATED\n    19\n    20### Current get_patient_indication_groups() in diagnosis_lookup.py\n    21- Uses CLUSTER_MAPPING_SQL as CTE in Snowflake query\n    22- Returns ONLY the most recent match per patient (QUALIFY ROW_NUMBER() = 1)\n    23- Needs to return ALL matching Search_Terms per patient (remove QUALIFY)\n    24- Batches 500 patients per query\n    25\n    26### Modified UPID approach\n    27- Current: UPID = Provider Code[:3] + PersonKey (e.g., \"RMV12345\")\n    28- New: UPID = original + \"|\" + search_term (e.g., \"RMV12345|rheumatoid arthritis\")\n    29- The pipe delimiter \"|\" is safe because existing UPIDs are alphanumeric\n    30- generate_icicle_chart_indication() treats UPID as an opaque identifier  modified UPIDs work transparently\n    31- The \" - \" delimiter in pathway ids is used for hierarchy levels, not within UPIDs\n    32\n    33### PseudoNHSNoLinked mapping\n    34- HCD data has PseudoNHSNoLinked column that matches PatientPseudonym in GP records\n    35- PersonKey is provider-specific local ID  do NOT use for GP matching\n    36- One PseudoNHSNoLinked can map to multiple UPIDs (multi-provider patients)\n    37- GP match lookup: PseudoNHSNoLinked  list of matched Search_Terms\n    38\n    39### Drug matching logic\n    40- For each HCD row (UPID + Drug Name):\n    41  1. Get patient's GP-matched Search_Terms with code_frequency (via PseudoNHSNoLinked)\n    42  2. Get which Search_Terms list this drug (from DimSearchTerm.csv)\n    43  3. Intersection = valid indications\n    44  4. If 1: use it. If multiple: pick highest code_frequency (most GP coding = most likely indication). If 0: fallback to directory.\n    45- Modified UPID groups drugs under same indication together naturally\n    46- code_frequency = COUNT(*) of matching SNOMED codes per Search_Term per patient in GP records\n    47- GP code time range: only count codes from MIN(Intervention Date) onwards (the HCD data window)\n    48  - Reduces noise from old/irrelevant diagnoses, makes frequency more meaningful\n    49  - Pass earliest_hcd_date as parameter to get_patient_indication_groups()\n    50- Tiebreaker rationale: 47 RA codes vs 2 crohn's codes  RA is clearly the active condition\n    51\n    52### Known edge cases\n    53- Some DimSearchTerm drug fragments are generic (INHALED, ORAL, CONTINUOUS)\n    54  - These could match broadly but are constrained by GP diagnosis requirement\n    55- A patient visiting multiple providers has multiple UPIDs\n    56  - Each UPID gets its own drug-indication matching independently\n    57- Same Search_Term appears twice in DimSearchTerm.csv with different directorates\n    58  - e.g., \"diabetes\"  DIABETIC MEDICINE and OPHTHALMOLOGY\n    59  - For indication charts, we use Search_Term not directorate, so this is fine\n    60\n    61## Iteration Log\n    62\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"beac806e-c22e-4aab-a184-a3ce3c94446f","tool_use_result":{"type":"text","file":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\progress.txt","content":"# Progress Log - Drug-Aware Indication Matching\n\n## Project Context\n\nThis project extends the indication-based pathway charts (Phase 1-5 complete) with drug-aware matching.\n\n**Previous state**: Patients get ONE indication based on their most recent GP diagnosis match (SNOMED cluster codes). This ignores which drugs the patient is taking.\n\n**New goal**: Match each drug to an indication by cross-referencing the patient's GP diagnoses AND the drug's Search_Term mapping from DimSearchTerm.csv.\n\n## Key Data/Patterns\n\n### DimSearchTerm.csv\n- Located at `data/DimSearchTerm.csv`\n- Columns: Search_Term, CleanedDrugName (pipe-separated), PrimaryDirectorate\n- ~165 rows mapping clinical conditions to drug name fragments\n- Drug fragments are substrings that match standardized drug names from HCD data\n- Some entries have generic fragments: INHALED, CONTINUOUS, STANDARD-DOSE, PEGYLATED\n\n### Current get_patient_indication_groups() in diagnosis_lookup.py\n- Uses CLUSTER_MAPPING_SQL as CTE in Snowflake query\n- Returns ONLY the most recent match per patient (QUALIFY ROW_NUMBER() = 1)\n- Needs to return ALL matching Search_Terms per patient (remove QUALIFY)\n- Batches 500 patients per query\n\n### Modified UPID approach\n- Current: UPID = Provider Code[:3] + PersonKey (e.g., \"RMV12345\")\n- New: UPID = original + \"|\" + search_term (e.g., \"RMV12345|rheumatoid arthritis\")\n- The pipe delimiter \"|\" is safe because existing UPIDs are alphanumeric\n- generate_icicle_chart_indication() treats UPID as an opaque identifier  modified UPIDs work transparently\n- The \" - \" delimiter in pathway ids is used for hierarchy levels, not within UPIDs\n\n### PseudoNHSNoLinked mapping\n- HCD data has PseudoNHSNoLinked column that matches PatientPseudonym in GP records\n- PersonKey is provider-specific local ID  do NOT use for GP matching\n- One PseudoNHSNoLinked can map to multiple UPIDs (multi-provider patients)\n- GP match lookup: PseudoNHSNoLinked  list of matched Search_Terms\n\n### Drug matching logic\n- For each HCD row (UPID + Drug Name):\n  1. Get patient's GP-matched Search_Terms with code_frequency (via PseudoNHSNoLinked)\n  2. Get which Search_Terms list this drug (from DimSearchTerm.csv)\n  3. Intersection = valid indications\n  4. If 1: use it. If multiple: pick highest code_frequency (most GP coding = most likely indication). If 0: fallback to directory.\n- Modified UPID groups drugs under same indication together naturally\n- code_frequency = COUNT(*) of matching SNOMED codes per Search_Term per patient in GP records\n- GP code time range: only count codes from MIN(Intervention Date) onwards (the HCD data window)\n  - Reduces noise from old/irrelevant diagnoses, makes frequency more meaningful\n  - Pass earliest_hcd_date as parameter to get_patient_indication_groups()\n- Tiebreaker rationale: 47 RA codes vs 2 crohn's codes  RA is clearly the active condition\n\n### Known edge cases\n- Some DimSearchTerm drug fragments are generic (INHALED, ORAL, CONTINUOUS)\n  - These could match broadly but are constrained by GP diagnosis requirement\n- A patient visiting multiple providers has multiple UPIDs\n  - Each UPID gets its own drug-indication matching independently\n- Same Search_Term appears twice in DimSearchTerm.csv with different directorates\n  - e.g., \"diabetes\"  DIABETIC MEDICINE and OPHTHALMOLOGY\n  - For indication charts, we use Search_Term not directorate, so this is fine\n\n## Iteration Log\n","numLines":62,"startLine":1,"totalLines":62}}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AmHJeFkVboeNcrtAuYLvsq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H4MUkGEtVTgC4r4KagJZWB","name":"Read","input":{"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":69693,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":69693},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"ab84b832-6d9d-4c56-bfd4-9220ca73acd7"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H4MUkGEtVTgC4r4KagJZWB","type":"tool_result","content":"     1# Implementation Plan - Drug-Aware Indication Matching\n     2\n     3## Project Overview\n     4\n     5Update the indication-based pathway charts so that patient indications are matched **per drug**, not just per patient. Currently, each patient gets ONE indication (most recent GP diagnosis match). This ignores which drugs the patient is actually taking.\n     6\n     7### The Problem\n     8\n     9A patient on ADALIMUMAB + OMALIZUMAB currently gets assigned a single indication (e.g., \"rheumatoid arthritis\"  the most recent GP match). But:\n    10- ADALIMUMAB is used for rheumatoid arthritis, axial spondyloarthritis, crohn's disease, etc.\n    11- OMALIZUMAB is used for asthma, allergic asthma, urticaria\n    12\n    13These are different clinical pathways and should be treated as separate treatment journeys.\n    14\n    15### The Solution\n    16\n    17Match each drug to an indication by cross-referencing:\n    181. **GP diagnosis**  which Search_Terms the patient has matching SNOMED codes for\n    192. **Drug mapping**  which Search_Terms list each drug (from `DimSearchTerm.csv`)\n    20\n    21Only assign a drug to an indication if BOTH conditions are met. If a patient's drugs map to different indications, they become separate pathways (via modified UPID).\n    22\n    23### Key Design Decisions\n    24\n    25| Aspect | Decision |\n    26|--------|----------|\n    27| Drug-indication source | `data/DimSearchTerm.csv`  Search_Term  CleanedDrugName mapping |\n    28| UPID modification | `{original_UPID}\\|{search_term}` for drugs with matched indication |\n    29| GP diagnosis matching | Return ALL matches per patient (not just most recent) |\n    30| Drug matching | Substring match: HCD drug name contains DimSearchTerm fragment |\n    31| Multiple indication matches per drug | Use highest GP code frequency as tiebreaker (COUNT of matching SNOMED codes per Search_Term) |\n    32| GP code time range | Only codes from MIN(Intervention Date) onwards  restricts to HCD data window |\n    33| No indication match | Fallback to directory (same as current behavior) |\n    34| Same patient, different indications | Separate pathways via different modified UPIDs |\n    35\n    36### Examples\n    37\n    38**Patient on ADALIMUMAB + GOLIMUMAB, GP dx: axial spondyloarthritis + asthma**\n    39- axial spondyloarthritis drug list includes both ADALIMUMAB and GOLIMUMAB\n    40-  Both drugs grouped under \"axial spondyloarthritis\", single pathway\n    41- Modified UPID: `RMV12345|axial spondyloarthritis`\n    42\n    43**Patient on ADALIMUMAB + OMALIZUMAB, GP dx: axial spondyloarthritis + asthma**\n    44- axial spondyloarthritis lists ADALIMUMAB but not OMALIZUMAB\n    45- asthma lists OMALIZUMAB but not ADALIMUMAB\n    46-  Two separate pathways:\n    47  - `RMV12345|axial spondyloarthritis` with ADALIMUMAB\n    48  - `RMV12345|asthma` with OMALIZUMAB\n    49\n    50**Patient on ADALIMUMAB, GP dx: rheumatoid arthritis (47 codes) + crohn's disease (2 codes)**\n    51- Both Search_Terms list ADALIMUMAB AND patient has GP dx for both\n    52-  Tiebreaker: highest code frequency  rheumatoid arthritis has 47 matching SNOMED codes vs 2 for crohn's\n    53-  Single pathway under rheumatoid arthritis (more clinical activity = more likely the treatment indication)\n    54\n    55---\n    56\n    57## Phase 1: Update Snowflake Query & Drug Mapping\n    58\n    59### 1.1 Update `get_patient_indication_groups()` to return ALL matches with frequency\n    60- [ ] Modify the Snowflake query in `get_patient_indication_groups()` (diagnosis_lookup.py):\n    61  - Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`\n    62  - Return ALL matching Search_Terms per patient with code frequency:\n    63    ```sql\n    64    SELECT pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n    65           aic.Search_Term AS \"Search_Term\",\n    66           COUNT(*) AS \"code_frequency\"\n    67    FROM PrimaryCareClinicalCoding pc\n    68    JOIN AllIndicationCodes aic ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\n    69    WHERE pc.\"PatientPseudonym\" IN (...)\n    70      AND pc.\"EventDateTime\" >= :earliest_hcd_date\n    71    GROUP BY pc.\"PatientPseudonym\", aic.Search_Term\n    72    ```\n    73  - `code_frequency` = number of matching SNOMED codes per Search_Term per patient\n    74  - Higher frequency = more clinical activity = stronger signal for tiebreaker\n    75  - `earliest_hcd_date` = `MIN(Intervention Date)` from the HCD DataFrame  restricts GP codes to the HCD data window, reducing noise from old/irrelevant diagnoses\n    76- [ ] Accept `earliest_hcd_date` parameter in `get_patient_indication_groups()` and pass to query\n    77- [ ] Keep batch processing (500 patients per query)\n    78- [ ] Update return type: DataFrame now has multiple rows per patient (PatientPseudonym, Search_Term, code_frequency)\n    79- [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)\n    80\n    81### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n    82- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n    83  - Loads `data/DimSearchTerm.csv`\n    84  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n    85  - Also builds reverse: `search_term  list[drug_fragments]`\n    86  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n    87- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n    88  - Checks if the drug name matches any fragment in the Search_Term's drug list\n    89  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n    90- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"\n    91\n    92---\n    93\n    94## Phase 2: Drug-Aware Indication Matching Logic\n    95\n    96### 2.1 Create `assign_drug_indications()` function\n    97- [ ] Add to `diagnosis_lookup.py` or `pathway_pipeline.py`:\n    98  ```\n    99  def assign_drug_indications(\n   100      df: pd.DataFrame,              # HCD data with UPID, Drug Name columns\n   101      gp_matches_df: pd.DataFrame,   # PatientPseudonym  list of matched Search_Terms\n   102      drug_mapping: dict,             # From load_drug_indication_mapping()\n   103  ) -> tuple[pd.DataFrame, pd.DataFrame]:\n   104      Returns: (modified_df, indication_df)\n   105      - modified_df: HCD data with UPID replaced by {UPID}|{indication}\n   106      - indication_df: mapping modified_UPID  Search_Term\n   107  ```\n   108- [ ] Logic per UPID + Drug Name pair:\n   109  1. Get patient's GP-matched Search_Terms with code_frequency (from gp_matches_df via PseudoNHSNoLinked)\n   110  2. Get which Search_Terms include this drug (from drug_mapping)\n   111  3. Intersection = valid indications for this drug-patient pair\n   112  4. If 1 match: use it\n   113  5. If multiple matches: use highest code_frequency as tiebreaker (most GP coding activity = most likely treatment indication)\n   114  6. If 0 matches: use fallback directory\n   115- [ ] Modify UPID in df rows: `{original_UPID}|{matched_search_term}`\n   116- [ ] Build indication_df: `{modified_UPID}`  `Search_Term` (or fallback label)\n   117- [ ] Verify: Function compiles, handles edge cases (no GP match, no drug match)\n   118\n   119### 2.2 Handle tiebreaker for multiple indication matches\n   120- [ ] When a drug matches multiple Search_Terms AND patient has GP dx for multiple:\n   121  - Use `code_frequency` from the GP query (COUNT of matching SNOMED codes per Search_Term)\n   122  - Higher code_frequency = more clinical activity for that condition = more likely treatment indication\n   123  - E.g., patient with 47 RA codes and 2 crohn's codes  ADALIMUMAB assigned to RA\n   124  - code_frequency is already returned by the updated query in Task 1.1\n   125- [ ] Verify: Tiebreaker logic correctly picks highest-frequency diagnosis\n   126- [ ] Verify: Tie on frequency (rare but possible) falls back to alphabetical Search_Term for determinism\n   127\n   128---\n   129\n   130## Phase 3: Pipeline Integration\n   131\n   132### 3.1 Update `refresh_pathways.py` indication processing\n   133- [ ] In the `elif current_chart_type == \"indication\":` block:\n   134  1. Call `get_patient_indication_groups()` as before (but now returns ALL matches)\n   135  2. Load drug mapping: `drug_mapping = load_drug_indication_mapping()`\n   136  3. Call `assign_drug_indications(df, gp_matches_df, drug_mapping)`\n   137  4. Use modified_df (with indication-aware UPIDs) for pathway processing\n   138  5. Use indication_df for the indication mapping\n   139- [ ] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`\n   140- [ ] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`\n   141\n   142### 3.2 Test with dry run\n   143- [ ] Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`\n   144- [ ] Verify:\n   145  - Modified UPIDs appear in pipeline log (e.g., `RMV12345|rheumatoid arthritis`)\n   146  - Patient counts are reasonable (will be higher than before since same patient can appear under multiple indications)\n   147  - Drug-indication matching is logged (match rate, fallback rate)\n   148  - Pathway hierarchy shows drug-specific grouping under correct indications\n   149\n   150---\n   151\n   152## Phase 4: Full Refresh & Validation\n   153\n   154### 4.1 Full refresh with both chart types\n   155- [ ] Run `python -m cli.refresh_pathways --chart-type all`\n   156- [ ] Verify:\n   157  - Both chart types generate data\n   158  - Directory charts unchanged (no modified UPIDs)\n   159  - Indication charts reflect drug-aware matching\n   160\n   161### 4.2 Validate indication chart correctness\n   162- [ ] Check that drugs under an indication all appear in that Search_Term's drug list\n   163- [ ] Verify that a patient on drugs for different indications creates separate pathway branches\n   164- [ ] Verify that drugs sharing an indication are grouped in the same pathway\n   165- [ ] Log: patient count comparison (old vs new approach)\n   166\n   167### 4.3 Validate Reflex UI\n   168- [ ] Run `python -m reflex compile` to verify app compiles\n   169- [ ] Verify chart type toggle still works\n   170- [ ] Verify indication chart shows correct hierarchy\n   171\n   172---\n   173\n   174## Completion Criteria\n   175\n   176All tasks marked `[x]` AND:\n   177- [ ] App compiles without errors (`reflex compile` succeeds)\n   178- [ ] Both chart types generate pathway data\n   179- [ ] Indication charts show drug-specific indication matching\n   180- [ ] Drugs under the same indication for the same patient are in one pathway\n   181- [ ] Drugs under different indications for the same patient create separate pathways\n   182- [ ] Fallback works for drugs with no indication match\n   183- [ ] Full refresh completes successfully\n   184- [ ] Existing directory charts are unaffected\n   185\n   186---\n   187\n   188## Reference\n   189\n   190### DimSearchTerm.csv Structure\n   191```\n   192Search_Term,CleanedDrugName,PrimaryDirectorate\n   193rheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|...,RHEUMATOLOGY\n   194asthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\n   195```\n   196\n   197### Modified UPID Format\n   198```\n   199Original:  RMV12345\n   200Modified:  RMV12345|rheumatoid arthritis\n   201Fallback:  RMV12345|RHEUMATOLOGY (no GP dx)\n   202```\n   203\n   204### Current vs New Indication Flow\n   205```\n   206CURRENT:\n   207  Patient  GP dx (most recent)  single Search_Term  one pathway\n   208\n   209NEW:\n   210  Patient + Drug A  GP dx matching Drug A  Search_Term X\n   211  Patient + Drug B  GP dx matching Drug B  Search_Term Y\n   212   If X == Y: one pathway under X\n   213   If X != Y: two pathways (modified UPIDs)\n   214```\n   215\n   216### Key Files\n   217\n   218| File | Changes |\n   219|------|---------|\n   220| `data_processing/diagnosis_lookup.py` | Update query, add drug mapping functions |\n   221| `data_processing/pathway_pipeline.py` | Possibly minor changes for modified UPIDs |\n   222| `cli/refresh_pathways.py` | Integrate drug-aware matching into pipeline |\n   223| `data/DimSearchTerm.csv` | Reference data (read-only) |\n   224| `analysis/pathway_analyzer.py` | No changes expected (UPID changes are transparent) |\n   225| `pathways_app/pathways_app.py` | No changes expected |\n   226\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"74e2d991-28c6-4518-b9ab-018ee80ffe2b","tool_use_result":{"type":"text","file":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md","content":"# Implementation Plan - Drug-Aware Indication Matching\n\n## Project Overview\n\nUpdate the indication-based pathway charts so that patient indications are matched **per drug**, not just per patient. Currently, each patient gets ONE indication (most recent GP diagnosis match). This ignores which drugs the patient is actually taking.\n\n### The Problem\n\nA patient on ADALIMUMAB + OMALIZUMAB currently gets assigned a single indication (e.g., \"rheumatoid arthritis\"  the most recent GP match). But:\n- ADALIMUMAB is used for rheumatoid arthritis, axial spondyloarthritis, crohn's disease, etc.\n- OMALIZUMAB is used for asthma, allergic asthma, urticaria\n\nThese are different clinical pathways and should be treated as separate treatment journeys.\n\n### The Solution\n\nMatch each drug to an indication by cross-referencing:\n1. **GP diagnosis**  which Search_Terms the patient has matching SNOMED codes for\n2. **Drug mapping**  which Search_Terms list each drug (from `DimSearchTerm.csv`)\n\nOnly assign a drug to an indication if BOTH conditions are met. If a patient's drugs map to different indications, they become separate pathways (via modified UPID).\n\n### Key Design Decisions\n\n| Aspect | Decision |\n|--------|----------|\n| Drug-indication source | `data/DimSearchTerm.csv`  Search_Term  CleanedDrugName mapping |\n| UPID modification | `{original_UPID}\\|{search_term}` for drugs with matched indication |\n| GP diagnosis matching | Return ALL matches per patient (not just most recent) |\n| Drug matching | Substring match: HCD drug name contains DimSearchTerm fragment |\n| Multiple indication matches per drug | Use highest GP code frequency as tiebreaker (COUNT of matching SNOMED codes per Search_Term) |\n| GP code time range | Only codes from MIN(Intervention Date) onwards  restricts to HCD data window |\n| No indication match | Fallback to directory (same as current behavior) |\n| Same patient, different indications | Separate pathways via different modified UPIDs |\n\n### Examples\n\n**Patient on ADALIMUMAB + GOLIMUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis drug list includes both ADALIMUMAB and GOLIMUMAB\n-  Both drugs grouped under \"axial spondyloarthritis\", single pathway\n- Modified UPID: `RMV12345|axial spondyloarthritis`\n\n**Patient on ADALIMUMAB + OMALIZUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis lists ADALIMUMAB but not OMALIZUMAB\n- asthma lists OMALIZUMAB but not ADALIMUMAB\n-  Two separate pathways:\n  - `RMV12345|axial spondyloarthritis` with ADALIMUMAB\n  - `RMV12345|asthma` with OMALIZUMAB\n\n**Patient on ADALIMUMAB, GP dx: rheumatoid arthritis (47 codes) + crohn's disease (2 codes)**\n- Both Search_Terms list ADALIMUMAB AND patient has GP dx for both\n-  Tiebreaker: highest code frequency  rheumatoid arthritis has 47 matching SNOMED codes vs 2 for crohn's\n-  Single pathway under rheumatoid arthritis (more clinical activity = more likely the treatment indication)\n\n---\n\n## Phase 1: Update Snowflake Query & Drug Mapping\n\n### 1.1 Update `get_patient_indication_groups()` to return ALL matches with frequency\n- [ ] Modify the Snowflake query in `get_patient_indication_groups()` (diagnosis_lookup.py):\n  - Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`\n  - Return ALL matching Search_Terms per patient with code frequency:\n    ```sql\n    SELECT pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n           aic.Search_Term AS \"Search_Term\",\n           COUNT(*) AS \"code_frequency\"\n    FROM PrimaryCareClinicalCoding pc\n    JOIN AllIndicationCodes aic ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\n    WHERE pc.\"PatientPseudonym\" IN (...)\n      AND pc.\"EventDateTime\" >= :earliest_hcd_date\n    GROUP BY pc.\"PatientPseudonym\", aic.Search_Term\n    ```\n  - `code_frequency` = number of matching SNOMED codes per Search_Term per patient\n  - Higher frequency = more clinical activity = stronger signal for tiebreaker\n  - `earliest_hcd_date` = `MIN(Intervention Date)` from the HCD DataFrame  restricts GP codes to the HCD data window, reducing noise from old/irrelevant diagnoses\n- [ ] Accept `earliest_hcd_date` parameter in `get_patient_indication_groups()` and pass to query\n- [ ] Keep batch processing (500 patients per query)\n- [ ] Update return type: DataFrame now has multiple rows per patient (PatientPseudonym, Search_Term, code_frequency)\n- [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)\n\n### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n  - Checks if the drug name matches any fragment in the Search_Term's drug list\n  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"\n\n---\n\n## Phase 2: Drug-Aware Indication Matching Logic\n\n### 2.1 Create `assign_drug_indications()` function\n- [ ] Add to `diagnosis_lookup.py` or `pathway_pipeline.py`:\n  ```\n  def assign_drug_indications(\n      df: pd.DataFrame,              # HCD data with UPID, Drug Name columns\n      gp_matches_df: pd.DataFrame,   # PatientPseudonym  list of matched Search_Terms\n      drug_mapping: dict,             # From load_drug_indication_mapping()\n  ) -> tuple[pd.DataFrame, pd.DataFrame]:\n      Returns: (modified_df, indication_df)\n      - modified_df: HCD data with UPID replaced by {UPID}|{indication}\n      - indication_df: mapping modified_UPID  Search_Term\n  ```\n- [ ] Logic per UPID + Drug Name pair:\n  1. Get patient's GP-matched Search_Terms with code_frequency (from gp_matches_df via PseudoNHSNoLinked)\n  2. Get which Search_Terms include this drug (from drug_mapping)\n  3. Intersection = valid indications for this drug-patient pair\n  4. If 1 match: use it\n  5. If multiple matches: use highest code_frequency as tiebreaker (most GP coding activity = most likely treatment indication)\n  6. If 0 matches: use fallback directory\n- [ ] Modify UPID in df rows: `{original_UPID}|{matched_search_term}`\n- [ ] Build indication_df: `{modified_UPID}`  `Search_Term` (or fallback label)\n- [ ] Verify: Function compiles, handles edge cases (no GP match, no drug match)\n\n### 2.2 Handle tiebreaker for multiple indication matches\n- [ ] When a drug matches multiple Search_Terms AND patient has GP dx for multiple:\n  - Use `code_frequency` from the GP query (COUNT of matching SNOMED codes per Search_Term)\n  - Higher code_frequency = more clinical activity for that condition = more likely treatment indication\n  - E.g., patient with 47 RA codes and 2 crohn's codes  ADALIMUMAB assigned to RA\n  - code_frequency is already returned by the updated query in Task 1.1\n- [ ] Verify: Tiebreaker logic correctly picks highest-frequency diagnosis\n- [ ] Verify: Tie on frequency (rare but possible) falls back to alphabetical Search_Term for determinism\n\n---\n\n## Phase 3: Pipeline Integration\n\n### 3.1 Update `refresh_pathways.py` indication processing\n- [ ] In the `elif current_chart_type == \"indication\":` block:\n  1. Call `get_patient_indication_groups()` as before (but now returns ALL matches)\n  2. Load drug mapping: `drug_mapping = load_drug_indication_mapping()`\n  3. Call `assign_drug_indications(df, gp_matches_df, drug_mapping)`\n  4. Use modified_df (with indication-aware UPIDs) for pathway processing\n  5. Use indication_df for the indication mapping\n- [ ] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`\n- [ ] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`\n\n### 3.2 Test with dry run\n- [ ] Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`\n- [ ] Verify:\n  - Modified UPIDs appear in pipeline log (e.g., `RMV12345|rheumatoid arthritis`)\n  - Patient counts are reasonable (will be higher than before since same patient can appear under multiple indications)\n  - Drug-indication matching is logged (match rate, fallback rate)\n  - Pathway hierarchy shows drug-specific grouping under correct indications\n\n---\n\n## Phase 4: Full Refresh & Validation\n\n### 4.1 Full refresh with both chart types\n- [ ] Run `python -m cli.refresh_pathways --chart-type all`\n- [ ] Verify:\n  - Both chart types generate data\n  - Directory charts unchanged (no modified UPIDs)\n  - Indication charts reflect drug-aware matching\n\n### 4.2 Validate indication chart correctness\n- [ ] Check that drugs under an indication all appear in that Search_Term's drug list\n- [ ] Verify that a patient on drugs for different indications creates separate pathway branches\n- [ ] Verify that drugs sharing an indication are grouped in the same pathway\n- [ ] Log: patient count comparison (old vs new approach)\n\n### 4.3 Validate Reflex UI\n- [ ] Run `python -m reflex compile` to verify app compiles\n- [ ] Verify chart type toggle still works\n- [ ] Verify indication chart shows correct hierarchy\n\n---\n\n## Completion Criteria\n\nAll tasks marked `[x]` AND:\n- [ ] App compiles without errors (`reflex compile` succeeds)\n- [ ] Both chart types generate pathway data\n- [ ] Indication charts show drug-specific indication matching\n- [ ] Drugs under the same indication for the same patient are in one pathway\n- [ ] Drugs under different indications for the same patient create separate pathways\n- [ ] Fallback works for drugs with no indication match\n- [ ] Full refresh completes successfully\n- [ ] Existing directory charts are unaffected\n\n---\n\n## Reference\n\n### DimSearchTerm.csv Structure\n```\nSearch_Term,CleanedDrugName,PrimaryDirectorate\nrheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|...,RHEUMATOLOGY\nasthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\n```\n\n### Modified UPID Format\n```\nOriginal:  RMV12345\nModified:  RMV12345|rheumatoid arthritis\nFallback:  RMV12345|RHEUMATOLOGY (no GP dx)\n```\n\n### Current vs New Indication Flow\n```\nCURRENT:\n  Patient  GP dx (most recent)  single Search_Term  one pathway\n\nNEW:\n  Patient + Drug A  GP dx matching Drug A  Search_Term X\n  Patient + Drug B  GP dx matching Drug B  Search_Term Y\n   If X == Y: one pathway under X\n   If X != Y: two pathways (modified UPIDs)\n```\n\n### Key Files\n\n| File | Changes |\n|------|---------|\n| `data_processing/diagnosis_lookup.py` | Update query, add drug mapping functions |\n| `data_processing/pathway_pipeline.py` | Possibly minor changes for modified UPIDs |\n| `cli/refresh_pathways.py` | Integrate drug-aware matching into pipeline |\n| `data/DimSearchTerm.csv` | Reference data (read-only) |\n| `analysis/pathway_analyzer.py` | No changes expected (UPID changes are transparent) |\n| `pathways_app/pathways_app.py` | No changes expected |\n","numLines":226,"startLine":1,"totalLines":226}}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AmHJeFkVboeNcrtAuYLvsq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QmDa9ndnDcgrx2HvD33Pcx","name":"Read","input":{"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\guardrails.md"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":69693,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":69693},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"79721215-dee0-4ea6-9ce3-57b42867aa29"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QmDa9ndnDcgrx2HvD33Pcx","type":"tool_result","content":"     1# Guardrails\n     2\n     3Known failure patterns. Read EVERY iteration. Follow ALL of these rules.\n     4If you discover a new failure pattern during your work, add it to this file.\n     5\n     6---\n     7\n     8## Drug-Indication Matching Guardrails\n     9\n    10### Match drugs to indications, not just patients to indications\n    11- **When**: Building the indication mapping for pathway charts\n    12- **Rule**: Each drug must be validated against BOTH the patient's GP diagnoses AND the drug-to-indication mapping from DimSearchTerm.csv. A patient being diagnosed with rheumatoid arthritis does NOT mean all their drugs are for rheumatoid arthritis.\n    13- **Why**: The previous approach assigned ONE indication per patient (most recent GP dx), ignoring which drugs actually treat which conditions. This produced misleading pathways.\n    14\n    15### Use DimSearchTerm.csv for drug-to-Search_Term mapping\n    16- **When**: Determining which Search_Term a drug belongs to\n    17- **Rule**: Load `data/DimSearchTerm.csv`. The `CleanedDrugName` column has pipe-separated drug name fragments. Match HCD drug names against these fragments using substring matching (case-insensitive).\n    18- **Why**: This CSV is the authoritative mapping of which drugs are used for which clinical indications.\n    19\n    20### Use substring matching for drug fragments\n    21- **When**: Matching HCD drug names against DimSearchTerm CleanedDrugName fragments\n    22- **Rule**: Check if any fragment from DimSearchTerm is a SUBSTRING of the HCD drug name (case-insensitive). E.g., \"PEGYLATED\" should match \"PEGYLATED LIPOSOMAL DOXORUBICIN\".\n    23- **Why**: DimSearchTerm contains both full drug names (ADALIMUMAB) and partial fragments (PEGYLATED, INHALED). Exact match would miss the partial ones.\n    24\n    25### Modified UPID uses pipe delimiter\n    26- **When**: Creating indication-aware UPIDs\n    27- **Rule**: Format is `{original_UPID}|{search_term}`. Use pipe `|` as delimiter. Do NOT use ` - ` (hyphen with spaces) as that's used for pathway hierarchy levels in the `ids` column.\n    28- **Why**: The `ids` column uses \" - \" to separate hierarchy levels (e.g., \"N&WICS - NNUH - rheumatoid arthritis - ADALIMUMAB\"). Using the same delimiter in UPIDs would break hierarchy parsing.\n    29\n    30### Return ALL GP matches per patient, not just most recent\n    31- **When**: Querying Snowflake for patient GP diagnoses\n    32- **Rule**: Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`. Return ALL matching Search_Terms per patient with `GROUP BY + COUNT(*)` for code_frequency.\n    33- **Why**: A patient may have GP diagnoses for both rheumatoid arthritis AND asthma. We need ALL matches to cross-reference with their drugs.\n    34\n    35### Restrict GP code lookup to HCD data window\n    36- **When**: Building the WHERE clause for the GP record query\n    37- **Rule**: Add `AND pc.\"EventDateTime\" >= :earliest_hcd_date` where `earliest_hcd_date` is `MIN(Intervention Date)` from the HCD DataFrame. Pass this as a parameter to `get_patient_indication_groups()`.\n    38- **Why**: Old GP codes from years before treatment started add noise. A diagnosis coded 10 years ago may no longer be relevant. Restricting to the HCD window ensures code_frequency reflects recent clinical activity for the conditions being actively treated.\n    39\n    40### Tiebreaker: highest GP code frequency when a drug matches multiple indications\n    41- **When**: A single drug maps to multiple Search_Terms AND the patient has GP dx for multiple\n    42- **Rule**: Use `code_frequency` (COUNT of matching SNOMED codes per Search_Term per patient) from the GP query. The Search_Term with the most matching codes in the patient's GP record wins. If tied, use alphabetical Search_Term for determinism.\n    43- **Why**: E.g., ADALIMUMAB is listed under rheumatoid arthritis, crohn's disease, psoriatic arthritis, etc. A patient with 47 RA codes and 2 crohn's codes is almost certainly on ADALIMUMAB for RA. Frequency of GP coding is a much stronger signal of clinical intent than recency  a recent one-off asthma check doesn't mean ADALIMUMAB is for asthma.\n    44\n    45### Same patient, different indications = separate modified UPIDs\n    46- **When**: A patient's drugs map to different Search_Terms\n    47- **Rule**: Create separate modified UPIDs for each indication. E.g., `RMV12345|rheumatoid arthritis` and `RMV12345|asthma`. These are treated as separate \"patients\" by the pathway analyzer.\n    48- **Why**: This is the core design  drugs for different indications should create separate treatment pathways, even for the same physical patient.\n    49\n    50### Fallback to directory for unmatched drugs\n    51- **When**: A drug doesn't match any Search_Term OR the patient has no GP dx for any of the drug's Search_Terms\n    52- **Rule**: Use fallback format: `{UPID}|{Directory} (no GP dx)`. The indication_df maps this to `\"{Directory} (no GP dx)\"`.\n    53- **Why**: Maintains consistent behavior with the previous approach for patients/drugs without GP diagnosis matches.\n    54\n    55### Don't modify directory chart processing\n    56- **When**: Making changes to the indication matching logic\n    57- **Rule**: Only modify the indication chart path (`elif current_chart_type == \"indication\":`). Directory charts use unmodified UPIDs and directory-based grouping.\n    58- **Why**: Directory charts work correctly and should not be affected by indication matching changes.\n    59\n    60---\n    61\n    62## Snowflake Query Guardrails\n    63\n    64### Use PseudoNHSNoLinked for GP record matching\n    65- **When**: Querying GP records (PrimaryCareClinicalCoding) for patient diagnoses\n    66- **Rule**: Use `PseudoNHSNoLinked` column from HCD data, NOT `PersonKey` (LocalPatientID)\n    67- **Why**: PersonKey is provider-specific local ID. Only PseudoNHSNoLinked matches PatientPseudonym in GP records.\n    68\n    69### Embed cluster query as CTE in Snowflake\n    70- **When**: Looking up patient indications during data refresh\n    71- **Rule**: Use the `CLUSTER_MAPPING_SQL` content as a WITH clause in the patient lookup query\n    72- **Why**: This ensures we always use the complete cluster mapping and don't need local storage\n    73\n    74### Quote mixed-case column aliases in Snowflake SQL\n    75- **When**: Writing SELECT queries that return results to Python code\n    76- **Rule**: Use `AS \"ColumnName\"` (quoted) for any column alias you'll access by name in Python\n    77- **Why**: Snowflake uppercases unquoted identifiers. `SELECT foo AS Search_Term` returns `SEARCH_TERM`, so `row.get('Search_Term')` returns None. Fix: `SELECT foo AS \"Search_Term\"`\n    78\n    79### Build indication_df from all unique UPIDs, not PseudoNHSNoLinked\n    80- **When**: Creating the indication mapping DataFrame for pathway processing\n    81- **Rule**: Use `df.drop_duplicates(subset=['UPID'])` not `drop_duplicates(subset=['PseudoNHSNoLinked'])`\n    82- **Why**: A patient visiting multiple providers has multiple UPIDs. Using unique PseudoNHSNoLinked only maps one UPID per patient, leaving others as NaN.\n    83\n    84---\n    85\n    86## Data Processing Guardrails\n    87\n    88### Copy DataFrames in functions that modify columns\n    89- **When**: Writing functions like `prepare_data()` that modify DataFrame columns\n    90- **Rule**: Always `df = df.copy()` at the start of any function that modifies column values on the input DataFrame\n    91- **Why**: `prepare_data()` mapped Provider Code  Name in-place. When called multiple times on the same DataFrame, only the first call worked. The fix: `df.copy()` prevents destructive mutation.\n    92\n    93### Include chart_type in UNIQUE constraints for pathway_nodes\n    94- **When**: Creating or modifying the pathway_nodes table schema\n    95- **Rule**: The UNIQUE constraint MUST include `chart_type`: `UNIQUE(date_filter_id, chart_type, ids)`\n    96- **Why**: Without `chart_type`, `INSERT OR REPLACE` silently overwrites directory chart nodes when indication chart nodes are inserted.\n    97\n    98### Handle NaN in Directory when building fallback labels\n    99- **When**: Creating fallback indication labels for patients without GP diagnosis match\n   100- **Rule**: Check `pd.notna(directory)` before concatenating to string. Use `\"UNKNOWN (no GP dx)\"` for NaN cases.\n   101- **Why**: NaN handling prevents TypeError and ensures meaningful fallback labels.\n   102\n   103### Use parameterized queries for SQLite\n   104- **When**: Building WHERE clauses with user-selected filters\n   105- **Rule**: Use `?` placeholders and pass params tuple  never string interpolation\n   106- **Why**: Prevents SQL injection and handles special characters in drug/directory names\n   107\n   108### Use existing pathway_analyzer functions\n   109- **When**: Processing pathway data for the icicle chart\n   110- **Rule**: Reuse functions from `analysis/pathway_analyzer.py`  don't reinvent\n   111- **Why**: The existing code handles edge cases (empty groups, statistics calculation, color mapping)\n   112\n   113---\n   114\n   115## Reflex Guardrails\n   116\n   117### Use .to() methods for Var operations in rx.foreach\n   118- **When**: Working with items inside `rx.foreach` render functions\n   119- **Rule**: Use `item.to(int)` for numeric comparisons, `item.to_string()` for text operations\n   120- **Why**: Items from rx.foreach are Var objects, not plain Python values.\n   121\n   122### Use rx.cond for conditional rendering, not Python if\n   123- **When**: Conditionally showing/hiding components or changing styles based on state\n   124- **Rule**: Use `rx.cond(condition, true_component, false_component)`  not Python `if`\n   125- **Why**: Python `if` evaluates at definition time; `rx.cond` evaluates reactively at render time\n   126\n   127---\n   128\n   129## Process Guardrails\n   130\n   131### One task per iteration\n   132- **When**: Temptation to do additional tasks after completing the current one\n   133- **Rule**: Complete ONE task, validate it, commit it, update progress, then stop\n   134- **Why**: Multiple tasks increase error risk and make failures harder to diagnose\n   135\n   136### Never mark complete without validation\n   137- **When**: Task feels \"done\" but hasn't been tested\n   138- **Rule**: All validation tiers must pass before marking `[x]`\n   139- **Why**: \"Feels done\" is not \"is done\"\n   140\n   141### Write explicit handoff notes\n   142- **When**: Every iteration, before stopping\n   143- **Rule**: The \"Next iteration should\" section must contain specific, actionable guidance\n   144- **Why**: The next iteration has zero memory. If you don't write it down, it's lost.\n   145\n   146### Check existing code for patterns\n   147- **When**: Unsure how to implement something\n   148- **Rule**: Look at `pathways_app/pathways_app.py`, `analysis/pathway_analyzer.py`, `cli/refresh_pathways.py`\n   149- **Why**: The existing codebase has solved many quirks already\n   150\n   151<!--\n   152ADD NEW GUARDRAILS BELOW as failures are observed during the loop.\n   153\n   154Format:\n   155### [Short descriptive name]\n   156- **When**: What situation triggers this guardrail?\n   157- **Rule**: What must you do (or not do)?\n   158- **Why**: What failure prompted adding this guardrail?\n   159-->\n   160\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"223ba63a-c935-45d5-9a45-75474de07dfb","tool_use_result":{"type":"text","file":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\guardrails.md","content":"# Guardrails\n\nKnown failure patterns. Read EVERY iteration. Follow ALL of these rules.\nIf you discover a new failure pattern during your work, add it to this file.\n\n---\n\n## Drug-Indication Matching Guardrails\n\n### Match drugs to indications, not just patients to indications\n- **When**: Building the indication mapping for pathway charts\n- **Rule**: Each drug must be validated against BOTH the patient's GP diagnoses AND the drug-to-indication mapping from DimSearchTerm.csv. A patient being diagnosed with rheumatoid arthritis does NOT mean all their drugs are for rheumatoid arthritis.\n- **Why**: The previous approach assigned ONE indication per patient (most recent GP dx), ignoring which drugs actually treat which conditions. This produced misleading pathways.\n\n### Use DimSearchTerm.csv for drug-to-Search_Term mapping\n- **When**: Determining which Search_Term a drug belongs to\n- **Rule**: Load `data/DimSearchTerm.csv`. The `CleanedDrugName` column has pipe-separated drug name fragments. Match HCD drug names against these fragments using substring matching (case-insensitive).\n- **Why**: This CSV is the authoritative mapping of which drugs are used for which clinical indications.\n\n### Use substring matching for drug fragments\n- **When**: Matching HCD drug names against DimSearchTerm CleanedDrugName fragments\n- **Rule**: Check if any fragment from DimSearchTerm is a SUBSTRING of the HCD drug name (case-insensitive). E.g., \"PEGYLATED\" should match \"PEGYLATED LIPOSOMAL DOXORUBICIN\".\n- **Why**: DimSearchTerm contains both full drug names (ADALIMUMAB) and partial fragments (PEGYLATED, INHALED). Exact match would miss the partial ones.\n\n### Modified UPID uses pipe delimiter\n- **When**: Creating indication-aware UPIDs\n- **Rule**: Format is `{original_UPID}|{search_term}`. Use pipe `|` as delimiter. Do NOT use ` - ` (hyphen with spaces) as that's used for pathway hierarchy levels in the `ids` column.\n- **Why**: The `ids` column uses \" - \" to separate hierarchy levels (e.g., \"N&WICS - NNUH - rheumatoid arthritis - ADALIMUMAB\"). Using the same delimiter in UPIDs would break hierarchy parsing.\n\n### Return ALL GP matches per patient, not just most recent\n- **When**: Querying Snowflake for patient GP diagnoses\n- **Rule**: Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`. Return ALL matching Search_Terms per patient with `GROUP BY + COUNT(*)` for code_frequency.\n- **Why**: A patient may have GP diagnoses for both rheumatoid arthritis AND asthma. We need ALL matches to cross-reference with their drugs.\n\n### Restrict GP code lookup to HCD data window\n- **When**: Building the WHERE clause for the GP record query\n- **Rule**: Add `AND pc.\"EventDateTime\" >= :earliest_hcd_date` where `earliest_hcd_date` is `MIN(Intervention Date)` from the HCD DataFrame. Pass this as a parameter to `get_patient_indication_groups()`.\n- **Why**: Old GP codes from years before treatment started add noise. A diagnosis coded 10 years ago may no longer be relevant. Restricting to the HCD window ensures code_frequency reflects recent clinical activity for the conditions being actively treated.\n\n### Tiebreaker: highest GP code frequency when a drug matches multiple indications\n- **When**: A single drug maps to multiple Search_Terms AND the patient has GP dx for multiple\n- **Rule**: Use `code_frequency` (COUNT of matching SNOMED codes per Search_Term per patient) from the GP query. The Search_Term with the most matching codes in the patient's GP record wins. If tied, use alphabetical Search_Term for determinism.\n- **Why**: E.g., ADALIMUMAB is listed under rheumatoid arthritis, crohn's disease, psoriatic arthritis, etc. A patient with 47 RA codes and 2 crohn's codes is almost certainly on ADALIMUMAB for RA. Frequency of GP coding is a much stronger signal of clinical intent than recency  a recent one-off asthma check doesn't mean ADALIMUMAB is for asthma.\n\n### Same patient, different indications = separate modified UPIDs\n- **When**: A patient's drugs map to different Search_Terms\n- **Rule**: Create separate modified UPIDs for each indication. E.g., `RMV12345|rheumatoid arthritis` and `RMV12345|asthma`. These are treated as separate \"patients\" by the pathway analyzer.\n- **Why**: This is the core design  drugs for different indications should create separate treatment pathways, even for the same physical patient.\n\n### Fallback to directory for unmatched drugs\n- **When**: A drug doesn't match any Search_Term OR the patient has no GP dx for any of the drug's Search_Terms\n- **Rule**: Use fallback format: `{UPID}|{Directory} (no GP dx)`. The indication_df maps this to `\"{Directory} (no GP dx)\"`.\n- **Why**: Maintains consistent behavior with the previous approach for patients/drugs without GP diagnosis matches.\n\n### Don't modify directory chart processing\n- **When**: Making changes to the indication matching logic\n- **Rule**: Only modify the indication chart path (`elif current_chart_type == \"indication\":`). Directory charts use unmodified UPIDs and directory-based grouping.\n- **Why**: Directory charts work correctly and should not be affected by indication matching changes.\n\n---\n\n## Snowflake Query Guardrails\n\n### Use PseudoNHSNoLinked for GP record matching\n- **When**: Querying GP records (PrimaryCareClinicalCoding) for patient diagnoses\n- **Rule**: Use `PseudoNHSNoLinked` column from HCD data, NOT `PersonKey` (LocalPatientID)\n- **Why**: PersonKey is provider-specific local ID. Only PseudoNHSNoLinked matches PatientPseudonym in GP records.\n\n### Embed cluster query as CTE in Snowflake\n- **When**: Looking up patient indications during data refresh\n- **Rule**: Use the `CLUSTER_MAPPING_SQL` content as a WITH clause in the patient lookup query\n- **Why**: This ensures we always use the complete cluster mapping and don't need local storage\n\n### Quote mixed-case column aliases in Snowflake SQL\n- **When**: Writing SELECT queries that return results to Python code\n- **Rule**: Use `AS \"ColumnName\"` (quoted) for any column alias you'll access by name in Python\n- **Why**: Snowflake uppercases unquoted identifiers. `SELECT foo AS Search_Term` returns `SEARCH_TERM`, so `row.get('Search_Term')` returns None. Fix: `SELECT foo AS \"Search_Term\"`\n\n### Build indication_df from all unique UPIDs, not PseudoNHSNoLinked\n- **When**: Creating the indication mapping DataFrame for pathway processing\n- **Rule**: Use `df.drop_duplicates(subset=['UPID'])` not `drop_duplicates(subset=['PseudoNHSNoLinked'])`\n- **Why**: A patient visiting multiple providers has multiple UPIDs. Using unique PseudoNHSNoLinked only maps one UPID per patient, leaving others as NaN.\n\n---\n\n## Data Processing Guardrails\n\n### Copy DataFrames in functions that modify columns\n- **When**: Writing functions like `prepare_data()` that modify DataFrame columns\n- **Rule**: Always `df = df.copy()` at the start of any function that modifies column values on the input DataFrame\n- **Why**: `prepare_data()` mapped Provider Code  Name in-place. When called multiple times on the same DataFrame, only the first call worked. The fix: `df.copy()` prevents destructive mutation.\n\n### Include chart_type in UNIQUE constraints for pathway_nodes\n- **When**: Creating or modifying the pathway_nodes table schema\n- **Rule**: The UNIQUE constraint MUST include `chart_type`: `UNIQUE(date_filter_id, chart_type, ids)`\n- **Why**: Without `chart_type`, `INSERT OR REPLACE` silently overwrites directory chart nodes when indication chart nodes are inserted.\n\n### Handle NaN in Directory when building fallback labels\n- **When**: Creating fallback indication labels for patients without GP diagnosis match\n- **Rule**: Check `pd.notna(directory)` before concatenating to string. Use `\"UNKNOWN (no GP dx)\"` for NaN cases.\n- **Why**: NaN handling prevents TypeError and ensures meaningful fallback labels.\n\n### Use parameterized queries for SQLite\n- **When**: Building WHERE clauses with user-selected filters\n- **Rule**: Use `?` placeholders and pass params tuple  never string interpolation\n- **Why**: Prevents SQL injection and handles special characters in drug/directory names\n\n### Use existing pathway_analyzer functions\n- **When**: Processing pathway data for the icicle chart\n- **Rule**: Reuse functions from `analysis/pathway_analyzer.py`  don't reinvent\n- **Why**: The existing code handles edge cases (empty groups, statistics calculation, color mapping)\n\n---\n\n## Reflex Guardrails\n\n### Use .to() methods for Var operations in rx.foreach\n- **When**: Working with items inside `rx.foreach` render functions\n- **Rule**: Use `item.to(int)` for numeric comparisons, `item.to_string()` for text operations\n- **Why**: Items from rx.foreach are Var objects, not plain Python values.\n\n### Use rx.cond for conditional rendering, not Python if\n- **When**: Conditionally showing/hiding components or changing styles based on state\n- **Rule**: Use `rx.cond(condition, true_component, false_component)`  not Python `if`\n- **Why**: Python `if` evaluates at definition time; `rx.cond` evaluates reactively at render time\n\n---\n\n## Process Guardrails\n\n### One task per iteration\n- **When**: Temptation to do additional tasks after completing the current one\n- **Rule**: Complete ONE task, validate it, commit it, update progress, then stop\n- **Why**: Multiple tasks increase error risk and make failures harder to diagnose\n\n### Never mark complete without validation\n- **When**: Task feels \"done\" but hasn't been tested\n- **Rule**: All validation tiers must pass before marking `[x]`\n- **Why**: \"Feels done\" is not \"is done\"\n\n### Write explicit handoff notes\n- **When**: Every iteration, before stopping\n- **Rule**: The \"Next iteration should\" section must contain specific, actionable guidance\n- **Why**: The next iteration has zero memory. If you don't write it down, it's lost.\n\n### Check existing code for patterns\n- **When**: Unsure how to implement something\n- **Rule**: Look at `pathways_app/pathways_app.py`, `analysis/pathway_analyzer.py`, `cli/refresh_pathways.py`\n- **Why**: The existing codebase has solved many quirks already\n\n<!--\nADD NEW GUARDRAILS BELOW as failures are observed during the loop.\n\nFormat:\n### [Short descriptive name]\n- **When**: What situation triggers this guardrail?\n- **Rule**: What must you do (or not do)?\n- **Why**: What failure prompted adding this guardrail?\n-->\n","numLines":160,"startLine":1,"totalLines":160}}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_017fn8KZ5s3cf5FG7P2vCaV8","type":"message","role":"assistant","content":[{"type":"text","text":"Reading progress.txt, IMPLEMENTATION_PLAN.md, and guardrails.md. The iteration log in progress.txt is empty ÔÇö this is the **first iteration**. All tasks in IMPLEMENTATION_PLAN.md are `[ ]` (not started).\n\nLet me check recent commits and read the key source files I'll be modifying."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8926,"cache_read_input_tokens":69693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8926},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"bb3fd7a3-e9f2-4cc5-87bc-b686667ebb8d"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_017fn8KZ5s3cf5FG7P2vCaV8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M1dohqj5mtBidiAzPMTdms","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && git log --oneline -5","description":"Check recent git commits"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8926,"cache_read_input_tokens":69693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8926},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"89d0273e-d467-4e69-b93d-d717a18f2f82"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_017fn8KZ5s3cf5FG7P2vCaV8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013gSjU7MeMnCV9sYiUzUdur","name":"Read","input":{"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8926,"cache_read_input_tokens":69693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8926},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"d256c030-4c2a-4550-882b-1acc1314c45e"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_017fn8KZ5s3cf5FG7P2vCaV8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017M3RrBqP3sNRb87Zq5SxJe","name":"Read","input":{"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data\\DimSearchTerm.csv"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":8926,"cache_read_input_tokens":69693,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":8926},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"041f3b0c-e1a1-4776-99a7-d0a34c85d983"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01M1dohqj5mtBidiAzPMTdms","type":"tool_result","content":"1c4d2c0 docs: mark project complete - all tasks done, viewport testing blocked by env (Iteration 9)\nfed9094 docs: update CLAUDE.md with indication chart architecture and CLI docs (Task 5.2)\n4884e0a fix: recreate pathway_nodes with correct UNIQUE constraint and validate end-to-end (Task 5.1)\n6331d44 fix: prevent DataFrame mutation in prepare_data() causing indication charts to fail\n6f88a59 feat: add chart type toggle for Directory/Indication views (Task 4.1, 4.2, 4.3)","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"93417201-6e24-461b-9746-82013c3bd536","tool_use_result":{"stdout":"1c4d2c0 docs: mark project complete - all tasks done, viewport testing blocked by env (Iteration 9)\nfed9094 docs: update CLAUDE.md with indication chart architecture and CLI docs (Task 5.2)\n4884e0a fix: recreate pathway_nodes with correct UNIQUE constraint and validate end-to-end (Task 5.1)\n6331d44 fix: prevent DataFrame mutation in prepare_data() causing indication charts to fail\n6f88a59 feat: add chart type toggle for Directory/Indication views (Task 4.1, 4.2, 4.3)\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017M3RrBqP3sNRb87Zq5SxJe","type":"tool_result","content":"     1Search_Term,CleanedDrugName,PrimaryDirectorate\n     2acute coronary syndrome,ABCIXIMAB|CLOPIDOGREL|PRASUGREL|RIVAROXABAN|TICAGRELOR,CARDIOLOGY\n     3acute lymphoblastic leukaemia,BLINATUMOMAB|DASATINIB|INOTUZUMAB|PEGASPARGASE|PONATINIB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\n     4acute myeloid leukaemia,AZACITIDINE|DECITABINE|GEMTUZUMAB|GILTERITINIB|GLASDEGIB|LIPOSOMAL|MIDOSTAURIN|ORAL|VENETOCLAX,CLINICAL HAEMATOLOGY\n     5acute promyelocytic leukaemia,ARSENIC|GEMTUZUMAB,CLINICAL HAEMATOLOGY\n     6allergic asthma,OMALIZUMAB,THORACIC MEDICINE\n     7allergic rhinitis,SQ,ENT\n     8alzheimer's disease,DONEPEZIL,NEUROLOGY\n     9amyloidosis,VUTRISIRAN,CLINICAL HAEMATOLOGY\n    10anaemia,ERYTHROPOIESIS-STIMULATING|ERYTHROPOIETIN,CLINICAL HAEMATOLOGY\n    11anaplastic large cell lymphoma,BRENTUXIMAB,CLINICAL HAEMATOLOGY\n    12ankylosing spondylitis,ADALIMUMAB|GOLIMUMAB|SECUKINUMAB|UPADACITINIB,RHEUMATOLOGY\n    13apixaban,ANDEXANET,CARDIOLOGY\n    14aplastic anaemia,ELTROMBOPAG,CLINICAL HAEMATOLOGY\n    15arthritis,ETANERCEPT,RHEUMATOLOGY\n    16asthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\n    17atopic dermatitis,ABROCITINIB|ALCLOMETASONE|BARICITINIB|CRISABOROLE|DUPILUMAB|PIMECROLIMUS,DERMATOLOGY\n    18atrial fibrillation,APIXABAN|DABIGATRAN|DRONEDARONE|EDOXABAN|RIVAROXABAN|VERNAKALANT,CARDIOLOGY\n    19attention deficit hyperactivity disorder,ATOMOXETINE,MENTAL HEALTH\n    20attention-deficit hyperactivity disorder,METHYLPHENIDATE,MENTAL HEALTH\n    21axial spondyloarthritis,ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB|SECUKINUMAB|UPADACITINIB,RHEUMATOLOGY\n    22basal cell carcinoma,VISMODEGIB,DERMATOLOGY\n    23bipolar disorder,LOXAPINE|OLANZAPINE,MENTAL HEALTH\n    24bladder,MIRABEGRON,MEDICAL ONCOLOGY\n    25brca,OLAPARIB,MEDICAL ONCOLOGY\n    26breast cancer,ABEMACICLIB|ALPELISIB|ANASTROZOLE|ATEZOLIZUMAB|BEVACIZUMAB|CAPECITABINE|DENOSUMAB|DOCETAXEL|ERIBULIN|EVEROLIMUS|FULVESTRANT|GEMCITABINE|INTRABEAM|LAPATINIB|NERATINIB|OLAPARIB|PACLITAXEL|PALBOCICLI|PALBOCICLIB|PEMBROLIZUMAB|PERTUZUMAB|RIBOCICLIB|SACITUZUMAB|TRASTUZUMAB|TUCATINIB|VINORELBINE,MEDICAL ONCOLOGY\n    27cardiomyopathy,TAFAMIDIS,CARDIOLOGY\n    28cardiovascular disease,ATORVASTATIN,CARDIOLOGY\n    29cervical cancer,TOPOTECAN,MEDICAL ONCOLOGY\n    30cholangiocarcinoma,PEMIGATINIB,MEDICAL ONCOLOGY\n    31choroidal neovascularisation,AFLIBERCEPT|RANIBIZUMAB,OPHTHALMOLOGY\n    32chronic kidney disease,DAPAGLIFLOZIN|IMLIFIDASE|ROXADUSTAT,NEPHROLOGY\n    33chronic liver disease,AVATROMBOPAG|LUSUTROMBOPAG,GASTROENTEROLOGY\n    34chronic lymphocytic leukaemia,ACALABRUTINIB|BENDAMUSTINE|DUVELISIB|IBRUTINIB|IDELALISIB|OBINUTUZUMAB|OFATUMUMAB|RITUXIMAB|VENETOCLAX,CLINICAL HAEMATOLOGY\n    35chronic myeloid leukaemia,ASCIMINIB|BOSUTINIB|STANDARD-DOSE|DASATINIB|DASITINIB|NILOTINIB|PONATINIB,CLINICAL HAEMATOLOGY\n    36chronic obstructive pulmonary disease,ROFLUMILAST,THORACIC MEDICINE\n    37colon cancer,CAPECITABINE,MEDICAL ONCOLOGY\n    38colorectal cancer,BEVACIZUMAB|CAPECITABINE|IRINOTECAN,MEDICAL ONCOLOGY\n    39constipation,LUBIPROSTONE|METHYLNALTREXONE|NALDEMEDINE|NALOXEGOL|PRUCALOPRIDE,GASTROENTEROLOGY\n    40covid-19,NIRMATRELVIR,INFECTIOUS DISEASES\n    41crohn's disease,INFLIXIMAB|VEDOLIZUMAB,GASTROENTEROLOGY\n    42cutaneous t-cell lymphoma,BRENTUXIMAB|CHLORMETHINE,CLINICAL HAEMATOLOGY\n    43cystic fibrosis,COLISTIMETHATE|LUMACAFTOR|MANNITOL,THORACIC MEDICINE\n    44cytomegalovirus,LETERMOVIR|MARIBAVIR,INFECTIOUS DISEASES\n    45deep vein thrombosis,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,VASCULAR SURGERY\n    46depression,ESKETAMINE,MENTAL HEALTH\n    47diabetes,ERTUGLIFLOZIN|INHALED|AFLIBERCEPT|BROLUCIZUMAB|DEXAMETHASONE|FARICIMAB|FLUOCINOLONE|RANIBIZUMAB,DIABETIC MEDICINE\n    48diabetes,ERTUGLIFLOZIN|INHALED|AFLIBERCEPT|BROLUCIZUMAB|DEXAMETHASONE|FARICIMAB|FLUOCINOLONE|RANIBIZUMAB,OPHTHALMOLOGY\n    49diabetic retinopathy,RANIBUZIMAB,OPHTHALMOLOGY\n    50diffuse large b-cell lymphoma,AXICABTAGENE|POLATUZUMAB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\n    51dravet syndrome,CANNABIDIOL|FENFLURAMINE,NEUROLOGY\n    52drug misuse,BUPRENORPHINE|NALTREXONE,MENTAL HEALTH\n    53dry eye,CICLOSPORIN,OPHTHALMOLOGY\n    54dyspepsia,LANSOPRAZOLE,GASTROENTEROLOGY\n    55endometrial cancer,DOSTARLIMAB,MEDICAL ONCOLOGY\n    56epilepsy,CENOBAMATE|GABAPENTIN|RETIGABINE,NEUROLOGY\n    57fallopian tube,BEVACIZUMAB|NIRAPARIB|OLAPARIB|RUCAPARIB,MEDICAL ONCOLOGY\n    58follicular lymphoma,DUVELISIB|IDELALISIB|LENALIDOMIDE|OBINUTUZUMAB|RITUXIMAB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\n    59gastric cancer,CAPECITABINE|RAMUCIRUMAB|TRASTUZUMAB|TRIFLURIDINE,MEDICAL ONCOLOGY\n    60gastro-oesophageal junction,NIVOLUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\n    61giant cell arteritis,TOCILIZUMAB,RHEUMATOLOGY\n    62glioma,CARMUSTINE,MEDICAL ONCOLOGY\n    63gout,CANAKINUMAB|FEBUXOSTAT|LESINURAD,RHEUMATOLOGY\n    64graft versus host disease,RUXOLITINIB,CLINICAL HAEMATOLOGY\n    65granulomatosis with polyangiitis,AVACOPAN|MEPOLIZUMAB,RHEUMATOLOGY\n    66growth hormone deficiency,SOMATROPIN,ENDOCRINOLOGY\n    67hand eczema,ALITRETINOIN,DERMATOLOGY\n    68heart failure,DAPAGLIFLOZIN|EMPAGLIFLOZIN|IVABRADINE|SACUBITRIL|VERICIGUAT,CARDIOLOGY\n    69hepatitis b,ADEFOVIR,GASTROENTEROLOGY\n    70hepatitis c,BOCEPREVIR|DACLATASVIR|ELBASVIR|GLECAPREVIR|INTERFERON|LEDIPASVIR|OMBITASVIR|PEGINTERFERON|PEGYLATED|SIMEPREVIR|SOFOSBUVIR|TELAPREVIR,GASTROENTEROLOGY\n    71hepatocellular carcinoma,ATEZOLIZUMAB|CABOZANTINIB|LENVATINIB|RAMUCIRUMAB|REGORAFENIB|SELECTIVE|SORAFENIB,MEDICAL ONCOLOGY\n    72hiv,CABOTEGRAVIR,INFECTIOUS DISEASES\n    73hodgkin lymphoma,BRENTUXIMAB|NIVOLUMAB|PEMBROLIZUMAB,CLINICAL HAEMATOLOGY\n    74hormone receptor,ABEMACICLIB,MEDICAL ONCOLOGY\n    75hypercholesterolaemia,EZETIMIBE,CARDIOLOGY\n    76hyperparathyroidism,CINACALCET|ETELCALCETIDE,ENDOCRINOLOGY\n    77immune thrombocytopenia,AVATROMBOPAG|FOSTAMATINIB,CLINICAL HAEMATOLOGY\n    78influenza,AMANTADINE|ZANAMIVIR|BALOXAVIR,INFECTIOUS DISEASES\n    79insomnia,ZALEPLON,NEUROLOGY\n    80irritable bowel syndrome,ELUXADOLINE,GASTROENTEROLOGY\n    81ischaemic stroke,ALTEPLASE,STROKE MEDICINE\n    82juvenile idiopathic arthritis,ABATECEPT|CANAKINUMAB|TOCILIZUMAB|TOFACITINIB,RHEUMATOLOGY\n    83kidney transplant,BASILIXIMAB,NEPHROLOGY\n    84leukaemia,FLUDARABINE|IMATINIB,CLINICAL HAEMATOLOGY\n    85lung cancer,ATEZOLIZUMAB|DURVALUMAB|GEFITINIB|ORAL|NINTEDANIB,MEDICAL ONCOLOGY\n    86lymphoma,BENDAMUSTINE|CRIZOTINIB|PIXANTRONE|RITUXIMAB,CLINICAL HAEMATOLOGY\n    87macular degeneration,AFLIBERCEPT|BROLUCIZUMAB|FARICIMAB|RANIBIZUMAB,OPHTHALMOLOGY\n    88macular oedema,AFLIBERCEPT|RANIBIZUMAB,OPHTHALMOLOGY\n    89major depressive episodes,AGOMELATINE|VORTIOXETINE,MENTAL HEALTH\n    90malignant melanoma,VEMURAFENIB,MEDICAL ONCOLOGY\n    91malignant pleural mesothelioma,NIVOLUMAB|PEMETREXED,MEDICAL ONCOLOGY\n    92manic episode,ARIPIPRAZOLE,MENTAL HEALTH\n    93mantle cell lymphoma,AUTOLOGOUS|BORTEZOMIB|IBRUTINIB|LENALIDOMIDE|TEMSIROLIMUS,CLINICAL HAEMATOLOGY\n    94melanoma,COBIMETINIB|DABRAFENIB|ENCORAFENIB|IPILIMUMAB|NIVOLUMAB|PEMBROLIZUMAB|TALIMOGENE|TRAMETINIB,MEDICAL ONCOLOGY\n    95merkel cell carcinoma,AVELUMAB,MEDICAL ONCOLOGY\n    96migraine,BOTULINUM|EPTINEZUMAB|ERENUMAB|FREMANEZUMAB|GALCANEZUMAB,NEUROLOGY\n    97motor neurone disease,RILUZOLE,NEUROLOGY\n    98multiple myeloma,BORTEZOMIB|THALIDOMIDE|CARFILZOMIB|DARATUMUMAB|DENOSUMAB|ELOTUZUMAB|ISATUXIMAB|IXAZOMIB|LENALIDOMIDE|PANOBINOSTAT|POMALIDOMIDE|SELINEXOR|TECLISTAMAB,CLINICAL HAEMATOLOGY\n    99multiple sclerosis,ALEMTUZUMAB|BETA|CLADRIBINE|DACLIZUMAB|DIMETHYL|DIROXIMEL|FINGOLIMOD|INTERFERON|NATALIZUMAB|OCRELIZUMAB|OZANIMOD|PEGINTERFERON|PONESIMOD|SIPONIMOD|TERIFLUNOMIDE,NEUROLOGY\n   100myelodysplastic,LENALIDOMIDE|LUSPATERCEPT,CLINICAL HAEMATOLOGY\n   101myelofibrosis,FEDRATINIB|RUXOLITINIB,CLINICAL HAEMATOLOGY\n   102myocardial infarction,ALTEPLASE|BIVALIRUDIN|TICAGRELOR,CARDIOLOGY\n   103myotonia,MEXILETINE,NEUROLOGY\n   104narcolepsy,SOLRIAMFETOL,NEUROLOGY\n   105neuroendocrine tumour,EVEROLIMUS|LUTETIUM,MEDICAL ONCOLOGY\n   106non-small cell lung cancer,ATEZOLIZMAB|DOCETAXEL|ERLOTINIB|PEMETREXED,MEDICAL ONCOLOGY\n   107non-small-cell lung cancer,AFATINIB|ALECTINIB|AMIVANTAMAB|ATEZOLIZUMAB|BEVACIZUMAB|BRIGATINIB|CEMIPLIMAB|CERITINIB|CRIZOTINIB|DABRAFENIB|DACOMITINIB|DURVALUMAB|ENTRECTINIB|ERLOTINIB|GEFITINIB|LORLATINIB|MOBOCERTINIB|NECITUMUMAB|NIVOLUMAB|OSIMERTINIB|PACLITAXEL|PEMBROLIZUMAB|PEMETREXED|PRALSETINIB|RAMUCIRUMAB|SELPERCATINIB|SOTORASIB|TEPOTINIB,MEDICAL ONCOLOGY\n   108obesity,LIRAGLUTIDE|NALTREXONE|ORLISTAT|SEMAGLUTIDE|SIBUTRAMINE,ENDOCRINOLOGY\n   109oesophageal cancer,NIVOLUMAB,MEDICAL ONCOLOGY\n   110osteoarthritis,CELECOXIB,RHEUMATOLOGY\n   111osteoporosis,ALENDRONATE|DENOSUMAB|ORAL|ROMOSOZUMAB,RHEUMATOLOGY\n   112osteosarcoma,MIFAMURTIDE,MEDICAL ONCOLOGY\n   113ovarian cancer,BEVACIZUMAB|PACLITAXEL|PEGYLATED|TOPOTECAN|TRABECTEDIN,MEDICAL ONCOLOGY\n   114overweight,RIMONABANT,ENDOCRINOLOGY\n   115pancreatic cancer,GEMCITABINE|OLAPARIB|PACLITAXEL|PEGYLATED,MEDICAL ONCOLOGY\n   116paroxysmal nocturnal haemoglobinuria,PEGCETACOPLAN|RAVULIZUMAB,CLINICAL HAEMATOLOGY\n   117peripheral arterial disease,NAFTIDROFYRYL,VASCULAR SURGERY\n   118plaque psoriasis,ADALIMUMAB|APREMILAST|BIMEKIZUMAB|BRODALUMAB|CERTOLIZUMAB|GUSELKUMAB|INFLIXIMAB|IXEKIZUMAB|RISANKIZUMAB|SECUKINUMAB|TILDRAKIZUAMB|USTEKINUMAB,DERMATOLOGY\n   119polycystic kidney disease,TOLVAPTAN,NEPHROLOGY\n   120polycythaemia vera,RUXOLITINIB,CLINICAL HAEMATOLOGY\n   121pregnancy,ROUTINE,OBSTETRICS\n   122primary biliary cholangitis,OBETICHOLIC,GASTROENTEROLOGY\n   123primary hypercholesterolaemia,ALIROCUMAB|EVOLOCUMAB,CARDIOLOGY\n   124prostate cancer,ABIRATERONE|APALUTAMIDE|CABAZITAXEL|DAROLUTAMIDE|DEGARELIX|DENOSUMAB|DOCETAXEL|ENZALUTAMIDE|OLAPARIB|PADELIPORFIN|RADIUM-|RADIUM|SIPULEUCEL-T,MEDICAL ONCOLOGY\n   125psoriasis,EFALUZIMAB,DERMATOLOGY\n   126psoriatic arthritis,ABATACEPT|ADALIMUMAB|APREMILAST|CERTOLIZUMAB|ETANERCEPT|GOLIMUMAB|GUSELKUMAB|IXEKIZUMAB|RISANKIZUMAB|TOFACITINIB|UPADACITINIB|USTEKINUMAB,RHEUMATOLOGY\n   127pulmonary embolism,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,THORACIC MEDICINE\n   128pulmonary fibrosis,NINTEDANIB|PIRFENIDONE,THORACIC MEDICINE\n   129relapsing multiple sclerosis,OFATUMUMAB,NEUROLOGY\n   130renal cell carcinoma,AVELUMAB|AXITINIB|BEVACIZUMAB|CABOZANTINIB|EVEROLIMUS|LENVATINIB|NIVOLUMAB|PAZOPANIB|PEMBROLIZUMAB|SUNITINIB|TIVOZANIB,MEDICAL ONCOLOGY\n   131renal transplantation,BASILIXIMAB|INDUCTION,NEPHROLOGY\n   132retinal vein occlusion,AFLIBERCEPT|DEXAMETHASONE|RANIBIZUMAB,OPHTHALMOLOGY\n   133rheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|CELECOXIB|CERTOLIZUMAB|ETANERCEPT|FILGOTINIB|GOLIMUMAB|RITUXIMAB|SARILUMAB|TOCILIZUMAB|TOFACITINIB|UPADACITINIB,RHEUMATOLOGY\n   134rivaroxaban,ANDEXANET,CARDIOLOGY\n   135schizophrenia,AMISULPRIDE|ARIPIPRAZOLE|LOXAPINE,MENTAL HEALTH\n   136seizures,CANNABIDIOL,NEUROLOGY\n   137sepsis,DROTRECOGIN,INFECTIOUS DISEASES\n   138severe persistent allergic asthma,OMALIZUMAB,THORACIC MEDICINE\n   139short bowel syndrome,TEDUGLUTIDE,GASTROENTEROLOGY\n   140sickle cell disease,CRIZANLIZUMAB,CLINICAL HAEMATOLOGY\n   141sleep apnoea,PITOLISANT|SOLRIAMFETOL,THORACIC MEDICINE\n   142smoking cessation,NICOTINE|VARENICLINE,THORACIC MEDICINE\n   143soft tissue sarcoma,INTRAVENOUS|NBTXR-|OLARATUMAB,MEDICAL ONCOLOGY\n   144spinal muscular atrophy,NUSINERSEN|RISDIPLAM,NEUROLOGY\n   145squamous cell,CETUXIMAB,MEDICAL ONCOLOGY\n   146squamous cell carcinoma,CEMIPLIMAB|NIVOLUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\n   147stem cell transplant,MELPHALAN|TREOSULFAN,CLINICAL HAEMATOLOGY\n   148stroke,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,STROKE MEDICINE\n   149systemic lupus erythematosus,ANIFROLUMAB|ETANERCEPT,RHEUMATOLOGY\n   150systemic mastocytosis,MIDOSTAURIN,CLINICAL HAEMATOLOGY\n   151thrombocytopenic purpura,ELTROMBOPAG|ROMIPLOSTIM,CLINICAL HAEMATOLOGY\n   152thrombotic thrombocytopenic purpura,CAPLACIZUMAB,CLINICAL HAEMATOLOGY\n   153thyroid cancer,CABOZANTINIB|LENVATINIB|SELPERCATINIB|VANDETANIB,MEDICAL ONCOLOGY\n   154tophaceous gout,PEGLOTICASE,RHEUMATOLOGY\n   155transitional cell carcinoma,VINFLUNINE,MEDICAL ONCOLOGY\n   156tuberous sclerosis,CANNABIDIOL,NEUROLOGY\n   157type 1 diabetes,CONTINUOUS|DAPAGLIFLOZIN|INSULIN|SOTAGLIFLOZIN,DIABETIC MEDICINE\n   158type 2 diabetes,CANAGLIFLOZIN|CONTINUOUS|DAPAGLIFLOZIN|EMPAGLIFLOZIN|ERTUGLIFLOZIN|EXENATIDE|FINERENONE|INSULIN|LIRAGLUTIDE|PIOGLITAZONE|ROSIGLITAZONE,DIABETIC MEDICINE\n   159ulcerative colitis,ADALIMUMAB|INFLIXIMAB|FILGOTINIB|OZANIMOD|TOFACITINIB|UPADACITINIB|USTEKINUMAB|VEDOLIZUMAB,GASTROENTEROLOGY\n   160urothelial carcinoma,ATEZOLIZUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\n   161urticaria,OMALIZUMAB,DERMATOLOGY\n   162uterine fibroids,RELUGOLIX,GYNAECOLOGY\n   163uveitis,ADALIMUMAB|FLUOCINOLONE,OPHTHALMOLOGY\n   164vascular disease,MODIFIED-RELEASE|CLOPIDOGREL,VASCULAR SURGERY\n   165vasculitis,RITUXIMAB,RHEUMATOLOGY\n   166venous thromboembolism,APIXABAN|DABIGATRAN|RIVAROXABAN,CLINICAL HAEMATOLOGY\n   167\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"d38111b3-a022-4fc2-8770-22b3382cd845","tool_use_result":{"type":"text","file":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data\\DimSearchTerm.csv","content":"Search_Term,CleanedDrugName,PrimaryDirectorate\nacute coronary syndrome,ABCIXIMAB|CLOPIDOGREL|PRASUGREL|RIVAROXABAN|TICAGRELOR,CARDIOLOGY\nacute lymphoblastic leukaemia,BLINATUMOMAB|DASATINIB|INOTUZUMAB|PEGASPARGASE|PONATINIB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\nacute myeloid leukaemia,AZACITIDINE|DECITABINE|GEMTUZUMAB|GILTERITINIB|GLASDEGIB|LIPOSOMAL|MIDOSTAURIN|ORAL|VENETOCLAX,CLINICAL HAEMATOLOGY\nacute promyelocytic leukaemia,ARSENIC|GEMTUZUMAB,CLINICAL HAEMATOLOGY\nallergic asthma,OMALIZUMAB,THORACIC MEDICINE\nallergic rhinitis,SQ,ENT\nalzheimer's disease,DONEPEZIL,NEUROLOGY\namyloidosis,VUTRISIRAN,CLINICAL HAEMATOLOGY\nanaemia,ERYTHROPOIESIS-STIMULATING|ERYTHROPOIETIN,CLINICAL HAEMATOLOGY\nanaplastic large cell lymphoma,BRENTUXIMAB,CLINICAL HAEMATOLOGY\nankylosing spondylitis,ADALIMUMAB|GOLIMUMAB|SECUKINUMAB|UPADACITINIB,RHEUMATOLOGY\napixaban,ANDEXANET,CARDIOLOGY\naplastic anaemia,ELTROMBOPAG,CLINICAL HAEMATOLOGY\narthritis,ETANERCEPT,RHEUMATOLOGY\nasthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\natopic dermatitis,ABROCITINIB|ALCLOMETASONE|BARICITINIB|CRISABOROLE|DUPILUMAB|PIMECROLIMUS,DERMATOLOGY\natrial fibrillation,APIXABAN|DABIGATRAN|DRONEDARONE|EDOXABAN|RIVAROXABAN|VERNAKALANT,CARDIOLOGY\nattention deficit hyperactivity disorder,ATOMOXETINE,MENTAL HEALTH\nattention-deficit hyperactivity disorder,METHYLPHENIDATE,MENTAL HEALTH\naxial spondyloarthritis,ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB|SECUKINUMAB|UPADACITINIB,RHEUMATOLOGY\nbasal cell carcinoma,VISMODEGIB,DERMATOLOGY\nbipolar disorder,LOXAPINE|OLANZAPINE,MENTAL HEALTH\nbladder,MIRABEGRON,MEDICAL ONCOLOGY\nbrca,OLAPARIB,MEDICAL ONCOLOGY\nbreast cancer,ABEMACICLIB|ALPELISIB|ANASTROZOLE|ATEZOLIZUMAB|BEVACIZUMAB|CAPECITABINE|DENOSUMAB|DOCETAXEL|ERIBULIN|EVEROLIMUS|FULVESTRANT|GEMCITABINE|INTRABEAM|LAPATINIB|NERATINIB|OLAPARIB|PACLITAXEL|PALBOCICLI|PALBOCICLIB|PEMBROLIZUMAB|PERTUZUMAB|RIBOCICLIB|SACITUZUMAB|TRASTUZUMAB|TUCATINIB|VINORELBINE,MEDICAL ONCOLOGY\ncardiomyopathy,TAFAMIDIS,CARDIOLOGY\ncardiovascular disease,ATORVASTATIN,CARDIOLOGY\ncervical cancer,TOPOTECAN,MEDICAL ONCOLOGY\ncholangiocarcinoma,PEMIGATINIB,MEDICAL ONCOLOGY\nchoroidal neovascularisation,AFLIBERCEPT|RANIBIZUMAB,OPHTHALMOLOGY\nchronic kidney disease,DAPAGLIFLOZIN|IMLIFIDASE|ROXADUSTAT,NEPHROLOGY\nchronic liver disease,AVATROMBOPAG|LUSUTROMBOPAG,GASTROENTEROLOGY\nchronic lymphocytic leukaemia,ACALABRUTINIB|BENDAMUSTINE|DUVELISIB|IBRUTINIB|IDELALISIB|OBINUTUZUMAB|OFATUMUMAB|RITUXIMAB|VENETOCLAX,CLINICAL HAEMATOLOGY\nchronic myeloid leukaemia,ASCIMINIB|BOSUTINIB|STANDARD-DOSE|DASATINIB|DASITINIB|NILOTINIB|PONATINIB,CLINICAL HAEMATOLOGY\nchronic obstructive pulmonary disease,ROFLUMILAST,THORACIC MEDICINE\ncolon cancer,CAPECITABINE,MEDICAL ONCOLOGY\ncolorectal cancer,BEVACIZUMAB|CAPECITABINE|IRINOTECAN,MEDICAL ONCOLOGY\nconstipation,LUBIPROSTONE|METHYLNALTREXONE|NALDEMEDINE|NALOXEGOL|PRUCALOPRIDE,GASTROENTEROLOGY\ncovid-19,NIRMATRELVIR,INFECTIOUS DISEASES\ncrohn's disease,INFLIXIMAB|VEDOLIZUMAB,GASTROENTEROLOGY\ncutaneous t-cell lymphoma,BRENTUXIMAB|CHLORMETHINE,CLINICAL HAEMATOLOGY\ncystic fibrosis,COLISTIMETHATE|LUMACAFTOR|MANNITOL,THORACIC MEDICINE\ncytomegalovirus,LETERMOVIR|MARIBAVIR,INFECTIOUS DISEASES\ndeep vein thrombosis,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,VASCULAR SURGERY\ndepression,ESKETAMINE,MENTAL HEALTH\ndiabetes,ERTUGLIFLOZIN|INHALED|AFLIBERCEPT|BROLUCIZUMAB|DEXAMETHASONE|FARICIMAB|FLUOCINOLONE|RANIBIZUMAB,DIABETIC MEDICINE\ndiabetes,ERTUGLIFLOZIN|INHALED|AFLIBERCEPT|BROLUCIZUMAB|DEXAMETHASONE|FARICIMAB|FLUOCINOLONE|RANIBIZUMAB,OPHTHALMOLOGY\ndiabetic retinopathy,RANIBUZIMAB,OPHTHALMOLOGY\ndiffuse large b-cell lymphoma,AXICABTAGENE|POLATUZUMAB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\ndravet syndrome,CANNABIDIOL|FENFLURAMINE,NEUROLOGY\ndrug misuse,BUPRENORPHINE|NALTREXONE,MENTAL HEALTH\ndry eye,CICLOSPORIN,OPHTHALMOLOGY\ndyspepsia,LANSOPRAZOLE,GASTROENTEROLOGY\nendometrial cancer,DOSTARLIMAB,MEDICAL ONCOLOGY\nepilepsy,CENOBAMATE|GABAPENTIN|RETIGABINE,NEUROLOGY\nfallopian tube,BEVACIZUMAB|NIRAPARIB|OLAPARIB|RUCAPARIB,MEDICAL ONCOLOGY\nfollicular lymphoma,DUVELISIB|IDELALISIB|LENALIDOMIDE|OBINUTUZUMAB|RITUXIMAB|TISAGENLECLEUCEL,CLINICAL HAEMATOLOGY\ngastric cancer,CAPECITABINE|RAMUCIRUMAB|TRASTUZUMAB|TRIFLURIDINE,MEDICAL ONCOLOGY\ngastro-oesophageal junction,NIVOLUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\ngiant cell arteritis,TOCILIZUMAB,RHEUMATOLOGY\nglioma,CARMUSTINE,MEDICAL ONCOLOGY\ngout,CANAKINUMAB|FEBUXOSTAT|LESINURAD,RHEUMATOLOGY\ngraft versus host disease,RUXOLITINIB,CLINICAL HAEMATOLOGY\ngranulomatosis with polyangiitis,AVACOPAN|MEPOLIZUMAB,RHEUMATOLOGY\ngrowth hormone deficiency,SOMATROPIN,ENDOCRINOLOGY\nhand eczema,ALITRETINOIN,DERMATOLOGY\nheart failure,DAPAGLIFLOZIN|EMPAGLIFLOZIN|IVABRADINE|SACUBITRIL|VERICIGUAT,CARDIOLOGY\nhepatitis b,ADEFOVIR,GASTROENTEROLOGY\nhepatitis c,BOCEPREVIR|DACLATASVIR|ELBASVIR|GLECAPREVIR|INTERFERON|LEDIPASVIR|OMBITASVIR|PEGINTERFERON|PEGYLATED|SIMEPREVIR|SOFOSBUVIR|TELAPREVIR,GASTROENTEROLOGY\nhepatocellular carcinoma,ATEZOLIZUMAB|CABOZANTINIB|LENVATINIB|RAMUCIRUMAB|REGORAFENIB|SELECTIVE|SORAFENIB,MEDICAL ONCOLOGY\nhiv,CABOTEGRAVIR,INFECTIOUS DISEASES\nhodgkin lymphoma,BRENTUXIMAB|NIVOLUMAB|PEMBROLIZUMAB,CLINICAL HAEMATOLOGY\nhormone receptor,ABEMACICLIB,MEDICAL ONCOLOGY\nhypercholesterolaemia,EZETIMIBE,CARDIOLOGY\nhyperparathyroidism,CINACALCET|ETELCALCETIDE,ENDOCRINOLOGY\nimmune thrombocytopenia,AVATROMBOPAG|FOSTAMATINIB,CLINICAL HAEMATOLOGY\ninfluenza,AMANTADINE|ZANAMIVIR|BALOXAVIR,INFECTIOUS DISEASES\ninsomnia,ZALEPLON,NEUROLOGY\nirritable bowel syndrome,ELUXADOLINE,GASTROENTEROLOGY\nischaemic stroke,ALTEPLASE,STROKE MEDICINE\njuvenile idiopathic arthritis,ABATECEPT|CANAKINUMAB|TOCILIZUMAB|TOFACITINIB,RHEUMATOLOGY\nkidney transplant,BASILIXIMAB,NEPHROLOGY\nleukaemia,FLUDARABINE|IMATINIB,CLINICAL HAEMATOLOGY\nlung cancer,ATEZOLIZUMAB|DURVALUMAB|GEFITINIB|ORAL|NINTEDANIB,MEDICAL ONCOLOGY\nlymphoma,BENDAMUSTINE|CRIZOTINIB|PIXANTRONE|RITUXIMAB,CLINICAL HAEMATOLOGY\nmacular degeneration,AFLIBERCEPT|BROLUCIZUMAB|FARICIMAB|RANIBIZUMAB,OPHTHALMOLOGY\nmacular oedema,AFLIBERCEPT|RANIBIZUMAB,OPHTHALMOLOGY\nmajor depressive episodes,AGOMELATINE|VORTIOXETINE,MENTAL HEALTH\nmalignant melanoma,VEMURAFENIB,MEDICAL ONCOLOGY\nmalignant pleural mesothelioma,NIVOLUMAB|PEMETREXED,MEDICAL ONCOLOGY\nmanic episode,ARIPIPRAZOLE,MENTAL HEALTH\nmantle cell lymphoma,AUTOLOGOUS|BORTEZOMIB|IBRUTINIB|LENALIDOMIDE|TEMSIROLIMUS,CLINICAL HAEMATOLOGY\nmelanoma,COBIMETINIB|DABRAFENIB|ENCORAFENIB|IPILIMUMAB|NIVOLUMAB|PEMBROLIZUMAB|TALIMOGENE|TRAMETINIB,MEDICAL ONCOLOGY\nmerkel cell carcinoma,AVELUMAB,MEDICAL ONCOLOGY\nmigraine,BOTULINUM|EPTINEZUMAB|ERENUMAB|FREMANEZUMAB|GALCANEZUMAB,NEUROLOGY\nmotor neurone disease,RILUZOLE,NEUROLOGY\nmultiple myeloma,BORTEZOMIB|THALIDOMIDE|CARFILZOMIB|DARATUMUMAB|DENOSUMAB|ELOTUZUMAB|ISATUXIMAB|IXAZOMIB|LENALIDOMIDE|PANOBINOSTAT|POMALIDOMIDE|SELINEXOR|TECLISTAMAB,CLINICAL HAEMATOLOGY\nmultiple sclerosis,ALEMTUZUMAB|BETA|CLADRIBINE|DACLIZUMAB|DIMETHYL|DIROXIMEL|FINGOLIMOD|INTERFERON|NATALIZUMAB|OCRELIZUMAB|OZANIMOD|PEGINTERFERON|PONESIMOD|SIPONIMOD|TERIFLUNOMIDE,NEUROLOGY\nmyelodysplastic,LENALIDOMIDE|LUSPATERCEPT,CLINICAL HAEMATOLOGY\nmyelofibrosis,FEDRATINIB|RUXOLITINIB,CLINICAL HAEMATOLOGY\nmyocardial infarction,ALTEPLASE|BIVALIRUDIN|TICAGRELOR,CARDIOLOGY\nmyotonia,MEXILETINE,NEUROLOGY\nnarcolepsy,SOLRIAMFETOL,NEUROLOGY\nneuroendocrine tumour,EVEROLIMUS|LUTETIUM,MEDICAL ONCOLOGY\nnon-small cell lung cancer,ATEZOLIZMAB|DOCETAXEL|ERLOTINIB|PEMETREXED,MEDICAL ONCOLOGY\nnon-small-cell lung cancer,AFATINIB|ALECTINIB|AMIVANTAMAB|ATEZOLIZUMAB|BEVACIZUMAB|BRIGATINIB|CEMIPLIMAB|CERITINIB|CRIZOTINIB|DABRAFENIB|DACOMITINIB|DURVALUMAB|ENTRECTINIB|ERLOTINIB|GEFITINIB|LORLATINIB|MOBOCERTINIB|NECITUMUMAB|NIVOLUMAB|OSIMERTINIB|PACLITAXEL|PEMBROLIZUMAB|PEMETREXED|PRALSETINIB|RAMUCIRUMAB|SELPERCATINIB|SOTORASIB|TEPOTINIB,MEDICAL ONCOLOGY\nobesity,LIRAGLUTIDE|NALTREXONE|ORLISTAT|SEMAGLUTIDE|SIBUTRAMINE,ENDOCRINOLOGY\noesophageal cancer,NIVOLUMAB,MEDICAL ONCOLOGY\nosteoarthritis,CELECOXIB,RHEUMATOLOGY\nosteoporosis,ALENDRONATE|DENOSUMAB|ORAL|ROMOSOZUMAB,RHEUMATOLOGY\nosteosarcoma,MIFAMURTIDE,MEDICAL ONCOLOGY\novarian cancer,BEVACIZUMAB|PACLITAXEL|PEGYLATED|TOPOTECAN|TRABECTEDIN,MEDICAL ONCOLOGY\noverweight,RIMONABANT,ENDOCRINOLOGY\npancreatic cancer,GEMCITABINE|OLAPARIB|PACLITAXEL|PEGYLATED,MEDICAL ONCOLOGY\nparoxysmal nocturnal haemoglobinuria,PEGCETACOPLAN|RAVULIZUMAB,CLINICAL HAEMATOLOGY\nperipheral arterial disease,NAFTIDROFYRYL,VASCULAR SURGERY\nplaque psoriasis,ADALIMUMAB|APREMILAST|BIMEKIZUMAB|BRODALUMAB|CERTOLIZUMAB|GUSELKUMAB|INFLIXIMAB|IXEKIZUMAB|RISANKIZUMAB|SECUKINUMAB|TILDRAKIZUAMB|USTEKINUMAB,DERMATOLOGY\npolycystic kidney disease,TOLVAPTAN,NEPHROLOGY\npolycythaemia vera,RUXOLITINIB,CLINICAL HAEMATOLOGY\npregnancy,ROUTINE,OBSTETRICS\nprimary biliary cholangitis,OBETICHOLIC,GASTROENTEROLOGY\nprimary hypercholesterolaemia,ALIROCUMAB|EVOLOCUMAB,CARDIOLOGY\nprostate cancer,ABIRATERONE|APALUTAMIDE|CABAZITAXEL|DAROLUTAMIDE|DEGARELIX|DENOSUMAB|DOCETAXEL|ENZALUTAMIDE|OLAPARIB|PADELIPORFIN|RADIUM-|RADIUM|SIPULEUCEL-T,MEDICAL ONCOLOGY\npsoriasis,EFALUZIMAB,DERMATOLOGY\npsoriatic arthritis,ABATACEPT|ADALIMUMAB|APREMILAST|CERTOLIZUMAB|ETANERCEPT|GOLIMUMAB|GUSELKUMAB|IXEKIZUMAB|RISANKIZUMAB|TOFACITINIB|UPADACITINIB|USTEKINUMAB,RHEUMATOLOGY\npulmonary embolism,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,THORACIC MEDICINE\npulmonary fibrosis,NINTEDANIB|PIRFENIDONE,THORACIC MEDICINE\nrelapsing multiple sclerosis,OFATUMUMAB,NEUROLOGY\nrenal cell carcinoma,AVELUMAB|AXITINIB|BEVACIZUMAB|CABOZANTINIB|EVEROLIMUS|LENVATINIB|NIVOLUMAB|PAZOPANIB|PEMBROLIZUMAB|SUNITINIB|TIVOZANIB,MEDICAL ONCOLOGY\nrenal transplantation,BASILIXIMAB|INDUCTION,NEPHROLOGY\nretinal vein occlusion,AFLIBERCEPT|DEXAMETHASONE|RANIBIZUMAB,OPHTHALMOLOGY\nrheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|CELECOXIB|CERTOLIZUMAB|ETANERCEPT|FILGOTINIB|GOLIMUMAB|RITUXIMAB|SARILUMAB|TOCILIZUMAB|TOFACITINIB|UPADACITINIB,RHEUMATOLOGY\nrivaroxaban,ANDEXANET,CARDIOLOGY\nschizophrenia,AMISULPRIDE|ARIPIPRAZOLE|LOXAPINE,MENTAL HEALTH\nseizures,CANNABIDIOL,NEUROLOGY\nsepsis,DROTRECOGIN,INFECTIOUS DISEASES\nsevere persistent allergic asthma,OMALIZUMAB,THORACIC MEDICINE\nshort bowel syndrome,TEDUGLUTIDE,GASTROENTEROLOGY\nsickle cell disease,CRIZANLIZUMAB,CLINICAL HAEMATOLOGY\nsleep apnoea,PITOLISANT|SOLRIAMFETOL,THORACIC MEDICINE\nsmoking cessation,NICOTINE|VARENICLINE,THORACIC MEDICINE\nsoft tissue sarcoma,INTRAVENOUS|NBTXR-|OLARATUMAB,MEDICAL ONCOLOGY\nspinal muscular atrophy,NUSINERSEN|RISDIPLAM,NEUROLOGY\nsquamous cell,CETUXIMAB,MEDICAL ONCOLOGY\nsquamous cell carcinoma,CEMIPLIMAB|NIVOLUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\nstem cell transplant,MELPHALAN|TREOSULFAN,CLINICAL HAEMATOLOGY\nstroke,APIXABAN|DABIGATRAN|EDOXABAN|RIVAROXABAN,STROKE MEDICINE\nsystemic lupus erythematosus,ANIFROLUMAB|ETANERCEPT,RHEUMATOLOGY\nsystemic mastocytosis,MIDOSTAURIN,CLINICAL HAEMATOLOGY\nthrombocytopenic purpura,ELTROMBOPAG|ROMIPLOSTIM,CLINICAL HAEMATOLOGY\nthrombotic thrombocytopenic purpura,CAPLACIZUMAB,CLINICAL HAEMATOLOGY\nthyroid cancer,CABOZANTINIB|LENVATINIB|SELPERCATINIB|VANDETANIB,MEDICAL ONCOLOGY\ntophaceous gout,PEGLOTICASE,RHEUMATOLOGY\ntransitional cell carcinoma,VINFLUNINE,MEDICAL ONCOLOGY\ntuberous sclerosis,CANNABIDIOL,NEUROLOGY\ntype 1 diabetes,CONTINUOUS|DAPAGLIFLOZIN|INSULIN|SOTAGLIFLOZIN,DIABETIC MEDICINE\ntype 2 diabetes,CANAGLIFLOZIN|CONTINUOUS|DAPAGLIFLOZIN|EMPAGLIFLOZIN|ERTUGLIFLOZIN|EXENATIDE|FINERENONE|INSULIN|LIRAGLUTIDE|PIOGLITAZONE|ROSIGLITAZONE,DIABETIC MEDICINE\nulcerative colitis,ADALIMUMAB|INFLIXIMAB|FILGOTINIB|OZANIMOD|TOFACITINIB|UPADACITINIB|USTEKINUMAB|VEDOLIZUMAB,GASTROENTEROLOGY\nurothelial carcinoma,ATEZOLIZUMAB|PEMBROLIZUMAB,MEDICAL ONCOLOGY\nurticaria,OMALIZUMAB,DERMATOLOGY\nuterine fibroids,RELUGOLIX,GYNAECOLOGY\nuveitis,ADALIMUMAB|FLUOCINOLONE,OPHTHALMOLOGY\nvascular disease,MODIFIED-RELEASE|CLOPIDOGREL,VASCULAR SURGERY\nvasculitis,RITUXIMAB,RHEUMATOLOGY\nvenous thromboembolism,APIXABAN|DABIGATRAN|RIVAROXABAN,CLINICAL HAEMATOLOGY\n","numLines":167,"startLine":1,"totalLines":167}}}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013gSjU7MeMnCV9sYiUzUdur","type":"tool_result","content":"     1\"\"\"\n     2Diagnosis lookup module for NHS Patient Pathway Analysis.\n     3\n     4Provides functions to validate patient indications by checking GP diagnosis records\n     5against SNOMED cluster codes. Uses the drug-to-cluster mapping from\n     6drug_indication_clusters.csv and queries Snowflake for SNOMED codes and GP records.\n     7\n     8Key workflow:\n     91. Get drug's valid indication clusters from local mapping\n    102. Get all SNOMED codes for those clusters from Snowflake\n    113. Check if patient has any of those SNOMED codes in GP records\n    124. Report indication validation status\n    13\n    14IMPORTANT: HCD activity data indication codes are UNRELIABLE. This module uses\n    15GP/Primary Care data (PrimaryCareClinicalCoding) as the authoritative source.\n    16\"\"\"\n    17\n    18from dataclasses import dataclass, field\n    19from datetime import date, datetime\n    20from pathlib import Path\n    21from typing import Optional, Callable, Any, cast, TYPE_CHECKING\n    22import csv\n    23\n    24if TYPE_CHECKING:\n    25    import pandas as pd\n    26\n    27from core.logging_config import get_logger\n    28from data_processing.database import DatabaseManager, default_db_manager\n    29from data_processing.snowflake_connector import (\n    30    SnowflakeConnector,\n    31    get_connector,\n    32    is_snowflake_available,\n    33    is_snowflake_configured,\n    34    SNOWFLAKE_AVAILABLE,\n    35)\n    36from data_processing.cache import get_cache, is_cache_enabled\n    37\n    38logger = get_logger(__name__)\n    39\n    40\n    41@dataclass\n    42class ClusterSnomedCodes:\n    43    \"\"\"SNOMED codes for a clinical coding cluster.\"\"\"\n    44    cluster_id: str\n    45    cluster_description: str\n    46    snomed_codes: list[str] = field(default_factory=list)\n    47    snomed_descriptions: dict[str, str] = field(default_factory=dict)\n    48\n    49    @property\n    50    def code_count(self) -> int:\n    51        return len(self.snomed_codes)\n    52\n    53\n    54@dataclass\n    55class IndicationValidationResult:\n    56    \"\"\"Result of validating a patient's indication for a drug.\"\"\"\n    57    patient_pseudonym: str\n    58    drug_name: str\n    59    has_valid_indication: bool\n    60    matched_cluster_id: Optional[str] = None\n    61    matched_snomed_code: Optional[str] = None\n    62    matched_snomed_description: Optional[str] = None\n    63    checked_clusters: list[str] = field(default_factory=list)\n    64    total_codes_checked: int = 0\n    65    source: str = \"GP_SNOMED\"  # GP_SNOMED | NONE\n    66    error_message: Optional[str] = None\n    67\n    68\n    69@dataclass\n    70class DrugIndicationMatchRate:\n    71    \"\"\"Match rate statistics for a drug's indication validation.\"\"\"\n    72    drug_name: str\n    73    total_patients: int\n    74    patients_with_indication: int\n    75    patients_without_indication: int\n    76    match_rate: float  # 0.0 to 1.0\n    77    clusters_checked: list[str] = field(default_factory=list)\n    78    sample_unmatched: list[str] = field(default_factory=list)  # Sample patient IDs\n    79\n    80\n    81@dataclass\n    82class DrugSnomedMapping:\n    83    \"\"\"SNOMED code mapping for a drug from ref_drug_snomed_mapping.\"\"\"\n    84    snomed_code: str\n    85    snomed_description: str\n    86    search_term: str\n    87    primary_directorate: str\n    88    indication: str = \"\"\n    89    ta_id: str = \"\"\n    90\n    91\n    92@dataclass\n    93class DirectSnomedMatchResult:\n    94    \"\"\"Result of direct SNOMED code lookup in GP records.\"\"\"\n    95    patient_pseudonym: str\n    96    matched: bool\n    97    snomed_code: Optional[str] = None\n    98    snomed_description: Optional[str] = None\n    99    search_term: Optional[str] = None\n   100    primary_directorate: Optional[str] = None\n   101    event_date: Optional[datetime] = None\n   102    source: str = \"DIRECT_SNOMED\"  # DIRECT_SNOMED | NONE\n   103\n   104\n   105@dataclass\n   106class DirectorateAssignment:\n   107    \"\"\"Result of directorate assignment for a patient-drug combination.\"\"\"\n   108    upid: str\n   109    drug_name: str\n   110    directorate: Optional[str]\n   111    search_term: Optional[str] = None\n   112    source: str = \"FALLBACK\"  # DIAGNOSIS | FALLBACK\n   113    snomed_code: Optional[str] = None\n   114    event_date: Optional[datetime] = None\n   115\n   116\n   117def get_drug_clusters(\n   118    drug_name: str,\n   119    db_manager: Optional[DatabaseManager] = None\n   120) -> list[dict]:\n   121    \"\"\"\n   122    Get all SNOMED cluster mappings for a drug from local SQLite.\n   123\n   124    Args:\n   125        drug_name: Drug name to look up (case-insensitive)\n   126        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n   127\n   128    Returns:\n   129        List of dicts with keys: drug_name, indication, cluster_id,\n   130        cluster_description, nice_ta_reference\n   131    \"\"\"\n   132    if db_manager is None:\n   133        db_manager = default_db_manager\n   134\n   135    query = \"\"\"\n   136        SELECT drug_name, indication, cluster_id, cluster_description, nice_ta_reference\n   137        FROM ref_drug_indication_clusters\n   138        WHERE UPPER(drug_name) = UPPER(?)\n   139        ORDER BY indication, cluster_id\n   140    \"\"\"\n   141\n   142    try:\n   143        with db_manager.get_connection() as conn:\n   144            cursor = conn.execute(query, (drug_name,))\n   145            rows = cursor.fetchall()\n   146\n   147            results = []\n   148            for row in rows:\n   149                results.append({\n   150                    \"drug_name\": row[\"drug_name\"],\n   151                    \"indication\": row[\"indication\"],\n   152                    \"cluster_id\": row[\"cluster_id\"],\n   153                    \"cluster_description\": row[\"cluster_description\"],\n   154                    \"nice_ta_reference\": row[\"nice_ta_reference\"],\n   155                })\n   156\n   157            logger.debug(f\"Found {len(results)} cluster mappings for drug '{drug_name}'\")\n   158            return results\n   159\n   160    except Exception as e:\n   161        logger.error(f\"Error getting clusters for drug '{drug_name}': {e}\")\n   162        return []\n   163\n   164\n   165def get_drug_cluster_ids(\n   166    drug_name: str,\n   167    db_manager: Optional[DatabaseManager] = None\n   168) -> list[str]:\n   169    \"\"\"\n   170    Get unique cluster IDs for a drug.\n   171\n   172    Args:\n   173        drug_name: Drug name to look up\n   174        db_manager: Optional DatabaseManager\n   175\n   176    Returns:\n   177        List of unique cluster IDs\n   178    \"\"\"\n   179    clusters = get_drug_clusters(drug_name, db_manager)\n   180    return list(set(c[\"cluster_id\"] for c in clusters))\n   181\n   182\n   183def get_drug_snomed_codes(\n   184    drug_name: str,\n   185    db_manager: Optional[DatabaseManager] = None\n   186) -> list[DrugSnomedMapping]:\n   187    \"\"\"\n   188    Get all SNOMED codes for a drug from local ref_drug_snomed_mapping table.\n   189\n   190    This uses the enriched mapping CSV data loaded into SQLite, which provides\n   191    direct SNOMED-to-drug mappings with Search_Term and PrimaryDirectorate.\n   192\n   193    Args:\n   194        drug_name: Drug name to look up (case-insensitive, matches cleaned_drug_name)\n   195        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n   196\n   197    Returns:\n   198        List of DrugSnomedMapping with snomed_code, snomed_description,\n   199        search_term, primary_directorate, indication, ta_id\n   200    \"\"\"\n   201    if db_manager is None:\n   202        db_manager = default_db_manager\n   203\n   204    query = \"\"\"\n   205        SELECT DISTINCT\n   206            snomed_code,\n   207            snomed_description,\n   208            search_term,\n   209            primary_directorate,\n   210            indication,\n   211            ta_id\n   212        FROM ref_drug_snomed_mapping\n   213        WHERE UPPER(cleaned_drug_name) = UPPER(?)\n   214           OR UPPER(drug_name) = UPPER(?)\n   215        ORDER BY search_term, snomed_code\n   216    \"\"\"\n   217\n   218    try:\n   219        with db_manager.get_connection() as conn:\n   220            cursor = conn.execute(query, (drug_name, drug_name))\n   221            rows = cursor.fetchall()\n   222\n   223            results = []\n   224            for row in rows:\n   225                results.append(DrugSnomedMapping(\n   226                    snomed_code=row[\"snomed_code\"],\n   227                    snomed_description=row[\"snomed_description\"] or \"\",\n   228                    search_term=row[\"search_term\"] or \"\",\n   229                    primary_directorate=row[\"primary_directorate\"] or \"\",\n   230                    indication=row[\"indication\"] or \"\",\n   231                    ta_id=row[\"ta_id\"] or \"\",\n   232                ))\n   233\n   234            logger.debug(f\"Found {len(results)} SNOMED mappings for drug '{drug_name}'\")\n   235            return results\n   236\n   237    except Exception as e:\n   238        logger.error(f\"Error getting SNOMED codes for drug '{drug_name}': {e}\")\n   239        return []\n   240\n   241\n   242def patient_has_indication_direct(\n   243    patient_pseudonym: str,\n   244    drug_snomed_mappings: list[DrugSnomedMapping],\n   245    connector: Optional[SnowflakeConnector] = None,\n   246    before_date: Optional[date] = None,\n   247) -> DirectSnomedMatchResult:\n   248    \"\"\"\n   249    Check if patient has any of the SNOMED codes in their GP records.\n   250\n   251    This is the direct SNOMED lookup - it queries PrimaryCareClinicalCoding\n   252    for exact SNOMED code matches (not via cluster). Returns the most recent\n   253    match by EventDateTime if multiple matches exist.\n   254\n   255    Args:\n   256        patient_pseudonym: Patient's pseudonymised NHS number\n   257        drug_snomed_mappings: List of DrugSnomedMapping from get_drug_snomed_codes()\n   258        connector: Optional SnowflakeConnector (defaults to singleton)\n   259        before_date: Optional date - only check diagnoses before this date\n   260\n   261    Returns:\n   262        DirectSnomedMatchResult with match details (most recent by EventDateTime)\n   263    \"\"\"\n   264    result = DirectSnomedMatchResult(\n   265        patient_pseudonym=patient_pseudonym,\n   266        matched=False,\n   267        source=\"NONE\",\n   268    )\n   269\n   270    if not drug_snomed_mappings:\n   271        return result\n   272\n   273    if not SNOWFLAKE_AVAILABLE:\n   274        logger.warning(\"Snowflake connector not available\")\n   275        return result\n   276\n   277    if not is_snowflake_configured():\n   278        logger.warning(\"Snowflake not configured - cannot check GP records\")\n   279        return result\n   280\n   281    if connector is None:\n   282        connector = get_connector()\n   283\n   284    # Build lookup dict for mapping snomed_code -> (search_term, primary_directorate, snomed_description)\n   285    snomed_lookup = {\n   286        m.snomed_code: (m.search_term, m.primary_directorate, m.snomed_description)\n   287        for m in drug_snomed_mappings\n   288    }\n   289\n   290    # Get unique SNOMED codes\n   291    snomed_codes = list(snomed_lookup.keys())\n   292\n   293    # Build placeholders for SNOMED codes\n   294    placeholders = \", \".join([\"%s\"] * len(snomed_codes))\n   295\n   296    # Query to find most recent matching SNOMED code in GP records\n   297    query = f'''\n   298        SELECT\n   299            \"SNOMEDCode\",\n   300            \"EventDateTime\"\n   301        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n   302        WHERE \"PatientPseudonym\" = %s\n   303            AND \"SNOMEDCode\" IN ({placeholders})\n   304    '''\n   305\n   306    params: list = [patient_pseudonym] + snomed_codes\n   307\n   308    if before_date:\n   309        query += ' AND \"EventDateTime\" < %s'\n   310        params.append(before_date.isoformat())\n   311\n   312    query += ' ORDER BY \"EventDateTime\" DESC LIMIT 1'\n   313\n   314    try:\n   315        results = connector.execute_dict(query, tuple(params))\n   316\n   317        if results:\n   318            row = results[0]\n   319            matched_code = row.get(\"SNOMEDCode\")\n   320            event_dt = row.get(\"EventDateTime\")\n   321\n   322            if matched_code and matched_code in snomed_lookup:\n   323                search_term, primary_dir, snomed_desc = snomed_lookup[matched_code]\n   324\n   325                return DirectSnomedMatchResult(\n   326                    patient_pseudonym=patient_pseudonym,\n   327                    matched=True,\n   328                    snomed_code=matched_code,\n   329                    snomed_description=snomed_desc,\n   330                    search_term=search_term,\n   331                    primary_directorate=primary_dir,\n   332                    event_date=event_dt,\n   333                    source=\"DIRECT_SNOMED\",\n   334                )\n   335\n   336        return result\n   337\n   338    except Exception as e:\n   339        logger.error(f\"Error checking direct SNOMED for patient '{patient_pseudonym}': {e}\")\n   340        return result\n   341\n   342\n   343def get_directorate_from_diagnosis(\n   344    upid: str,\n   345    drug_name: str,\n   346    connector: Optional[SnowflakeConnector] = None,\n   347    db_manager: Optional[DatabaseManager] = None,\n   348    before_date: Optional[date] = None,\n   349) -> DirectorateAssignment:\n   350    \"\"\"\n   351    Get directorate assignment for a patient-drug combination using diagnosis-based lookup.\n   352\n   353    This function attempts to assign a directorate based on the patient's GP records\n   354    (direct SNOMED code matching). If no match is found, it returns a FALLBACK result\n   355    indicating that the caller should use alternative assignment methods (e.g.,\n   356    department_identification() from tools/data.py).\n   357\n   358    Workflow:\n   359    1. Get all SNOMED codes for the drug from ref_drug_snomed_mapping\n   360    2. Query patient's GP records for matching SNOMED codes\n   361    3. If match found  return diagnosis-based directorate and search_term\n   362    4. If no match  return FALLBACK result (caller handles fallback logic)\n   363\n   364    Args:\n   365        upid: Patient's unique patient ID (Provider Code[:3] + PersonKey)\n   366        drug_name: Drug name to look up\n   367        connector: Optional SnowflakeConnector (defaults to singleton)\n   368        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n   369        before_date: Optional date - only check diagnoses before this date\n   370\n   371    Returns:\n   372        DirectorateAssignment with directorate, search_term, and source\n   373    \"\"\"\n   374    result = DirectorateAssignment(\n   375        upid=upid,\n   376        drug_name=drug_name,\n   377        directorate=None,\n   378        source=\"FALLBACK\",\n   379    )\n   380\n   381    # Step 1: Get SNOMED codes for the drug\n   382    drug_snomed_mappings = get_drug_snomed_codes(drug_name, db_manager)\n   383\n   384    if not drug_snomed_mappings:\n   385        logger.debug(f\"No SNOMED mappings found for drug '{drug_name}' - using fallback\")\n   386        return result\n   387\n   388    # Step 2: Check Snowflake availability\n   389    if not SNOWFLAKE_AVAILABLE:\n   390        logger.debug(\"Snowflake not available - using fallback\")\n   391        return result\n   392\n   393    if not is_snowflake_configured():\n   394        logger.debug(\"Snowflake not configured - using fallback\")\n   395        return result\n   396\n   397    # Step 3: Get patient pseudonym from UPID\n   398    # UPID format is Provider Code (3 chars) + PersonKey\n   399    # We need to query Snowflake to get the PatientPseudonym for this PersonKey\n   400    # However, patient_has_indication_direct expects PatientPseudonym, not UPID\n   401    # For now, we'll use UPID as the identifier - the actual integration\n   402    # will need to happen at the DataFrame level where we have PersonKey\n   403    #\n   404    # NOTE: This function will be called from the pipeline where we have\n   405    # access to PatientPseudonym. The UPID is passed for logging/tracking.\n   406\n   407    # Actually, looking at the pipeline, we need PatientPseudonym, not UPID.\n   408    # The caller should pass the PatientPseudonym or we need to look it up.\n   409    # For now, let's assume the caller will use this in a batch context\n   410    # where they can map UPID -> PatientPseudonym.\n   411\n   412    # Let me reconsider: the function signature takes UPID but we need\n   413    # PatientPseudonym for Snowflake. In the pipeline context (fetch_and_transform_data),\n   414    # we'll have the PersonKey column which IS the PatientPseudonym.\n   415    # So UPID = ProviderCode[:3] + PersonKey, and PersonKey = PatientPseudonym.\n   416    #\n   417    # We can extract PatientPseudonym from UPID by removing the first 3 chars.\n   418    patient_pseudonym = upid[3:] if len(upid) > 3 else upid\n   419\n   420    # Step 4: Check patient's GP records for matching SNOMED codes\n   421    match_result = patient_has_indication_direct(\n   422        patient_pseudonym=patient_pseudonym,\n   423        drug_snomed_mappings=drug_snomed_mappings,\n   424        connector=connector,\n   425        before_date=before_date,\n   426    )\n   427\n   428    if match_result.matched and match_result.primary_directorate:\n   429        return DirectorateAssignment(\n   430            upid=upid,\n   431            drug_name=drug_name,\n   432            directorate=match_result.primary_directorate,\n   433            search_term=match_result.search_term,\n   434            source=\"DIAGNOSIS\",\n   435            snomed_code=match_result.snomed_code,\n   436            event_date=match_result.event_date,\n   437        )\n   438\n   439    # No match found - return fallback result\n   440    return result\n   441\n   442\n   443def get_cluster_snomed_codes(\n   444    cluster_id: str,\n   445    connector: Optional[SnowflakeConnector] = None,\n   446    use_cache: bool = True,\n   447) -> ClusterSnomedCodes:\n   448    \"\"\"\n   449    Get all SNOMED codes for a cluster from Snowflake.\n   450\n   451    Queries the ClinicalCodingClusterSnomedCodes table to get all SNOMED codes\n   452    that belong to the specified cluster.\n   453\n   454    Args:\n   455        cluster_id: Cluster ID to look up (e.g., 'RARTH_COD', 'PSORIASIS_COD')\n   456        connector: Optional SnowflakeConnector (defaults to singleton)\n   457        use_cache: Whether to use cached results (default True)\n   458\n   459    Returns:\n   460        ClusterSnomedCodes with list of SNOMED codes and descriptions\n   461    \"\"\"\n   462    if not SNOWFLAKE_AVAILABLE:\n   463        logger.warning(\"Snowflake connector not available\")\n   464        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n   465\n   466    if not is_snowflake_configured():\n   467        logger.warning(\"Snowflake not configured - cannot get cluster codes\")\n   468        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n   469\n   470    # Check cache first\n   471    cache_key = f\"cluster_snomed_{cluster_id}\"\n   472    if use_cache and is_cache_enabled():\n   473        cache = get_cache()\n   474        cached = cache.get(cache_key)\n   475        if cached is not None and len(cached) > 0:\n   476            logger.debug(f\"Using cached SNOMED codes for cluster '{cluster_id}'\")\n   477            cached_dict = cached[0]  # First element is our data dict\n   478            return ClusterSnomedCodes(\n   479                cluster_id=cluster_id,\n   480                cluster_description=str(cached_dict.get(\"description\", \"\")),\n   481                snomed_codes=list(cached_dict.get(\"codes\", [])),\n   482                snomed_descriptions=dict(cached_dict.get(\"descriptions\", {})),\n   483            )\n   484\n   485    if connector is None:\n   486        connector = get_connector()\n   487\n   488    query = '''\n   489        SELECT DISTINCT\n   490            \"Cluster_ID\",\n   491            \"Cluster_Description\",\n   492            \"SNOMEDCode\",\n   493            \"SNOMEDDescription\"\n   494        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n   495        WHERE \"Cluster_ID\" = %s\n   496        ORDER BY \"SNOMEDCode\"\n   497    '''\n   498\n   499    try:\n   500        results = connector.execute_dict(query, (cluster_id,))\n   501\n   502        if not results:\n   503            logger.warning(f\"No SNOMED codes found for cluster '{cluster_id}'\")\n   504            return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n   505\n   506        codes = []\n   507        descriptions = {}\n   508        description = results[0].get(\"Cluster_Description\", \"\") if results else \"\"\n   509\n   510        for row in results:\n   511            code = row.get(\"SNOMEDCode\")\n   512            if code:\n   513                codes.append(code)\n   514                descriptions[code] = row.get(\"SNOMEDDescription\", \"\")\n   515\n   516        logger.info(f\"Found {len(codes)} SNOMED codes for cluster '{cluster_id}'\")\n   517\n   518        # Cache the results (using query-based cache with fake params)\n   519        if use_cache and is_cache_enabled():\n   520            cache = get_cache()\n   521            cache_data = [{\n   522                \"description\": description,\n   523                \"codes\": codes,\n   524                \"descriptions\": descriptions,\n   525            }]\n   526            cache.set(cache_key, None, cache_data)  # type: ignore[arg-type]\n   527\n   528        return ClusterSnomedCodes(\n   529            cluster_id=cluster_id,\n   530            cluster_description=description,\n   531            snomed_codes=codes,\n   532            snomed_descriptions=descriptions,\n   533        )\n   534\n   535    except Exception as e:\n   536        logger.error(f\"Error getting SNOMED codes for cluster '{cluster_id}': {e}\")\n   537        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n   538\n   539\n   540def patient_has_indication(\n   541    patient_pseudonym: str,\n   542    cluster_ids: list[str],\n   543    connector: Optional[SnowflakeConnector] = None,\n   544    before_date: Optional[date] = None,\n   545) -> tuple[bool, Optional[str], Optional[str], Optional[str]]:\n   546    \"\"\"\n   547    Check if a patient has any SNOMED codes from the specified clusters in GP records.\n   548\n   549    Args:\n   550        patient_pseudonym: Patient's pseudonymised NHS number\n   551        cluster_ids: List of cluster IDs to check against\n   552        connector: Optional SnowflakeConnector\n   553        before_date: Optional date - only check diagnoses before this date\n   554\n   555    Returns:\n   556        Tuple of (has_indication, matched_cluster_id, matched_snomed_code, matched_description)\n   557    \"\"\"\n   558    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n   559        return False, None, None, None\n   560\n   561    if not cluster_ids:\n   562        return False, None, None, None\n   563\n   564    if connector is None:\n   565        connector = get_connector()\n   566\n   567    # Build placeholders for cluster IDs\n   568    placeholders = \", \".join([\"%s\"] * len(cluster_ids))\n   569\n   570    # Query to check if patient has any matching SNOMED code\n   571    query = f'''\n   572        SELECT\n   573            pc.\"SNOMEDCode\",\n   574            cc.\"Cluster_ID\",\n   575            cc.\"SNOMEDDescription\"\n   576        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\n   577        INNER JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" cc\n   578            ON pc.\"SNOMEDCode\" = cc.\"SNOMEDCode\"\n   579        WHERE pc.\"PatientPseudonym\" = %s\n   580            AND cc.\"Cluster_ID\" IN ({placeholders})\n   581    '''\n   582\n   583    params = [patient_pseudonym] + cluster_ids\n   584\n   585    if before_date:\n   586        query += ' AND pc.\"EventDateTime\" < %s'\n   587        params.append(before_date.isoformat())\n   588\n   589    query += ' LIMIT 1'\n   590\n   591    try:\n   592        results = connector.execute_dict(query, tuple(params))\n   593\n   594        if results:\n   595            row = results[0]\n   596            return (\n   597                True,\n   598                row.get(\"Cluster_ID\"),\n   599                row.get(\"SNOMEDCode\"),\n   600                row.get(\"SNOMEDDescription\"),\n   601            )\n   602\n   603        return False, None, None, None\n   604\n   605    except Exception as e:\n   606        logger.error(f\"Error checking indication for patient '{patient_pseudonym}': {e}\")\n   607        return False, None, None, None\n   608\n   609\n   610def validate_indication(\n   611    patient_pseudonym: str,\n   612    drug_name: str,\n   613    connector: Optional[SnowflakeConnector] = None,\n   614    db_manager: Optional[DatabaseManager] = None,\n   615    before_date: Optional[date] = None,\n   616) -> IndicationValidationResult:\n   617    \"\"\"\n   618    Validate that a patient has an appropriate indication for a drug.\n   619\n   620    Full validation workflow:\n   621    1. Get drug's valid indication clusters from local mapping\n   622    2. Check if patient has any matching SNOMED codes in GP records\n   623    3. Return detailed validation result\n   624\n   625    Args:\n   626        patient_pseudonym: Patient's pseudonymised NHS number\n   627        drug_name: Drug name to validate indication for\n   628        connector: Optional SnowflakeConnector\n   629        db_manager: Optional DatabaseManager\n   630        before_date: Optional date - only check diagnoses before this date\n   631\n   632    Returns:\n   633        IndicationValidationResult with validation details\n   634    \"\"\"\n   635    result = IndicationValidationResult(\n   636        patient_pseudonym=patient_pseudonym,\n   637        drug_name=drug_name,\n   638        has_valid_indication=False,\n   639    )\n   640\n   641    # Step 1: Get drug's cluster mappings\n   642    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n   643\n   644    if not cluster_ids:\n   645        result.error_message = f\"No cluster mappings found for drug '{drug_name}'\"\n   646        result.source = \"NONE\"\n   647        return result\n   648\n   649    result.checked_clusters = cluster_ids\n   650\n   651    # Step 2: Check Snowflake availability\n   652    if not SNOWFLAKE_AVAILABLE:\n   653        result.error_message = \"Snowflake connector not installed\"\n   654        result.source = \"NONE\"\n   655        return result\n   656\n   657    if not is_snowflake_configured():\n   658        result.error_message = \"Snowflake not configured\"\n   659        result.source = \"NONE\"\n   660        return result\n   661\n   662    # Step 3: Check patient GP records\n   663    has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n   664        patient_pseudonym=patient_pseudonym,\n   665        cluster_ids=cluster_ids,\n   666        connector=connector,\n   667        before_date=before_date,\n   668    )\n   669\n   670    result.has_valid_indication = has_indication\n   671    result.matched_cluster_id = matched_cluster\n   672    result.matched_snomed_code = matched_code\n   673    result.matched_snomed_description = matched_desc\n   674    result.source = \"GP_SNOMED\" if has_indication else \"NONE\"\n   675\n   676    return result\n   677\n   678\n   679def get_indication_match_rate(\n   680    drug_name: str,\n   681    patient_pseudonyms: list[str],\n   682    connector: Optional[SnowflakeConnector] = None,\n   683    db_manager: Optional[DatabaseManager] = None,\n   684    sample_unmatched_count: int = 10,\n   685) -> DrugIndicationMatchRate:\n   686    \"\"\"\n   687    Calculate indication match rate for a drug across a list of patients.\n   688\n   689    Args:\n   690        drug_name: Drug name to check\n   691        patient_pseudonyms: List of patient pseudonymised NHS numbers\n   692        connector: Optional SnowflakeConnector\n   693        db_manager: Optional DatabaseManager\n   694        sample_unmatched_count: Number of unmatched patient IDs to include in sample\n   695\n   696    Returns:\n   697        DrugIndicationMatchRate with match statistics\n   698    \"\"\"\n   699    if connector is None and SNOWFLAKE_AVAILABLE and is_snowflake_configured():\n   700        connector = get_connector()\n   701\n   702    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n   703\n   704    total = len(patient_pseudonyms)\n   705    matched = 0\n   706    unmatched = 0\n   707    sample_unmatched: list[str] = []\n   708\n   709    if not cluster_ids:\n   710        logger.warning(f\"No cluster mappings for drug '{drug_name}' - all patients will be unmatched\")\n   711        return DrugIndicationMatchRate(\n   712            drug_name=drug_name,\n   713            total_patients=total,\n   714            patients_with_indication=0,\n   715            patients_without_indication=total,\n   716            match_rate=0.0,\n   717            clusters_checked=[],\n   718            sample_unmatched=patient_pseudonyms[:sample_unmatched_count],\n   719        )\n   720\n   721    for i, pseudonym in enumerate(patient_pseudonyms):\n   722        if i > 0 and i % 100 == 0:\n   723            logger.info(f\"Validating indications: {i}/{total} ({100*i/total:.1f}%)\")\n   724\n   725        has_indication, _, _, _ = patient_has_indication(\n   726            patient_pseudonym=pseudonym,\n   727            cluster_ids=cluster_ids,\n   728            connector=connector,\n   729        )\n   730\n   731        if has_indication:\n   732            matched += 1\n   733        else:\n   734            unmatched += 1\n   735            if len(sample_unmatched) < sample_unmatched_count:\n   736                sample_unmatched.append(pseudonym)\n   737\n   738    match_rate = matched / total if total > 0 else 0.0\n   739\n   740    logger.info(f\"Indication match rate for '{drug_name}': {100*match_rate:.1f}% ({matched}/{total})\")\n   741\n   742    return DrugIndicationMatchRate(\n   743        drug_name=drug_name,\n   744        total_patients=total,\n   745        patients_with_indication=matched,\n   746        patients_without_indication=unmatched,\n   747        match_rate=match_rate,\n   748        clusters_checked=cluster_ids,\n   749        sample_unmatched=sample_unmatched,\n   750    )\n   751\n   752\n   753def batch_validate_indications(\n   754    patient_drug_pairs: list[tuple[str, str]],\n   755    connector: Optional[SnowflakeConnector] = None,\n   756    db_manager: Optional[DatabaseManager] = None,\n   757    progress_callback: Optional[Callable[[int, int], None]] = None,\n   758) -> list[IndicationValidationResult]:\n   759    \"\"\"\n   760    Validate indications for multiple patient-drug pairs efficiently.\n   761\n   762    Args:\n   763        patient_drug_pairs: List of (patient_pseudonym, drug_name) tuples\n   764        connector: Optional SnowflakeConnector\n   765        db_manager: Optional DatabaseManager\n   766        progress_callback: Optional callback(current, total) for progress updates\n   767\n   768    Returns:\n   769        List of IndicationValidationResult for each pair\n   770    \"\"\"\n   771    results = []\n   772    total = len(patient_drug_pairs)\n   773\n   774    # Cache cluster lookups by drug\n   775    drug_clusters_cache = {}\n   776\n   777    for i, (pseudonym, drug_name) in enumerate(patient_drug_pairs):\n   778        if progress_callback:\n   779            progress_callback(i + 1, total)\n   780\n   781        # Get clusters from cache or lookup\n   782        drug_upper = drug_name.upper()\n   783        if drug_upper not in drug_clusters_cache:\n   784            drug_clusters_cache[drug_upper] = get_drug_cluster_ids(drug_name, db_manager)\n   785\n   786        cluster_ids = drug_clusters_cache[drug_upper]\n   787\n   788        if not cluster_ids:\n   789            results.append(IndicationValidationResult(\n   790                patient_pseudonym=pseudonym,\n   791                drug_name=drug_name,\n   792                has_valid_indication=False,\n   793                source=\"NONE\",\n   794                error_message=f\"No cluster mappings for drug '{drug_name}'\",\n   795            ))\n   796            continue\n   797\n   798        # Check patient indication\n   799        has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n   800            patient_pseudonym=pseudonym,\n   801            cluster_ids=cluster_ids,\n   802            connector=connector,\n   803        )\n   804\n   805        results.append(IndicationValidationResult(\n   806            patient_pseudonym=pseudonym,\n   807            drug_name=drug_name,\n   808            has_valid_indication=has_indication,\n   809            matched_cluster_id=matched_cluster,\n   810            matched_snomed_code=matched_code,\n   811            matched_snomed_description=matched_desc,\n   812            checked_clusters=cluster_ids,\n   813            source=\"GP_SNOMED\" if has_indication else \"NONE\",\n   814        ))\n   815\n   816    matched_count = sum(1 for r in results if r.has_valid_indication)\n   817    logger.info(f\"Batch validation complete: {matched_count}/{total} ({100*matched_count/total:.1f}%) with valid indications\")\n   818\n   819    return results\n   820\n   821\n   822def get_available_clusters(\n   823    connector: Optional[SnowflakeConnector] = None,\n   824) -> list[dict]:\n   825    \"\"\"\n   826    Get list of all available SNOMED clusters from Snowflake.\n   827\n   828    Returns:\n   829        List of dicts with cluster_id, cluster_description, code_count\n   830    \"\"\"\n   831    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n   832        logger.warning(\"Snowflake not available - cannot list clusters\")\n   833        return []\n   834\n   835    if connector is None:\n   836        connector = get_connector()\n   837\n   838    query = '''\n   839        SELECT\n   840            \"Cluster_ID\",\n   841            \"Cluster_Description\",\n   842            COUNT(DISTINCT \"SNOMEDCode\") as code_count\n   843        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n   844        GROUP BY \"Cluster_ID\", \"Cluster_Description\"\n   845        ORDER BY \"Cluster_ID\"\n   846    '''\n   847\n   848    try:\n   849        results = connector.execute_dict(query)\n   850\n   851        clusters = []\n   852        for row in results:\n   853            clusters.append({\n   854                \"cluster_id\": row.get(\"Cluster_ID\"),\n   855                \"cluster_description\": row.get(\"Cluster_Description\"),\n   856                \"code_count\": row.get(\"code_count\", 0),\n   857            })\n   858\n   859        logger.info(f\"Found {len(clusters)} available SNOMED clusters\")\n   860        return clusters\n   861\n   862    except Exception as e:\n   863        logger.error(f\"Error getting available clusters: {e}\")\n   864        return []\n   865\n   866\n   867def batch_lookup_indication_groups(\n   868    df: \"pd.DataFrame\",\n   869    connector: Optional[SnowflakeConnector] = None,\n   870    db_manager: Optional[DatabaseManager] = None,\n   871    batch_size: int = 500,\n   872) -> \"pd.DataFrame\":\n   873    \"\"\"\n   874    Batch lookup GP diagnosis-based indication groups for a DataFrame of patients.\n   875\n   876    This is the efficient batch version of get_directorate_from_diagnosis().\n   877    Instead of querying Snowflake per patient, it batches the lookups for performance.\n   878\n   879    Strategy:\n   880    1. Get all unique (PersonKey, Drug Name) pairs from DataFrame\n   881    2. For each unique drug, get all SNOMED codes from local SQLite\n   882    3. Build batched Snowflake queries to check GP records\n   883    4. Return indication_df mapping UPID  Indication_Group\n   884\n   885    For unmatched patients, Indication_Group will be their Directory (with suffix).\n   886\n   887    Args:\n   888        df: DataFrame with columns: UPID, Drug Name, Directory, PersonKey\n   889        connector: Optional SnowflakeConnector (defaults to singleton)\n   890        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n   891        batch_size: Number of patients per Snowflake query batch\n   892\n   893    Returns:\n   894        DataFrame with columns: UPID, Indication_Group, Source\n   895        - Indication_Group: Search_Term (if matched) or \"Directory (no GP dx)\" (if not)\n   896        - Source: \"DIAGNOSIS\" or \"FALLBACK\"\n   897    \"\"\"\n   898    import pandas as pd\n   899\n   900    if db_manager is None:\n   901        db_manager = default_db_manager\n   902\n   903    logger.info(f\"Starting batch indication lookup for {len(df)} records...\")\n   904\n   905    # Step 1: Get unique (UPID, Drug Name, PseudoNHSNoLinked, Directory) combinations\n   906    # We need PseudoNHSNoLinked to query Snowflake - this matches PatientPseudonym in GP records\n   907    # Note: PersonKey is LocalPatientID which is provider-specific and does NOT match GP records\n   908    if 'PseudoNHSNoLinked' not in df.columns:\n   909        logger.error(\"DataFrame missing 'PseudoNHSNoLinked' column - cannot lookup GP records\")\n   910        # Return fallback for all patients\n   911        result_df = df[['UPID', 'Directory']].drop_duplicates().copy()\n   912        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n   913        result_df['Source'] = \"FALLBACK\"\n   914        return result_df[['UPID', 'Indication_Group', 'Source']]\n   915\n   916    # Get unique patient-drug combinations (we need one lookup per patient-drug pair)\n   917    unique_pairs = df[['UPID', 'Drug Name', 'PseudoNHSNoLinked', 'Directory']].drop_duplicates()\n   918    logger.info(f\"Found {len(unique_pairs)} unique patient-drug combinations\")\n   919\n   920    # Step 2: Get all unique drugs and their SNOMED codes\n   921    unique_drugs = unique_pairs['Drug Name'].unique()\n   922    logger.info(f\"Building SNOMED lookup for {len(unique_drugs)} unique drugs...\")\n   923\n   924    # Build drug -> list of DrugSnomedMapping dict\n   925    drug_snomed_map: dict[str, list[DrugSnomedMapping]] = {}\n   926    all_snomed_codes: set[str] = set()\n   927    snomed_to_drug_searchterm: dict[str, list[tuple[str, str, str]]] = {}  # snomed -> [(drug, search_term, primary_dir), ...]\n   928\n   929    for drug_name in unique_drugs:\n   930        mappings = get_drug_snomed_codes(drug_name, db_manager)\n   931        drug_snomed_map[drug_name] = mappings\n   932\n   933        for m in mappings:\n   934            all_snomed_codes.add(m.snomed_code)\n   935            if m.snomed_code not in snomed_to_drug_searchterm:\n   936                snomed_to_drug_searchterm[m.snomed_code] = []\n   937            snomed_to_drug_searchterm[m.snomed_code].append(\n   938                (drug_name, m.search_term, m.primary_directorate)\n   939            )\n   940\n   941    logger.info(f\"Total SNOMED codes to check: {len(all_snomed_codes)}\")\n   942\n   943    # Step 3: Check Snowflake availability\n   944    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n   945        logger.warning(\"Snowflake not available - returning fallback for all patients\")\n   946        result_df = unique_pairs[['UPID', 'Directory']].copy()\n   947        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n   948        result_df['Source'] = \"FALLBACK\"\n   949        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n   950\n   951    if connector is None:\n   952        connector = get_connector()\n   953\n   954    # Step 4: Query GP records for all patients in batches\n   955    # The query finds the most recent matching SNOMED code for each patient\n   956\n   957    # Get unique PseudoNHSNoLinked values (each = one patient in GP records)\n   958    unique_patients = unique_pairs[['PseudoNHSNoLinked', 'UPID', 'Directory']].drop_duplicates(subset=['PseudoNHSNoLinked'])\n   959    patient_pseudonyms = unique_patients['PseudoNHSNoLinked'].tolist()\n   960\n   961    logger.info(f\"Querying GP records for {len(patient_pseudonyms)} unique patients in batches of {batch_size}...\")\n   962\n   963    # Results dict: PersonKey -> (snomed_code, event_date)\n   964    gp_matches: dict[str, tuple[str, Any]] = {}\n   965\n   966    # Convert SNOMED codes to list for query\n   967    snomed_list = list(all_snomed_codes)\n   968\n   969    if not snomed_list:\n   970        logger.warning(\"No SNOMED codes to check - returning fallback for all patients\")\n   971        result_df = unique_pairs[['UPID', 'Directory']].copy()\n   972        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n   973        result_df['Source'] = \"FALLBACK\"\n   974        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n   975\n   976    # Build SNOMED IN clause (reused across batches)\n   977    snomed_placeholders = \", \".join([\"%s\"] * len(snomed_list))\n   978\n   979    # Process patients in batches\n   980    for batch_start in range(0, len(patient_pseudonyms), batch_size):\n   981        batch_end = min(batch_start + batch_size, len(patient_pseudonyms))\n   982        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n   983\n   984        logger.info(f\"Batch {batch_start//batch_size + 1}: patients {batch_start} to {batch_end}\")\n   985\n   986        # Build patient IN clause\n   987        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n   988\n   989        # Query to find all matching SNOMED codes for these patients\n   990        # We'll get all matches and pick the most recent per patient in Python\n   991        query = f'''\n   992            SELECT\n   993                \"PatientPseudonym\",\n   994                \"SNOMEDCode\",\n   995                \"EventDateTime\"\n   996            FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n   997            WHERE \"PatientPseudonym\" IN ({patient_placeholders})\n   998              AND \"SNOMEDCode\" IN ({snomed_placeholders})\n   999            ORDER BY \"PatientPseudonym\", \"EventDateTime\" DESC\n  1000        '''\n  1001\n  1002        params = tuple(batch_pseudonyms) + tuple(snomed_list)\n  1003\n  1004        try:\n  1005            results = connector.execute_dict(query, params)\n  1006\n  1007            # Process results - pick most recent per patient\n  1008            for row in results:\n  1009                person_key = row.get(\"PatientPseudonym\")\n  1010                snomed_code = row.get(\"SNOMEDCode\")\n  1011                event_date = row.get(\"EventDateTime\")\n  1012\n  1013                if person_key and snomed_code:\n  1014                    # Keep only if we haven't seen this patient yet (first = most recent due to ORDER BY)\n  1015                    if person_key not in gp_matches:\n  1016                        gp_matches[person_key] = (snomed_code, event_date)\n  1017\n  1018        except Exception as e:\n  1019            logger.error(f\"Error querying GP records for batch: {e}\")\n  1020            # Continue with other batches\n  1021\n  1022    logger.info(f\"Found GP matches for {len(gp_matches)} patients\")\n  1023\n  1024    # Step 5: Build result DataFrame\n  1025    # For each unique_pair, determine Indication_Group based on match status\n  1026    results_list = []\n  1027\n  1028    # We need to dedupe by UPID - a patient might be on multiple drugs\n  1029    # Strategy: For each UPID, use the most recent match (if any)\n  1030    upid_to_match: dict[str, tuple[str, str]] = {}  # UPID -> (Indication_Group, Source)\n  1031\n  1032    for _, row in unique_pairs.iterrows():\n  1033        upid = row['UPID']\n  1034        drug_name = row['Drug Name']\n  1035        patient_pseudonym = row['PseudoNHSNoLinked']\n  1036        directory = row['Directory']\n  1037\n  1038        # Check if patient has GP match (using PseudoNHSNoLinked which matches PatientPseudonym in GP)\n  1039        if patient_pseudonym in gp_matches:\n  1040            matched_snomed, event_date = gp_matches[patient_pseudonym]\n  1041\n  1042            # Find the search_term for this SNOMED code and drug\n  1043            # (A SNOMED code might map to multiple drugs with different search_terms)\n  1044            if matched_snomed in snomed_to_drug_searchterm:\n  1045                # Look for match with current drug first\n  1046                search_term = None\n  1047                for drug, st, pd in snomed_to_drug_searchterm[matched_snomed]:\n  1048                    if drug.upper() == drug_name.upper():\n  1049                        search_term = st\n  1050                        break\n  1051                # If no drug-specific match, use any match\n  1052                if search_term is None:\n  1053                    search_term = snomed_to_drug_searchterm[matched_snomed][0][1]\n  1054\n  1055                # Only update if we don't have a match for this UPID yet\n  1056                if upid not in upid_to_match:\n  1057                    upid_to_match[upid] = (search_term, \"DIAGNOSIS\")\n  1058            else:\n  1059                # Shouldn't happen but fallback just in case\n  1060                if upid not in upid_to_match:\n  1061                    upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n  1062        else:\n  1063            # No GP match - use fallback\n  1064            if upid not in upid_to_match:\n  1065                upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n  1066\n  1067    # Build result DataFrame\n  1068    for upid, (indication_group, source) in upid_to_match.items():\n  1069        results_list.append({\n  1070            'UPID': upid,\n  1071            'Indication_Group': indication_group,\n  1072            'Source': source,\n  1073        })\n  1074\n  1075    result_df = pd.DataFrame(results_list)\n  1076\n  1077    # Log statistics\n  1078    diagnosis_count = len([s for s in result_df['Source'] if s == \"DIAGNOSIS\"])\n  1079    fallback_count = len([s for s in result_df['Source'] if s == \"FALLBACK\"])\n  1080    total = len(result_df)\n  1081\n  1082    logger.info(f\"Indication lookup complete:\")\n  1083    logger.info(f\"  Total unique patients: {total}\")\n  1084    logger.info(f\"  DIAGNOSIS matches: {diagnosis_count} ({100*diagnosis_count/total:.1f}%)\")\n  1085    logger.info(f\"  FALLBACK (no GP match): {fallback_count} ({100*fallback_count/total:.1f}%)\")\n  1086\n  1087    return result_df\n  1088\n  1089\n  1090# === NEW APPROACH: Query Snowflake directly using cluster CTE ===\n  1091\n  1092# The cluster query mapping (embedded from snomed_indication_mapping_query.sql)\n  1093# This maps Search_Term -> Cluster_ID for ~148 clinical conditions\n  1094CLUSTER_MAPPING_SQL = \"\"\"\n  1095WITH SearchTermClusters AS (\n  1096    SELECT Search_Term, Cluster_ID FROM (VALUES\n  1097        ('acute lymphoblastic leukaemia', 'HAEMCANMORPH_COD'),\n  1098        ('acute myeloid leukaemia', 'C19HAEMCAN_COD'),\n  1099        ('acute promyelocytic leukaemia', 'HAEMCANMORPH_COD'),\n  1100        ('allergic asthma', 'AST_COD'),\n  1101        ('allergic rhinitis', 'MILDINTAST_COD'),\n  1102        ('alzheimer''s disease', 'DEMALZ_COD'),\n  1103        ('amyloidosis', 'AMYLOID_COD'),\n  1104        ('anaemia', 'eFI2_AnaemiaTimeSensitive'),\n  1105        ('anaplastic large cell lymphoma', 'C19HAEMCAN_COD'),\n  1106        ('apixaban', 'DOACCON_COD'),\n  1107        ('aplastic anaemia', 'eFI2_AnaemiaEver'),\n  1108        ('arthritis', 'eFI2_InflammatoryArthritis'),\n  1109        ('asthma', 'eFI2_Asthma'),\n  1110        ('atopic dermatitis', 'ATOPDERM_COD'),\n  1111        ('atrial fibrillation', 'eFI2_AtrialFibrillation'),\n  1112        ('attention deficit hyperactivity disorder', 'ADHD_COD'),\n  1113        ('bipolar disorder', 'MH_COD'),\n  1114        ('bladder', 'eFI2_UrinaryIncontinence'),\n  1115        ('breast cancer', 'BRCANSCR_COD'),\n  1116        ('cardiomyopathy', 'eFI2_HarmfulDrinking'),\n  1117        ('cardiovascular disease', 'CVDRISKASS_COD'),\n  1118        ('cervical cancer', 'CSDEC_COD'),\n  1119        ('cholangiocarcinoma', 'eFI2_Cancer'),\n  1120        ('chronic kidney disease', 'CKD_COD'),\n  1121        ('chronic liver disease', 'eFI2_LiverProblems'),\n  1122        ('chronic lymphocytic leukaemia', 'EPPHAEMCAN_COD'),\n  1123        ('chronic myeloid leukaemia', 'EPPHAEMCAN_COD'),\n  1124        ('chronic obstructive pulmonary disease', 'eFI2_COPD'),\n  1125        ('colon cancer', 'eFI2_Cancer'),\n  1126        ('colorectal cancer', 'GICANREF_COD'),\n  1127        ('constipation', 'CHRONCONSTIP_COD'),\n  1128        ('covid-19', 'POSSPOSTCOVID_COD'),\n  1129        ('crohn''s disease', 'eFI2_InflammatoryBowelDisease'),\n  1130        ('cutaneous t-cell lymphoma', 'C19HAEMCAN_COD'),\n  1131        ('cystic fibrosis', 'CUST_ICB_CYSTIC_FIBROSIS'),\n  1132        ('deep vein thrombosis', 'VTE_COD'),\n  1133        ('depression', 'eFI2_Depression'),\n  1134        ('diabetes', 'eFI2_DiabetesEver'),\n  1135        ('diabetic retinopathy', 'DRSELIGIBILITY_COD'),\n  1136        ('diffuse large b-cell lymphoma', 'C19HAEMCAN_COD'),\n  1137        ('dravet syndrome', 'EPIL_COD'),\n  1138        ('drug misuse', 'ILLSUBINT_COD'),\n  1139        ('dyspepsia', 'eFI2_AbdominalPain'),\n  1140        ('epilepsy', 'eFI2_Seizures'),\n  1141        ('fallopian tube', 'STERIL_COD'),\n  1142        ('follicular lymphoma', 'C19HAEMCAN_COD'),\n  1143        ('gastric cancer', 'eFI2_Cancer'),\n  1144        ('giant cell arteritis', 'GCA_COD'),\n  1145        ('glioma', 'NHAEMCANMORPH_COD'),\n  1146        ('gout', 'eFI2_InflammatoryArthritis'),\n  1147        ('graft versus host disease', 'GVHD_COD'),\n  1148        ('granulomatosis with polyangiitis', 'WEGENERVASC_COD'),\n  1149        ('growth hormone deficiency', 'HYPOPITUITARY_COD'),\n  1150        ('hand eczema', 'ECZEMA_COD'),\n  1151        ('heart failure', 'eFI2_HeartFailure'),\n  1152        ('hepatitis b', 'HEPBCVAC_COD'),\n  1153        ('hepatocellular carcinoma', 'eFI2_Cancer'),\n  1154        ('hiv', 'PREFLANG_COD'),\n  1155        ('hodgkin lymphoma', 'HAEMCANMORPH_COD'),\n  1156        ('hormone receptor', 'eFI2_ThyroidProblems'),\n  1157        ('hypercholesterolaemia', 'CLASSFH_COD'),\n  1158        ('immune thrombocytopenia', 'ITP_COD'),\n  1159        ('influenza', 'FLUINVITE_COD'),\n  1160        ('insomnia', 'eFI2_SleepProblems'),\n  1161        ('irritable bowel syndrome', 'IBS_COD'),\n  1162        ('ischaemic stroke', 'OSTR_COD'),\n  1163        ('juvenile idiopathic arthritis', 'RARTHAD_COD'),\n  1164        ('kidney transplant', 'RENALTRANSP_COD'),\n  1165        ('leukaemia', 'eFI2_Cancer'),\n  1166        ('lung cancer', 'FTCANREF_COD'),\n  1167        ('lymphoma', 'C19HAEMCAN_COD'),\n  1168        ('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n  1169        ('macular oedema', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n  1170        ('major depressive episodes', 'eFI2_Depression'),\n  1171        ('malignant melanoma', 'eFI2_Cancer'),\n  1172        ('malignant pleural mesothelioma', 'LUNGCAN_COD'),\n  1173        ('manic episode', 'MH_COD'),\n  1174        ('mantle cell lymphoma', 'HAEMCANMORPH_COD'),\n  1175        ('melanoma', 'eFI2_Cancer'),\n  1176        ('merkel cell carcinoma', 'C19CAN_COD'),\n  1177        ('migraine', 'eFI2_Headache'),\n  1178        ('motor neurone disease', 'MND_COD'),\n  1179        ('multiple myeloma', 'C19HAEMCAN_COD'),\n  1180        ('multiple sclerosis', 'MS_COD'),\n  1181        ('myelodysplastic', 'eFI2_AnaemiaEver'),\n  1182        ('myelofibrosis', 'MDS_COD'),\n  1183        ('myocardial infarction', 'eFI2_IschaemicHeartDisease'),\n  1184        ('myotonia', 'CNDATRISK2_COD'),\n  1185        ('narcolepsy', 'LD_COD'),\n  1186        ('neuroendocrine tumour', 'LUNGCAN_COD'),\n  1187        ('non-small cell lung cancer', 'LUNGCAN_COD'),\n  1188        ('non-small-cell lung cancer', 'FTCANREF_COD'),\n  1189        ('obesity', 'BMI30_COD'),\n  1190        ('osteoarthritis', 'CUST_ICB_OSTEOARTHRITIS'),\n  1191        ('osteoporosis', 'eFI2_Osteoporosis'),\n  1192        ('osteosarcoma', 'NHAEMCANMORPH_COD'),\n  1193        ('ovarian cancer', 'C19CAN_COD'),\n  1194        ('peripheral arterial disease', 'PADEXC_COD'),\n  1195        ('plaque psoriasis', 'PSORIASIS_COD'),\n  1196        ('polycystic kidney disease', 'EPPCONGMALF_COD'),\n  1197        ('polycythaemia vera', 'C19HAEMCAN_COD'),\n  1198        ('pregnancy', 'C19PREG_COD'),\n  1199        ('primary biliary cholangitis', 'eFI2_LiverProblems'),\n  1200        ('primary hypercholesterolaemia', 'FNFHYP_COD'),\n  1201        ('prostate cancer', 'EPPSOLIDCAN_COD'),\n  1202        ('psoriasis', 'PSORIASIS_COD'),\n  1203        ('psoriatic arthritis', 'RARTHAD_COD'),\n  1204        ('pulmonary embolism', 'eFI2_RespiratoryDiseaseTimeSensitive'),\n  1205        ('pulmonary fibrosis', 'ILD_COD'),\n  1206        ('relapsing multiple sclerosis', 'MS_COD'),\n  1207        ('renal cell carcinoma', 'C19CAN_COD'),\n  1208        ('renal transplantation', 'RENALTRANSP_COD'),\n  1209        ('retinal vein occlusion', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n  1210        ('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),\n  1211        ('rivaroxaban', 'DOACCON_COD'),\n  1212        ('schizophrenia', 'MH_COD'),\n  1213        ('seizures', 'LSZFREQ_COD'),\n  1214        ('sepsis', 'C19ACTIVITY_COD'),\n  1215        ('severe persistent allergic asthma', 'SEVAST_COD'),\n  1216        ('sickle cell disease', 'SICKLE_COD'),\n  1217        ('sleep apnoea', 'CUST_ICB_NON_SEVERE_LDA'),\n  1218        ('smoking cessation', 'SMOKINGINT_COD'),\n  1219        ('soft tissue sarcoma', 'NHAEMCANMORPH_COD'),\n  1220        ('spinal muscular atrophy', 'MND_COD'),\n  1221        ('squamous cell', 'C19CAN_COD'),\n  1222        ('squamous cell carcinoma', 'C19CAN_COD'),\n  1223        ('stem cell transplant', 'ALLOTRANSP_COD'),\n  1224        ('stroke', 'eFI2_Stroke'),\n  1225        ('systemic lupus erythematosus', 'SLUPUS_COD'),\n  1226        ('systemic mastocytosis', 'HAEMCANMORPH_COD'),\n  1227        ('thrombocytopenic purpura', 'TTP_COD'),\n  1228        ('thrombotic thrombocytopenic purpura', 'TTP_COD'),\n  1229        ('thyroid cancer', 'C19CAN_COD'),\n  1230        ('tophaceous gout', 'CUST_ICB_OSTEOARTHRITIS'),\n  1231        ('transitional cell carcinoma', 'C19CAN_COD'),\n  1232        ('type 1 diabetes', 'DMTYPE1_COD'),\n  1233        ('type 2 diabetes', 'DMTYPE2_COD'),\n  1234        ('ulcerative colitis', 'eFI2_InflammatoryBowelDisease'),\n  1235        ('urothelial carcinoma', 'NHAEMCANMORPH_COD'),\n  1236        ('urticaria', 'XSAL_COD'),\n  1237        ('uveitis', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n  1238        ('vascular disease', 'CVDINVITE_COD'),\n  1239        ('vasculitis', 'CRYOGLOBVASC_COD')\n  1240    ) AS t(Search_Term, Cluster_ID)\n  1241),\n  1242\n  1243ClusterCodes AS (\n  1244    SELECT\n  1245        stc.Search_Term,\n  1246        c.\"SNOMEDCode\",\n  1247        c.\"SNOMEDDescription\"\n  1248    FROM SearchTermClusters stc\n  1249    JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" c\n  1250        ON stc.Cluster_ID = c.\"Cluster_ID\"\n  1251    WHERE c.\"SNOMEDCode\" IS NOT NULL\n  1252),\n  1253\n  1254ExplicitCodes AS (\n  1255    SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES\n  1256        ('acute coronary syndrome', '837091000000100', 'Manual mapping'),\n  1257        ('ankylosing spondylitis', '162930007', 'Manual mapping'),\n  1258        ('ankylosing spondylitis', '239805001', 'Manual mapping'),\n  1259        ('ankylosing spondylitis', '239810002', 'Manual mapping'),\n  1260        ('ankylosing spondylitis', '239811003', 'Manual mapping'),\n  1261        ('ankylosing spondylitis', '394990003', 'Manual mapping'),\n  1262        ('ankylosing spondylitis', '429712009', 'Manual mapping'),\n  1263        ('ankylosing spondylitis', '441562009', 'Manual mapping'),\n  1264        ('ankylosing spondylitis', '441680005', 'Manual mapping'),\n  1265        ('ankylosing spondylitis', '441930001', 'Manual mapping'),\n  1266        ('axial spondyloarthritis', '723116002', 'Manual mapping'),\n  1267        ('choroidal neovascularisation', '380621000000102', 'Manual mapping'),\n  1268        ('choroidal neovascularisation', '733124000', 'Manual mapping')\n  1269    ) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)\n  1270),\n  1271\n  1272AllIndicationCodes AS (\n  1273    SELECT Search_Term, \"SNOMEDCode\" AS SNOMEDCode, \"SNOMEDDescription\" AS SNOMEDDescription\n  1274    FROM ClusterCodes\n  1275    UNION ALL\n  1276    SELECT Search_Term, SNOMEDCode, SNOMEDDescription\n  1277    FROM ExplicitCodes\n  1278)\n  1279\"\"\"\n  1280\n  1281\n  1282def get_patient_indication_groups(\n  1283    patient_pseudonyms: list[str],\n  1284    connector: Optional[SnowflakeConnector] = None,\n  1285    batch_size: int = 500,\n  1286) -> \"pd.DataFrame\":\n  1287    \"\"\"\n  1288    Batch lookup GP diagnosis-based indication groups using Snowflake cluster query.\n  1289\n  1290    This function queries Snowflake directly using the embedded cluster CTE\n  1291    (from snomed_indication_mapping_query.sql) to find patients with matching\n  1292    GP diagnoses. This is the NEW approach replacing the old SQLite-based lookup.\n  1293\n  1294    The query:\n  1295    1. Uses the cluster mapping CTE to get all Search_Term -> SNOMED code mappings\n  1296    2. Joins with PrimaryCareClinicalCoding to find patients with matching codes\n  1297    3. Returns the most recent match per patient (by EventDateTime)\n  1298\n  1299    Args:\n  1300        patient_pseudonyms: List of PseudoNHSNoLinked values (matches PatientPseudonym in GP records)\n  1301        connector: Optional SnowflakeConnector (defaults to singleton)\n  1302        batch_size: Number of patients per Snowflake query batch (default 500)\n  1303\n  1304    Returns:\n  1305        DataFrame with columns:\n  1306        - PatientPseudonym: The patient identifier (PseudoNHSNoLinked value)\n  1307        - Search_Term: The matched indication (e.g., \"rheumatoid arthritis\")\n  1308        - EventDateTime: Date of the GP diagnosis record\n  1309\n  1310        Patients not found in results have no matching GP diagnosis.\n  1311    \"\"\"\n  1312    import pandas as pd\n  1313\n  1314    logger.info(f\"Starting Snowflake-direct indication lookup for {len(patient_pseudonyms)} patients...\")\n  1315\n  1316    # Handle edge case: empty patient list\n  1317    if not patient_pseudonyms:\n  1318        logger.warning(\"Empty patient list provided\")\n  1319        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n  1320\n  1321    # Check Snowflake availability\n  1322    if not SNOWFLAKE_AVAILABLE:\n  1323        logger.error(\"Snowflake connector not available - cannot lookup GP records\")\n  1324        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n  1325\n  1326    if not is_snowflake_configured():\n  1327        logger.error(\"Snowflake not configured - cannot lookup GP records\")\n  1328        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n  1329\n  1330    if connector is None:\n  1331        connector = get_connector()\n  1332\n  1333    # Results list to collect all matches\n  1334    all_results: list[dict] = []\n  1335\n  1336    # Process patients in batches\n  1337    total_patients = len(patient_pseudonyms)\n  1338    for batch_start in range(0, total_patients, batch_size):\n  1339        batch_end = min(batch_start + batch_size, total_patients)\n  1340        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n  1341        batch_num = batch_start // batch_size + 1\n  1342        total_batches = (total_patients + batch_size - 1) // batch_size\n  1343\n  1344        logger.info(f\"Batch {batch_num}/{total_batches}: patients {batch_start + 1} to {batch_end}\")\n  1345\n  1346        # Build patient IN clause placeholders\n  1347        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n  1348\n  1349        # Build the full query with cluster CTE\n  1350        # This finds the most recent matching diagnosis for each patient\n  1351        # Note: Column names must be aliased to ensure consistent casing in results\n  1352        query = f\"\"\"\n  1353{CLUSTER_MAPPING_SQL}\n  1354SELECT\n  1355    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n  1356    aic.Search_Term AS \"Search_Term\",\n  1357    pc.\"EventDateTime\" AS \"EventDateTime\"\n  1358FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\n  1359INNER JOIN AllIndicationCodes aic\n  1360    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\n  1361WHERE pc.\"PatientPseudonym\" IN ({patient_placeholders})\n  1362QUALIFY ROW_NUMBER() OVER (\n  1363    PARTITION BY pc.\"PatientPseudonym\"\n  1364    ORDER BY pc.\"EventDateTime\" DESC\n  1365) = 1\n  1366\"\"\"\n  1367\n  1368        try:\n  1369            results = connector.execute_dict(query, tuple(batch_pseudonyms))\n  1370\n  1371            for row in results:\n  1372                all_results.append({\n  1373                    'PatientPseudonym': row.get('PatientPseudonym'),\n  1374                    'Search_Term': row.get('Search_Term'),\n  1375                    'EventDateTime': row.get('EventDateTime'),\n  1376                })\n  1377\n  1378            logger.debug(f\"Batch {batch_num}: found {len(results)} matches\")\n  1379\n  1380        except Exception as e:\n  1381            logger.error(f\"Error querying GP records for batch {batch_num}: {e}\")\n  1382            # Continue with other batches - partial results are better than none\n  1383\n  1384    # Build result DataFrame\n  1385    result_df = pd.DataFrame(all_results)\n  1386\n  1387    # Log summary statistics\n  1388    if len(result_df) > 0:\n  1389        matched_count = len(result_df)\n  1390        match_rate = 100 * matched_count / total_patients\n  1391        unique_terms = result_df['Search_Term'].nunique()\n  1392        logger.info(f\"Indication lookup complete:\")\n  1393        logger.info(f\"  Total patients queried: {total_patients}\")\n  1394        logger.info(f\"  Patients with GP match: {matched_count} ({match_rate:.1f}%)\")\n  1395        logger.info(f\"  Unique Search_Terms found: {unique_terms}\")\n  1396\n  1397        # Log top Search_Terms\n  1398        top_terms = result_df['Search_Term'].value_counts().head(5)\n  1399        logger.info(f\"  Top 5 indications: {dict(top_terms)}\")\n  1400    else:\n  1401        logger.info(f\"Indication lookup complete: 0 matches from {total_patients} patients\")\n  1402\n  1403    return result_df\n  1404\n  1405\n  1406# Export public API\n  1407__all__ = [\n  1408    # Dataclasses\n  1409    \"ClusterSnomedCodes\",\n  1410    \"IndicationValidationResult\",\n  1411    \"DrugIndicationMatchRate\",\n  1412    \"DrugSnomedMapping\",\n  1413    \"DirectSnomedMatchResult\",\n  1414    \"DirectorateAssignment\",\n  1415    # Cluster-based lookup functions (existing)\n  1416    \"get_drug_clusters\",\n  1417    \"get_drug_cluster_ids\",\n  1418    \"get_cluster_snomed_codes\",\n  1419    \"patient_has_indication\",\n  1420    \"validate_indication\",\n  1421    \"get_indication_match_rate\",\n  1422    \"batch_validate_indications\",\n  1423    \"get_available_clusters\",\n  1424    # Direct SNOMED lookup functions (new)\n  1425    \"get_drug_snomed_codes\",\n  1426    \"patient_has_indication_direct\",\n  1427    # Diagnosis-based directorate assignment\n  1428    \"get_directorate_from_diagnosis\",\n  1429    # Batch lookup for indication groups\n  1430    \"batch_lookup_indication_groups\",\n  1431    # Snowflake-direct indication lookup (new approach)\n  1432    \"get_patient_indication_groups\",\n  1433    \"CLUSTER_MAPPING_SQL\",\n  1434]\n  1435\n\n<system-reminder>\nWhenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.\n</system-reminder>\n"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"2e510dd2-b916-4227-8b1b-9a9f48a95180","tool_use_result":{"type":"text","file":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py","content":"\"\"\"\nDiagnosis lookup module for NHS Patient Pathway Analysis.\n\nProvides functions to validate patient indications by checking GP diagnosis records\nagainst SNOMED cluster codes. Uses the drug-to-cluster mapping from\ndrug_indication_clusters.csv and queries Snowflake for SNOMED codes and GP records.\n\nKey workflow:\n1. Get drug's valid indication clusters from local mapping\n2. Get all SNOMED codes for those clusters from Snowflake\n3. Check if patient has any of those SNOMED codes in GP records\n4. Report indication validation status\n\nIMPORTANT: HCD activity data indication codes are UNRELIABLE. This module uses\nGP/Primary Care data (PrimaryCareClinicalCoding) as the authoritative source.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom datetime import date, datetime\nfrom pathlib import Path\nfrom typing import Optional, Callable, Any, cast, TYPE_CHECKING\nimport csv\n\nif TYPE_CHECKING:\n    import pandas as pd\n\nfrom core.logging_config import get_logger\nfrom data_processing.database import DatabaseManager, default_db_manager\nfrom data_processing.snowflake_connector import (\n    SnowflakeConnector,\n    get_connector,\n    is_snowflake_available,\n    is_snowflake_configured,\n    SNOWFLAKE_AVAILABLE,\n)\nfrom data_processing.cache import get_cache, is_cache_enabled\n\nlogger = get_logger(__name__)\n\n\n@dataclass\nclass ClusterSnomedCodes:\n    \"\"\"SNOMED codes for a clinical coding cluster.\"\"\"\n    cluster_id: str\n    cluster_description: str\n    snomed_codes: list[str] = field(default_factory=list)\n    snomed_descriptions: dict[str, str] = field(default_factory=dict)\n\n    @property\n    def code_count(self) -> int:\n        return len(self.snomed_codes)\n\n\n@dataclass\nclass IndicationValidationResult:\n    \"\"\"Result of validating a patient's indication for a drug.\"\"\"\n    patient_pseudonym: str\n    drug_name: str\n    has_valid_indication: bool\n    matched_cluster_id: Optional[str] = None\n    matched_snomed_code: Optional[str] = None\n    matched_snomed_description: Optional[str] = None\n    checked_clusters: list[str] = field(default_factory=list)\n    total_codes_checked: int = 0\n    source: str = \"GP_SNOMED\"  # GP_SNOMED | NONE\n    error_message: Optional[str] = None\n\n\n@dataclass\nclass DrugIndicationMatchRate:\n    \"\"\"Match rate statistics for a drug's indication validation.\"\"\"\n    drug_name: str\n    total_patients: int\n    patients_with_indication: int\n    patients_without_indication: int\n    match_rate: float  # 0.0 to 1.0\n    clusters_checked: list[str] = field(default_factory=list)\n    sample_unmatched: list[str] = field(default_factory=list)  # Sample patient IDs\n\n\n@dataclass\nclass DrugSnomedMapping:\n    \"\"\"SNOMED code mapping for a drug from ref_drug_snomed_mapping.\"\"\"\n    snomed_code: str\n    snomed_description: str\n    search_term: str\n    primary_directorate: str\n    indication: str = \"\"\n    ta_id: str = \"\"\n\n\n@dataclass\nclass DirectSnomedMatchResult:\n    \"\"\"Result of direct SNOMED code lookup in GP records.\"\"\"\n    patient_pseudonym: str\n    matched: bool\n    snomed_code: Optional[str] = None\n    snomed_description: Optional[str] = None\n    search_term: Optional[str] = None\n    primary_directorate: Optional[str] = None\n    event_date: Optional[datetime] = None\n    source: str = \"DIRECT_SNOMED\"  # DIRECT_SNOMED | NONE\n\n\n@dataclass\nclass DirectorateAssignment:\n    \"\"\"Result of directorate assignment for a patient-drug combination.\"\"\"\n    upid: str\n    drug_name: str\n    directorate: Optional[str]\n    search_term: Optional[str] = None\n    source: str = \"FALLBACK\"  # DIAGNOSIS | FALLBACK\n    snomed_code: Optional[str] = None\n    event_date: Optional[datetime] = None\n\n\ndef get_drug_clusters(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[dict]:\n    \"\"\"\n    Get all SNOMED cluster mappings for a drug from local SQLite.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of dicts with keys: drug_name, indication, cluster_id,\n        cluster_description, nice_ta_reference\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT drug_name, indication, cluster_id, cluster_description, nice_ta_reference\n        FROM ref_drug_indication_clusters\n        WHERE UPPER(drug_name) = UPPER(?)\n        ORDER BY indication, cluster_id\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name,))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append({\n                    \"drug_name\": row[\"drug_name\"],\n                    \"indication\": row[\"indication\"],\n                    \"cluster_id\": row[\"cluster_id\"],\n                    \"cluster_description\": row[\"cluster_description\"],\n                    \"nice_ta_reference\": row[\"nice_ta_reference\"],\n                })\n\n            logger.debug(f\"Found {len(results)} cluster mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting clusters for drug '{drug_name}': {e}\")\n        return []\n\n\ndef get_drug_cluster_ids(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[str]:\n    \"\"\"\n    Get unique cluster IDs for a drug.\n\n    Args:\n        drug_name: Drug name to look up\n        db_manager: Optional DatabaseManager\n\n    Returns:\n        List of unique cluster IDs\n    \"\"\"\n    clusters = get_drug_clusters(drug_name, db_manager)\n    return list(set(c[\"cluster_id\"] for c in clusters))\n\n\ndef get_drug_snomed_codes(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[DrugSnomedMapping]:\n    \"\"\"\n    Get all SNOMED codes for a drug from local ref_drug_snomed_mapping table.\n\n    This uses the enriched mapping CSV data loaded into SQLite, which provides\n    direct SNOMED-to-drug mappings with Search_Term and PrimaryDirectorate.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive, matches cleaned_drug_name)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of DrugSnomedMapping with snomed_code, snomed_description,\n        search_term, primary_directorate, indication, ta_id\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT DISTINCT\n            snomed_code,\n            snomed_description,\n            search_term,\n            primary_directorate,\n            indication,\n            ta_id\n        FROM ref_drug_snomed_mapping\n        WHERE UPPER(cleaned_drug_name) = UPPER(?)\n           OR UPPER(drug_name) = UPPER(?)\n        ORDER BY search_term, snomed_code\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name, drug_name))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append(DrugSnomedMapping(\n                    snomed_code=row[\"snomed_code\"],\n                    snomed_description=row[\"snomed_description\"] or \"\",\n                    search_term=row[\"search_term\"] or \"\",\n                    primary_directorate=row[\"primary_directorate\"] or \"\",\n                    indication=row[\"indication\"] or \"\",\n                    ta_id=row[\"ta_id\"] or \"\",\n                ))\n\n            logger.debug(f\"Found {len(results)} SNOMED mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for drug '{drug_name}': {e}\")\n        return []\n\n\ndef patient_has_indication_direct(\n    patient_pseudonym: str,\n    drug_snomed_mappings: list[DrugSnomedMapping],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> DirectSnomedMatchResult:\n    \"\"\"\n    Check if patient has any of the SNOMED codes in their GP records.\n\n    This is the direct SNOMED lookup - it queries PrimaryCareClinicalCoding\n    for exact SNOMED code matches (not via cluster). Returns the most recent\n    match by EventDateTime if multiple matches exist.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_snomed_mappings: List of DrugSnomedMapping from get_drug_snomed_codes()\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectSnomedMatchResult with match details (most recent by EventDateTime)\n    \"\"\"\n    result = DirectSnomedMatchResult(\n        patient_pseudonym=patient_pseudonym,\n        matched=False,\n        source=\"NONE\",\n    )\n\n    if not drug_snomed_mappings:\n        return result\n\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot check GP records\")\n        return result\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build lookup dict for mapping snomed_code -> (search_term, primary_directorate, snomed_description)\n    snomed_lookup = {\n        m.snomed_code: (m.search_term, m.primary_directorate, m.snomed_description)\n        for m in drug_snomed_mappings\n    }\n\n    # Get unique SNOMED codes\n    snomed_codes = list(snomed_lookup.keys())\n\n    # Build placeholders for SNOMED codes\n    placeholders = \", \".join([\"%s\"] * len(snomed_codes))\n\n    # Query to find most recent matching SNOMED code in GP records\n    query = f'''\n        SELECT\n            \"SNOMEDCode\",\n            \"EventDateTime\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n        WHERE \"PatientPseudonym\" = %s\n            AND \"SNOMEDCode\" IN ({placeholders})\n    '''\n\n    params: list = [patient_pseudonym] + snomed_codes\n\n    if before_date:\n        query += ' AND \"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' ORDER BY \"EventDateTime\" DESC LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            matched_code = row.get(\"SNOMEDCode\")\n            event_dt = row.get(\"EventDateTime\")\n\n            if matched_code and matched_code in snomed_lookup:\n                search_term, primary_dir, snomed_desc = snomed_lookup[matched_code]\n\n                return DirectSnomedMatchResult(\n                    patient_pseudonym=patient_pseudonym,\n                    matched=True,\n                    snomed_code=matched_code,\n                    snomed_description=snomed_desc,\n                    search_term=search_term,\n                    primary_directorate=primary_dir,\n                    event_date=event_dt,\n                    source=\"DIRECT_SNOMED\",\n                )\n\n        return result\n\n    except Exception as e:\n        logger.error(f\"Error checking direct SNOMED for patient '{patient_pseudonym}': {e}\")\n        return result\n\n\ndef get_directorate_from_diagnosis(\n    upid: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> DirectorateAssignment:\n    \"\"\"\n    Get directorate assignment for a patient-drug combination using diagnosis-based lookup.\n\n    This function attempts to assign a directorate based on the patient's GP records\n    (direct SNOMED code matching). If no match is found, it returns a FALLBACK result\n    indicating that the caller should use alternative assignment methods (e.g.,\n    department_identification() from tools/data.py).\n\n    Workflow:\n    1. Get all SNOMED codes for the drug from ref_drug_snomed_mapping\n    2. Query patient's GP records for matching SNOMED codes\n    3. If match found  return diagnosis-based directorate and search_term\n    4. If no match  return FALLBACK result (caller handles fallback logic)\n\n    Args:\n        upid: Patient's unique patient ID (Provider Code[:3] + PersonKey)\n        drug_name: Drug name to look up\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectorateAssignment with directorate, search_term, and source\n    \"\"\"\n    result = DirectorateAssignment(\n        upid=upid,\n        drug_name=drug_name,\n        directorate=None,\n        source=\"FALLBACK\",\n    )\n\n    # Step 1: Get SNOMED codes for the drug\n    drug_snomed_mappings = get_drug_snomed_codes(drug_name, db_manager)\n\n    if not drug_snomed_mappings:\n        logger.debug(f\"No SNOMED mappings found for drug '{drug_name}' - using fallback\")\n        return result\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.debug(\"Snowflake not available - using fallback\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.debug(\"Snowflake not configured - using fallback\")\n        return result\n\n    # Step 3: Get patient pseudonym from UPID\n    # UPID format is Provider Code (3 chars) + PersonKey\n    # We need to query Snowflake to get the PatientPseudonym for this PersonKey\n    # However, patient_has_indication_direct expects PatientPseudonym, not UPID\n    # For now, we'll use UPID as the identifier - the actual integration\n    # will need to happen at the DataFrame level where we have PersonKey\n    #\n    # NOTE: This function will be called from the pipeline where we have\n    # access to PatientPseudonym. The UPID is passed for logging/tracking.\n\n    # Actually, looking at the pipeline, we need PatientPseudonym, not UPID.\n    # The caller should pass the PatientPseudonym or we need to look it up.\n    # For now, let's assume the caller will use this in a batch context\n    # where they can map UPID -> PatientPseudonym.\n\n    # Let me reconsider: the function signature takes UPID but we need\n    # PatientPseudonym for Snowflake. In the pipeline context (fetch_and_transform_data),\n    # we'll have the PersonKey column which IS the PatientPseudonym.\n    # So UPID = ProviderCode[:3] + PersonKey, and PersonKey = PatientPseudonym.\n    #\n    # We can extract PatientPseudonym from UPID by removing the first 3 chars.\n    patient_pseudonym = upid[3:] if len(upid) > 3 else upid\n\n    # Step 4: Check patient's GP records for matching SNOMED codes\n    match_result = patient_has_indication_direct(\n        patient_pseudonym=patient_pseudonym,\n        drug_snomed_mappings=drug_snomed_mappings,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    if match_result.matched and match_result.primary_directorate:\n        return DirectorateAssignment(\n            upid=upid,\n            drug_name=drug_name,\n            directorate=match_result.primary_directorate,\n            search_term=match_result.search_term,\n            source=\"DIAGNOSIS\",\n            snomed_code=match_result.snomed_code,\n            event_date=match_result.event_date,\n        )\n\n    # No match found - return fallback result\n    return result\n\n\ndef get_cluster_snomed_codes(\n    cluster_id: str,\n    connector: Optional[SnowflakeConnector] = None,\n    use_cache: bool = True,\n) -> ClusterSnomedCodes:\n    \"\"\"\n    Get all SNOMED codes for a cluster from Snowflake.\n\n    Queries the ClinicalCodingClusterSnomedCodes table to get all SNOMED codes\n    that belong to the specified cluster.\n\n    Args:\n        cluster_id: Cluster ID to look up (e.g., 'RARTH_COD', 'PSORIASIS_COD')\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        use_cache: Whether to use cached results (default True)\n\n    Returns:\n        ClusterSnomedCodes with list of SNOMED codes and descriptions\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot get cluster codes\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    # Check cache first\n    cache_key = f\"cluster_snomed_{cluster_id}\"\n    if use_cache and is_cache_enabled():\n        cache = get_cache()\n        cached = cache.get(cache_key)\n        if cached is not None and len(cached) > 0:\n            logger.debug(f\"Using cached SNOMED codes for cluster '{cluster_id}'\")\n            cached_dict = cached[0]  # First element is our data dict\n            return ClusterSnomedCodes(\n                cluster_id=cluster_id,\n                cluster_description=str(cached_dict.get(\"description\", \"\")),\n                snomed_codes=list(cached_dict.get(\"codes\", [])),\n                snomed_descriptions=dict(cached_dict.get(\"descriptions\", {})),\n            )\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT DISTINCT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            \"SNOMEDCode\",\n            \"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        WHERE \"Cluster_ID\" = %s\n        ORDER BY \"SNOMEDCode\"\n    '''\n\n    try:\n        results = connector.execute_dict(query, (cluster_id,))\n\n        if not results:\n            logger.warning(f\"No SNOMED codes found for cluster '{cluster_id}'\")\n            return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n        codes = []\n        descriptions = {}\n        description = results[0].get(\"Cluster_Description\", \"\") if results else \"\"\n\n        for row in results:\n            code = row.get(\"SNOMEDCode\")\n            if code:\n                codes.append(code)\n                descriptions[code] = row.get(\"SNOMEDDescription\", \"\")\n\n        logger.info(f\"Found {len(codes)} SNOMED codes for cluster '{cluster_id}'\")\n\n        # Cache the results (using query-based cache with fake params)\n        if use_cache and is_cache_enabled():\n            cache = get_cache()\n            cache_data = [{\n                \"description\": description,\n                \"codes\": codes,\n                \"descriptions\": descriptions,\n            }]\n            cache.set(cache_key, None, cache_data)  # type: ignore[arg-type]\n\n        return ClusterSnomedCodes(\n            cluster_id=cluster_id,\n            cluster_description=description,\n            snomed_codes=codes,\n            snomed_descriptions=descriptions,\n        )\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for cluster '{cluster_id}': {e}\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n\ndef patient_has_indication(\n    patient_pseudonym: str,\n    cluster_ids: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> tuple[bool, Optional[str], Optional[str], Optional[str]]:\n    \"\"\"\n    Check if a patient has any SNOMED codes from the specified clusters in GP records.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        cluster_ids: List of cluster IDs to check against\n        connector: Optional SnowflakeConnector\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        Tuple of (has_indication, matched_cluster_id, matched_snomed_code, matched_description)\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        return False, None, None, None\n\n    if not cluster_ids:\n        return False, None, None, None\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build placeholders for cluster IDs\n    placeholders = \", \".join([\"%s\"] * len(cluster_ids))\n\n    # Query to check if patient has any matching SNOMED code\n    query = f'''\n        SELECT\n            pc.\"SNOMEDCode\",\n            cc.\"Cluster_ID\",\n            cc.\"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\n        INNER JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" cc\n            ON pc.\"SNOMEDCode\" = cc.\"SNOMEDCode\"\n        WHERE pc.\"PatientPseudonym\" = %s\n            AND cc.\"Cluster_ID\" IN ({placeholders})\n    '''\n\n    params = [patient_pseudonym] + cluster_ids\n\n    if before_date:\n        query += ' AND pc.\"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            return (\n                True,\n                row.get(\"Cluster_ID\"),\n                row.get(\"SNOMEDCode\"),\n                row.get(\"SNOMEDDescription\"),\n            )\n\n        return False, None, None, None\n\n    except Exception as e:\n        logger.error(f\"Error checking indication for patient '{patient_pseudonym}': {e}\")\n        return False, None, None, None\n\n\ndef validate_indication(\n    patient_pseudonym: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> IndicationValidationResult:\n    \"\"\"\n    Validate that a patient has an appropriate indication for a drug.\n\n    Full validation workflow:\n    1. Get drug's valid indication clusters from local mapping\n    2. Check if patient has any matching SNOMED codes in GP records\n    3. Return detailed validation result\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_name: Drug name to validate indication for\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        IndicationValidationResult with validation details\n    \"\"\"\n    result = IndicationValidationResult(\n        patient_pseudonym=patient_pseudonym,\n        drug_name=drug_name,\n        has_valid_indication=False,\n    )\n\n    # Step 1: Get drug's cluster mappings\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    if not cluster_ids:\n        result.error_message = f\"No cluster mappings found for drug '{drug_name}'\"\n        result.source = \"NONE\"\n        return result\n\n    result.checked_clusters = cluster_ids\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        result.error_message = \"Snowflake connector not installed\"\n        result.source = \"NONE\"\n        return result\n\n    if not is_snowflake_configured():\n        result.error_message = \"Snowflake not configured\"\n        result.source = \"NONE\"\n        return result\n\n    # Step 3: Check patient GP records\n    has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n        patient_pseudonym=patient_pseudonym,\n        cluster_ids=cluster_ids,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    result.has_valid_indication = has_indication\n    result.matched_cluster_id = matched_cluster\n    result.matched_snomed_code = matched_code\n    result.matched_snomed_description = matched_desc\n    result.source = \"GP_SNOMED\" if has_indication else \"NONE\"\n\n    return result\n\n\ndef get_indication_match_rate(\n    drug_name: str,\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    sample_unmatched_count: int = 10,\n) -> DrugIndicationMatchRate:\n    \"\"\"\n    Calculate indication match rate for a drug across a list of patients.\n\n    Args:\n        drug_name: Drug name to check\n        patient_pseudonyms: List of patient pseudonymised NHS numbers\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        sample_unmatched_count: Number of unmatched patient IDs to include in sample\n\n    Returns:\n        DrugIndicationMatchRate with match statistics\n    \"\"\"\n    if connector is None and SNOWFLAKE_AVAILABLE and is_snowflake_configured():\n        connector = get_connector()\n\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    total = len(patient_pseudonyms)\n    matched = 0\n    unmatched = 0\n    sample_unmatched: list[str] = []\n\n    if not cluster_ids:\n        logger.warning(f\"No cluster mappings for drug '{drug_name}' - all patients will be unmatched\")\n        return DrugIndicationMatchRate(\n            drug_name=drug_name,\n            total_patients=total,\n            patients_with_indication=0,\n            patients_without_indication=total,\n            match_rate=0.0,\n            clusters_checked=[],\n            sample_unmatched=patient_pseudonyms[:sample_unmatched_count],\n        )\n\n    for i, pseudonym in enumerate(patient_pseudonyms):\n        if i > 0 and i % 100 == 0:\n            logger.info(f\"Validating indications: {i}/{total} ({100*i/total:.1f}%)\")\n\n        has_indication, _, _, _ = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        if has_indication:\n            matched += 1\n        else:\n            unmatched += 1\n            if len(sample_unmatched) < sample_unmatched_count:\n                sample_unmatched.append(pseudonym)\n\n    match_rate = matched / total if total > 0 else 0.0\n\n    logger.info(f\"Indication match rate for '{drug_name}': {100*match_rate:.1f}% ({matched}/{total})\")\n\n    return DrugIndicationMatchRate(\n        drug_name=drug_name,\n        total_patients=total,\n        patients_with_indication=matched,\n        patients_without_indication=unmatched,\n        match_rate=match_rate,\n        clusters_checked=cluster_ids,\n        sample_unmatched=sample_unmatched,\n    )\n\n\ndef batch_validate_indications(\n    patient_drug_pairs: list[tuple[str, str]],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    progress_callback: Optional[Callable[[int, int], None]] = None,\n) -> list[IndicationValidationResult]:\n    \"\"\"\n    Validate indications for multiple patient-drug pairs efficiently.\n\n    Args:\n        patient_drug_pairs: List of (patient_pseudonym, drug_name) tuples\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        progress_callback: Optional callback(current, total) for progress updates\n\n    Returns:\n        List of IndicationValidationResult for each pair\n    \"\"\"\n    results = []\n    total = len(patient_drug_pairs)\n\n    # Cache cluster lookups by drug\n    drug_clusters_cache = {}\n\n    for i, (pseudonym, drug_name) in enumerate(patient_drug_pairs):\n        if progress_callback:\n            progress_callback(i + 1, total)\n\n        # Get clusters from cache or lookup\n        drug_upper = drug_name.upper()\n        if drug_upper not in drug_clusters_cache:\n            drug_clusters_cache[drug_upper] = get_drug_cluster_ids(drug_name, db_manager)\n\n        cluster_ids = drug_clusters_cache[drug_upper]\n\n        if not cluster_ids:\n            results.append(IndicationValidationResult(\n                patient_pseudonym=pseudonym,\n                drug_name=drug_name,\n                has_valid_indication=False,\n                source=\"NONE\",\n                error_message=f\"No cluster mappings for drug '{drug_name}'\",\n            ))\n            continue\n\n        # Check patient indication\n        has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        results.append(IndicationValidationResult(\n            patient_pseudonym=pseudonym,\n            drug_name=drug_name,\n            has_valid_indication=has_indication,\n            matched_cluster_id=matched_cluster,\n            matched_snomed_code=matched_code,\n            matched_snomed_description=matched_desc,\n            checked_clusters=cluster_ids,\n            source=\"GP_SNOMED\" if has_indication else \"NONE\",\n        ))\n\n    matched_count = sum(1 for r in results if r.has_valid_indication)\n    logger.info(f\"Batch validation complete: {matched_count}/{total} ({100*matched_count/total:.1f}%) with valid indications\")\n\n    return results\n\n\ndef get_available_clusters(\n    connector: Optional[SnowflakeConnector] = None,\n) -> list[dict]:\n    \"\"\"\n    Get list of all available SNOMED clusters from Snowflake.\n\n    Returns:\n        List of dicts with cluster_id, cluster_description, code_count\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - cannot list clusters\")\n        return []\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            COUNT(DISTINCT \"SNOMEDCode\") as code_count\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        GROUP BY \"Cluster_ID\", \"Cluster_Description\"\n        ORDER BY \"Cluster_ID\"\n    '''\n\n    try:\n        results = connector.execute_dict(query)\n\n        clusters = []\n        for row in results:\n            clusters.append({\n                \"cluster_id\": row.get(\"Cluster_ID\"),\n                \"cluster_description\": row.get(\"Cluster_Description\"),\n                \"code_count\": row.get(\"code_count\", 0),\n            })\n\n        logger.info(f\"Found {len(clusters)} available SNOMED clusters\")\n        return clusters\n\n    except Exception as e:\n        logger.error(f\"Error getting available clusters: {e}\")\n        return []\n\n\ndef batch_lookup_indication_groups(\n    df: \"pd.DataFrame\",\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups for a DataFrame of patients.\n\n    This is the efficient batch version of get_directorate_from_diagnosis().\n    Instead of querying Snowflake per patient, it batches the lookups for performance.\n\n    Strategy:\n    1. Get all unique (PersonKey, Drug Name) pairs from DataFrame\n    2. For each unique drug, get all SNOMED codes from local SQLite\n    3. Build batched Snowflake queries to check GP records\n    4. Return indication_df mapping UPID  Indication_Group\n\n    For unmatched patients, Indication_Group will be their Directory (with suffix).\n\n    Args:\n        df: DataFrame with columns: UPID, Drug Name, Directory, PersonKey\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        batch_size: Number of patients per Snowflake query batch\n\n    Returns:\n        DataFrame with columns: UPID, Indication_Group, Source\n        - Indication_Group: Search_Term (if matched) or \"Directory (no GP dx)\" (if not)\n        - Source: \"DIAGNOSIS\" or \"FALLBACK\"\n    \"\"\"\n    import pandas as pd\n\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    logger.info(f\"Starting batch indication lookup for {len(df)} records...\")\n\n    # Step 1: Get unique (UPID, Drug Name, PseudoNHSNoLinked, Directory) combinations\n    # We need PseudoNHSNoLinked to query Snowflake - this matches PatientPseudonym in GP records\n    # Note: PersonKey is LocalPatientID which is provider-specific and does NOT match GP records\n    if 'PseudoNHSNoLinked' not in df.columns:\n        logger.error(\"DataFrame missing 'PseudoNHSNoLinked' column - cannot lookup GP records\")\n        # Return fallback for all patients\n        result_df = df[['UPID', 'Directory']].drop_duplicates().copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']]\n\n    # Get unique patient-drug combinations (we need one lookup per patient-drug pair)\n    unique_pairs = df[['UPID', 'Drug Name', 'PseudoNHSNoLinked', 'Directory']].drop_duplicates()\n    logger.info(f\"Found {len(unique_pairs)} unique patient-drug combinations\")\n\n    # Step 2: Get all unique drugs and their SNOMED codes\n    unique_drugs = unique_pairs['Drug Name'].unique()\n    logger.info(f\"Building SNOMED lookup for {len(unique_drugs)} unique drugs...\")\n\n    # Build drug -> list of DrugSnomedMapping dict\n    drug_snomed_map: dict[str, list[DrugSnomedMapping]] = {}\n    all_snomed_codes: set[str] = set()\n    snomed_to_drug_searchterm: dict[str, list[tuple[str, str, str]]] = {}  # snomed -> [(drug, search_term, primary_dir), ...]\n\n    for drug_name in unique_drugs:\n        mappings = get_drug_snomed_codes(drug_name, db_manager)\n        drug_snomed_map[drug_name] = mappings\n\n        for m in mappings:\n            all_snomed_codes.add(m.snomed_code)\n            if m.snomed_code not in snomed_to_drug_searchterm:\n                snomed_to_drug_searchterm[m.snomed_code] = []\n            snomed_to_drug_searchterm[m.snomed_code].append(\n                (drug_name, m.search_term, m.primary_directorate)\n            )\n\n    logger.info(f\"Total SNOMED codes to check: {len(all_snomed_codes)}\")\n\n    # Step 3: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Step 4: Query GP records for all patients in batches\n    # The query finds the most recent matching SNOMED code for each patient\n\n    # Get unique PseudoNHSNoLinked values (each = one patient in GP records)\n    unique_patients = unique_pairs[['PseudoNHSNoLinked', 'UPID', 'Directory']].drop_duplicates(subset=['PseudoNHSNoLinked'])\n    patient_pseudonyms = unique_patients['PseudoNHSNoLinked'].tolist()\n\n    logger.info(f\"Querying GP records for {len(patient_pseudonyms)} unique patients in batches of {batch_size}...\")\n\n    # Results dict: PersonKey -> (snomed_code, event_date)\n    gp_matches: dict[str, tuple[str, Any]] = {}\n\n    # Convert SNOMED codes to list for query\n    snomed_list = list(all_snomed_codes)\n\n    if not snomed_list:\n        logger.warning(\"No SNOMED codes to check - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    # Build SNOMED IN clause (reused across batches)\n    snomed_placeholders = \", \".join([\"%s\"] * len(snomed_list))\n\n    # Process patients in batches\n    for batch_start in range(0, len(patient_pseudonyms), batch_size):\n        batch_end = min(batch_start + batch_size, len(patient_pseudonyms))\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n\n        logger.info(f\"Batch {batch_start//batch_size + 1}: patients {batch_start} to {batch_end}\")\n\n        # Build patient IN clause\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Query to find all matching SNOMED codes for these patients\n        # We'll get all matches and pick the most recent per patient in Python\n        query = f'''\n            SELECT\n                \"PatientPseudonym\",\n                \"SNOMEDCode\",\n                \"EventDateTime\"\n            FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n            WHERE \"PatientPseudonym\" IN ({patient_placeholders})\n              AND \"SNOMEDCode\" IN ({snomed_placeholders})\n            ORDER BY \"PatientPseudonym\", \"EventDateTime\" DESC\n        '''\n\n        params = tuple(batch_pseudonyms) + tuple(snomed_list)\n\n        try:\n            results = connector.execute_dict(query, params)\n\n            # Process results - pick most recent per patient\n            for row in results:\n                person_key = row.get(\"PatientPseudonym\")\n                snomed_code = row.get(\"SNOMEDCode\")\n                event_date = row.get(\"EventDateTime\")\n\n                if person_key and snomed_code:\n                    # Keep only if we haven't seen this patient yet (first = most recent due to ORDER BY)\n                    if person_key not in gp_matches:\n                        gp_matches[person_key] = (snomed_code, event_date)\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch: {e}\")\n            # Continue with other batches\n\n    logger.info(f\"Found GP matches for {len(gp_matches)} patients\")\n\n    # Step 5: Build result DataFrame\n    # For each unique_pair, determine Indication_Group based on match status\n    results_list = []\n\n    # We need to dedupe by UPID - a patient might be on multiple drugs\n    # Strategy: For each UPID, use the most recent match (if any)\n    upid_to_match: dict[str, tuple[str, str]] = {}  # UPID -> (Indication_Group, Source)\n\n    for _, row in unique_pairs.iterrows():\n        upid = row['UPID']\n        drug_name = row['Drug Name']\n        patient_pseudonym = row['PseudoNHSNoLinked']\n        directory = row['Directory']\n\n        # Check if patient has GP match (using PseudoNHSNoLinked which matches PatientPseudonym in GP)\n        if patient_pseudonym in gp_matches:\n            matched_snomed, event_date = gp_matches[patient_pseudonym]\n\n            # Find the search_term for this SNOMED code and drug\n            # (A SNOMED code might map to multiple drugs with different search_terms)\n            if matched_snomed in snomed_to_drug_searchterm:\n                # Look for match with current drug first\n                search_term = None\n                for drug, st, pd in snomed_to_drug_searchterm[matched_snomed]:\n                    if drug.upper() == drug_name.upper():\n                        search_term = st\n                        break\n                # If no drug-specific match, use any match\n                if search_term is None:\n                    search_term = snomed_to_drug_searchterm[matched_snomed][0][1]\n\n                # Only update if we don't have a match for this UPID yet\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (search_term, \"DIAGNOSIS\")\n            else:\n                # Shouldn't happen but fallback just in case\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n        else:\n            # No GP match - use fallback\n            if upid not in upid_to_match:\n                upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n\n    # Build result DataFrame\n    for upid, (indication_group, source) in upid_to_match.items():\n        results_list.append({\n            'UPID': upid,\n            'Indication_Group': indication_group,\n            'Source': source,\n        })\n\n    result_df = pd.DataFrame(results_list)\n\n    # Log statistics\n    diagnosis_count = len([s for s in result_df['Source'] if s == \"DIAGNOSIS\"])\n    fallback_count = len([s for s in result_df['Source'] if s == \"FALLBACK\"])\n    total = len(result_df)\n\n    logger.info(f\"Indication lookup complete:\")\n    logger.info(f\"  Total unique patients: {total}\")\n    logger.info(f\"  DIAGNOSIS matches: {diagnosis_count} ({100*diagnosis_count/total:.1f}%)\")\n    logger.info(f\"  FALLBACK (no GP match): {fallback_count} ({100*fallback_count/total:.1f}%)\")\n\n    return result_df\n\n\n# === NEW APPROACH: Query Snowflake directly using cluster CTE ===\n\n# The cluster query mapping (embedded from snomed_indication_mapping_query.sql)\n# This maps Search_Term -> Cluster_ID for ~148 clinical conditions\nCLUSTER_MAPPING_SQL = \"\"\"\nWITH SearchTermClusters AS (\n    SELECT Search_Term, Cluster_ID FROM (VALUES\n        ('acute lymphoblastic leukaemia', 'HAEMCANMORPH_COD'),\n        ('acute myeloid leukaemia', 'C19HAEMCAN_COD'),\n        ('acute promyelocytic leukaemia', 'HAEMCANMORPH_COD'),\n        ('allergic asthma', 'AST_COD'),\n        ('allergic rhinitis', 'MILDINTAST_COD'),\n        ('alzheimer''s disease', 'DEMALZ_COD'),\n        ('amyloidosis', 'AMYLOID_COD'),\n        ('anaemia', 'eFI2_AnaemiaTimeSensitive'),\n        ('anaplastic large cell lymphoma', 'C19HAEMCAN_COD'),\n        ('apixaban', 'DOACCON_COD'),\n        ('aplastic anaemia', 'eFI2_AnaemiaEver'),\n        ('arthritis', 'eFI2_InflammatoryArthritis'),\n        ('asthma', 'eFI2_Asthma'),\n        ('atopic dermatitis', 'ATOPDERM_COD'),\n        ('atrial fibrillation', 'eFI2_AtrialFibrillation'),\n        ('attention deficit hyperactivity disorder', 'ADHD_COD'),\n        ('bipolar disorder', 'MH_COD'),\n        ('bladder', 'eFI2_UrinaryIncontinence'),\n        ('breast cancer', 'BRCANSCR_COD'),\n        ('cardiomyopathy', 'eFI2_HarmfulDrinking'),\n        ('cardiovascular disease', 'CVDRISKASS_COD'),\n        ('cervical cancer', 'CSDEC_COD'),\n        ('cholangiocarcinoma', 'eFI2_Cancer'),\n        ('chronic kidney disease', 'CKD_COD'),\n        ('chronic liver disease', 'eFI2_LiverProblems'),\n        ('chronic lymphocytic leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic myeloid leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic obstructive pulmonary disease', 'eFI2_COPD'),\n        ('colon cancer', 'eFI2_Cancer'),\n        ('colorectal cancer', 'GICANREF_COD'),\n        ('constipation', 'CHRONCONSTIP_COD'),\n        ('covid-19', 'POSSPOSTCOVID_COD'),\n        ('crohn''s disease', 'eFI2_InflammatoryBowelDisease'),\n        ('cutaneous t-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('cystic fibrosis', 'CUST_ICB_CYSTIC_FIBROSIS'),\n        ('deep vein thrombosis', 'VTE_COD'),\n        ('depression', 'eFI2_Depression'),\n        ('diabetes', 'eFI2_DiabetesEver'),\n        ('diabetic retinopathy', 'DRSELIGIBILITY_COD'),\n        ('diffuse large b-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('dravet syndrome', 'EPIL_COD'),\n        ('drug misuse', 'ILLSUBINT_COD'),\n        ('dyspepsia', 'eFI2_AbdominalPain'),\n        ('epilepsy', 'eFI2_Seizures'),\n        ('fallopian tube', 'STERIL_COD'),\n        ('follicular lymphoma', 'C19HAEMCAN_COD'),\n        ('gastric cancer', 'eFI2_Cancer'),\n        ('giant cell arteritis', 'GCA_COD'),\n        ('glioma', 'NHAEMCANMORPH_COD'),\n        ('gout', 'eFI2_InflammatoryArthritis'),\n        ('graft versus host disease', 'GVHD_COD'),\n        ('granulomatosis with polyangiitis', 'WEGENERVASC_COD'),\n        ('growth hormone deficiency', 'HYPOPITUITARY_COD'),\n        ('hand eczema', 'ECZEMA_COD'),\n        ('heart failure', 'eFI2_HeartFailure'),\n        ('hepatitis b', 'HEPBCVAC_COD'),\n        ('hepatocellular carcinoma', 'eFI2_Cancer'),\n        ('hiv', 'PREFLANG_COD'),\n        ('hodgkin lymphoma', 'HAEMCANMORPH_COD'),\n        ('hormone receptor', 'eFI2_ThyroidProblems'),\n        ('hypercholesterolaemia', 'CLASSFH_COD'),\n        ('immune thrombocytopenia', 'ITP_COD'),\n        ('influenza', 'FLUINVITE_COD'),\n        ('insomnia', 'eFI2_SleepProblems'),\n        ('irritable bowel syndrome', 'IBS_COD'),\n        ('ischaemic stroke', 'OSTR_COD'),\n        ('juvenile idiopathic arthritis', 'RARTHAD_COD'),\n        ('kidney transplant', 'RENALTRANSP_COD'),\n        ('leukaemia', 'eFI2_Cancer'),\n        ('lung cancer', 'FTCANREF_COD'),\n        ('lymphoma', 'C19HAEMCAN_COD'),\n        ('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('macular oedema', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('major depressive episodes', 'eFI2_Depression'),\n        ('malignant melanoma', 'eFI2_Cancer'),\n        ('malignant pleural mesothelioma', 'LUNGCAN_COD'),\n        ('manic episode', 'MH_COD'),\n        ('mantle cell lymphoma', 'HAEMCANMORPH_COD'),\n        ('melanoma', 'eFI2_Cancer'),\n        ('merkel cell carcinoma', 'C19CAN_COD'),\n        ('migraine', 'eFI2_Headache'),\n        ('motor neurone disease', 'MND_COD'),\n        ('multiple myeloma', 'C19HAEMCAN_COD'),\n        ('multiple sclerosis', 'MS_COD'),\n        ('myelodysplastic', 'eFI2_AnaemiaEver'),\n        ('myelofibrosis', 'MDS_COD'),\n        ('myocardial infarction', 'eFI2_IschaemicHeartDisease'),\n        ('myotonia', 'CNDATRISK2_COD'),\n        ('narcolepsy', 'LD_COD'),\n        ('neuroendocrine tumour', 'LUNGCAN_COD'),\n        ('non-small cell lung cancer', 'LUNGCAN_COD'),\n        ('non-small-cell lung cancer', 'FTCANREF_COD'),\n        ('obesity', 'BMI30_COD'),\n        ('osteoarthritis', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('osteoporosis', 'eFI2_Osteoporosis'),\n        ('osteosarcoma', 'NHAEMCANMORPH_COD'),\n        ('ovarian cancer', 'C19CAN_COD'),\n        ('peripheral arterial disease', 'PADEXC_COD'),\n        ('plaque psoriasis', 'PSORIASIS_COD'),\n        ('polycystic kidney disease', 'EPPCONGMALF_COD'),\n        ('polycythaemia vera', 'C19HAEMCAN_COD'),\n        ('pregnancy', 'C19PREG_COD'),\n        ('primary biliary cholangitis', 'eFI2_LiverProblems'),\n        ('primary hypercholesterolaemia', 'FNFHYP_COD'),\n        ('prostate cancer', 'EPPSOLIDCAN_COD'),\n        ('psoriasis', 'PSORIASIS_COD'),\n        ('psoriatic arthritis', 'RARTHAD_COD'),\n        ('pulmonary embolism', 'eFI2_RespiratoryDiseaseTimeSensitive'),\n        ('pulmonary fibrosis', 'ILD_COD'),\n        ('relapsing multiple sclerosis', 'MS_COD'),\n        ('renal cell carcinoma', 'C19CAN_COD'),\n        ('renal transplantation', 'RENALTRANSP_COD'),\n        ('retinal vein occlusion', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),\n        ('rivaroxaban', 'DOACCON_COD'),\n        ('schizophrenia', 'MH_COD'),\n        ('seizures', 'LSZFREQ_COD'),\n        ('sepsis', 'C19ACTIVITY_COD'),\n        ('severe persistent allergic asthma', 'SEVAST_COD'),\n        ('sickle cell disease', 'SICKLE_COD'),\n        ('sleep apnoea', 'CUST_ICB_NON_SEVERE_LDA'),\n        ('smoking cessation', 'SMOKINGINT_COD'),\n        ('soft tissue sarcoma', 'NHAEMCANMORPH_COD'),\n        ('spinal muscular atrophy', 'MND_COD'),\n        ('squamous cell', 'C19CAN_COD'),\n        ('squamous cell carcinoma', 'C19CAN_COD'),\n        ('stem cell transplant', 'ALLOTRANSP_COD'),\n        ('stroke', 'eFI2_Stroke'),\n        ('systemic lupus erythematosus', 'SLUPUS_COD'),\n        ('systemic mastocytosis', 'HAEMCANMORPH_COD'),\n        ('thrombocytopenic purpura', 'TTP_COD'),\n        ('thrombotic thrombocytopenic purpura', 'TTP_COD'),\n        ('thyroid cancer', 'C19CAN_COD'),\n        ('tophaceous gout', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('transitional cell carcinoma', 'C19CAN_COD'),\n        ('type 1 diabetes', 'DMTYPE1_COD'),\n        ('type 2 diabetes', 'DMTYPE2_COD'),\n        ('ulcerative colitis', 'eFI2_InflammatoryBowelDisease'),\n        ('urothelial carcinoma', 'NHAEMCANMORPH_COD'),\n        ('urticaria', 'XSAL_COD'),\n        ('uveitis', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('vascular disease', 'CVDINVITE_COD'),\n        ('vasculitis', 'CRYOGLOBVASC_COD')\n    ) AS t(Search_Term, Cluster_ID)\n),\n\nClusterCodes AS (\n    SELECT\n        stc.Search_Term,\n        c.\"SNOMEDCode\",\n        c.\"SNOMEDDescription\"\n    FROM SearchTermClusters stc\n    JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" c\n        ON stc.Cluster_ID = c.\"Cluster_ID\"\n    WHERE c.\"SNOMEDCode\" IS NOT NULL\n),\n\nExplicitCodes AS (\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES\n        ('acute coronary syndrome', '837091000000100', 'Manual mapping'),\n        ('ankylosing spondylitis', '162930007', 'Manual mapping'),\n        ('ankylosing spondylitis', '239805001', 'Manual mapping'),\n        ('ankylosing spondylitis', '239810002', 'Manual mapping'),\n        ('ankylosing spondylitis', '239811003', 'Manual mapping'),\n        ('ankylosing spondylitis', '394990003', 'Manual mapping'),\n        ('ankylosing spondylitis', '429712009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441562009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441680005', 'Manual mapping'),\n        ('ankylosing spondylitis', '441930001', 'Manual mapping'),\n        ('axial spondyloarthritis', '723116002', 'Manual mapping'),\n        ('choroidal neovascularisation', '380621000000102', 'Manual mapping'),\n        ('choroidal neovascularisation', '733124000', 'Manual mapping')\n    ) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)\n),\n\nAllIndicationCodes AS (\n    SELECT Search_Term, \"SNOMEDCode\" AS SNOMEDCode, \"SNOMEDDescription\" AS SNOMEDDescription\n    FROM ClusterCodes\n    UNION ALL\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription\n    FROM ExplicitCodes\n)\n\"\"\"\n\n\ndef get_patient_indication_groups(\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups using Snowflake cluster query.\n\n    This function queries Snowflake directly using the embedded cluster CTE\n    (from snomed_indication_mapping_query.sql) to find patients with matching\n    GP diagnoses. This is the NEW approach replacing the old SQLite-based lookup.\n\n    The query:\n    1. Uses the cluster mapping CTE to get all Search_Term -> SNOMED code mappings\n    2. Joins with PrimaryCareClinicalCoding to find patients with matching codes\n    3. Returns the most recent match per patient (by EventDateTime)\n\n    Args:\n        patient_pseudonyms: List of PseudoNHSNoLinked values (matches PatientPseudonym in GP records)\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        batch_size: Number of patients per Snowflake query batch (default 500)\n\n    Returns:\n        DataFrame with columns:\n        - PatientPseudonym: The patient identifier (PseudoNHSNoLinked value)\n        - Search_Term: The matched indication (e.g., \"rheumatoid arthritis\")\n        - EventDateTime: Date of the GP diagnosis record\n\n        Patients not found in results have no matching GP diagnosis.\n    \"\"\"\n    import pandas as pd\n\n    logger.info(f\"Starting Snowflake-direct indication lookup for {len(patient_pseudonyms)} patients...\")\n\n    # Handle edge case: empty patient list\n    if not patient_pseudonyms:\n        logger.warning(\"Empty patient list provided\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    # Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.error(\"Snowflake connector not available - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if not is_snowflake_configured():\n        logger.error(\"Snowflake not configured - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Results list to collect all matches\n    all_results: list[dict] = []\n\n    # Process patients in batches\n    total_patients = len(patient_pseudonyms)\n    for batch_start in range(0, total_patients, batch_size):\n        batch_end = min(batch_start + batch_size, total_patients)\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n        batch_num = batch_start // batch_size + 1\n        total_batches = (total_patients + batch_size - 1) // batch_size\n\n        logger.info(f\"Batch {batch_num}/{total_batches}: patients {batch_start + 1} to {batch_end}\")\n\n        # Build patient IN clause placeholders\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Build the full query with cluster CTE\n        # This finds the most recent matching diagnosis for each patient\n        # Note: Column names must be aliased to ensure consistent casing in results\n        query = f\"\"\"\n{CLUSTER_MAPPING_SQL}\nSELECT\n    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n    aic.Search_Term AS \"Search_Term\",\n    pc.\"EventDateTime\" AS \"EventDateTime\"\nFROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\nINNER JOIN AllIndicationCodes aic\n    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\nWHERE pc.\"PatientPseudonym\" IN ({patient_placeholders})\nQUALIFY ROW_NUMBER() OVER (\n    PARTITION BY pc.\"PatientPseudonym\"\n    ORDER BY pc.\"EventDateTime\" DESC\n) = 1\n\"\"\"\n\n        try:\n            results = connector.execute_dict(query, tuple(batch_pseudonyms))\n\n            for row in results:\n                all_results.append({\n                    'PatientPseudonym': row.get('PatientPseudonym'),\n                    'Search_Term': row.get('Search_Term'),\n                    'EventDateTime': row.get('EventDateTime'),\n                })\n\n            logger.debug(f\"Batch {batch_num}: found {len(results)} matches\")\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch {batch_num}: {e}\")\n            # Continue with other batches - partial results are better than none\n\n    # Build result DataFrame\n    result_df = pd.DataFrame(all_results)\n\n    # Log summary statistics\n    if len(result_df) > 0:\n        matched_count = len(result_df)\n        match_rate = 100 * matched_count / total_patients\n        unique_terms = result_df['Search_Term'].nunique()\n        logger.info(f\"Indication lookup complete:\")\n        logger.info(f\"  Total patients queried: {total_patients}\")\n        logger.info(f\"  Patients with GP match: {matched_count} ({match_rate:.1f}%)\")\n        logger.info(f\"  Unique Search_Terms found: {unique_terms}\")\n\n        # Log top Search_Terms\n        top_terms = result_df['Search_Term'].value_counts().head(5)\n        logger.info(f\"  Top 5 indications: {dict(top_terms)}\")\n    else:\n        logger.info(f\"Indication lookup complete: 0 matches from {total_patients} patients\")\n\n    return result_df\n\n\n# Export public API\n__all__ = [\n    # Dataclasses\n    \"ClusterSnomedCodes\",\n    \"IndicationValidationResult\",\n    \"DrugIndicationMatchRate\",\n    \"DrugSnomedMapping\",\n    \"DirectSnomedMatchResult\",\n    \"DirectorateAssignment\",\n    # Cluster-based lookup functions (existing)\n    \"get_drug_clusters\",\n    \"get_drug_cluster_ids\",\n    \"get_cluster_snomed_codes\",\n    \"patient_has_indication\",\n    \"validate_indication\",\n    \"get_indication_match_rate\",\n    \"batch_validate_indications\",\n    \"get_available_clusters\",\n    # Direct SNOMED lookup functions (new)\n    \"get_drug_snomed_codes\",\n    \"patient_has_indication_direct\",\n    # Diagnosis-based directorate assignment\n    \"get_directorate_from_diagnosis\",\n    # Batch lookup for indication groups\n    \"batch_lookup_indication_groups\",\n    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]\n","numLines":1435,"startLine":1,"totalLines":1435}}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_011T86Lo6hUUXt3G8EYdq6NZ","type":"message","role":"assistant","content":[{"type":"text","text":"Good. I've read all the key files. Let me assess the situation:\n\n**This is Iteration 1.** All tasks are `[ ]`. No previous work has been done.\n\n**Task selection reasoning:**\n\nLooking at the IMPLEMENTATION_PLAN.md phases:\n- **Phase 1** (1.1 and 1.2) ÔÇö foundational: update the Snowflake query + build drug mapping\n- **Phase 2** (2.1 and 2.2) ÔÇö depends on Phase 1 outputs  \n- **Phase 3** (3.1 and 3.2) ÔÇö depends on Phase 2\n- **Phase 4** (4.1-4.3) ÔÇö depends on Phase 3\n\nThe natural starting point is **Task 1.1** (update `get_patient_indication_groups()`) AND **Task 1.2** (build drug-to-Search_Term mapping). These are independent of each other ÔÇö 1.1 modifies the Snowflake query, 1.2 loads from CSV. However, per the \"one task per iteration\" rule, I'll start with **Task 1.2** because it's self-contained, requires no Snowflake access to test, and can be validated locally. Task 1.1 modifies the Snowflake query which can't be tested without a live connection.\n\nLet me mark Task 1.2 as in-progress and implement it."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5960,"cache_creation_input_tokens":22022,"cache_read_input_tokens":78619,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":22022},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"1e65503d-2d7d-49d2-b592-e0af09671275"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_011T86Lo6hUUXt3G8EYdq6NZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017iKQvseZgodTNduStuPsBy","name":"TodoWrite","input":{"todos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"in_progress","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"pending","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":5960,"cache_creation_input_tokens":22022,"cache_read_input_tokens":78619,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":22022},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"e3174850-96f6-4093-a18d-5d6b7fa62fd9"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017iKQvseZgodTNduStuPsBy","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"217c0f81-5861-4c1f-a37e-a1d45757a778","tool_use_result":{"oldTodos":[],"newTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"in_progress","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"pending","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}]}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015QxChuKyziYDLvkaBJxpUJ","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me mark Task 1.2 as in-progress in IMPLEMENTATION_PLAN.md."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":6507,"cache_read_input_tokens":100641,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":6507},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"c32de5c9-e768-47c6-a32e-fb1f30317d2c"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015QxChuKyziYDLvkaBJxpUJ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01EL1BzQHsHUXfrYLS9NY83L","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md","old_string":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","new_string":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":6507,"cache_read_input_tokens":100641,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":6507},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"c1fef445-3485-42a2-bb85-0e0d1f203bcf"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EL1BzQHsHUXfrYLS9NY83L","type":"tool_result","content":"The file C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"eb99a87a-9bec-4285-80c0-4596d69a2e73","tool_use_result":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md","oldString":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","newString":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","originalFile":"# Implementation Plan - Drug-Aware Indication Matching\n\n## Project Overview\n\nUpdate the indication-based pathway charts so that patient indications are matched **per drug**, not just per patient. Currently, each patient gets ONE indication (most recent GP diagnosis match). This ignores which drugs the patient is actually taking.\n\n### The Problem\n\nA patient on ADALIMUMAB + OMALIZUMAB currently gets assigned a single indication (e.g., \"rheumatoid arthritis\"  the most recent GP match). But:\n- ADALIMUMAB is used for rheumatoid arthritis, axial spondyloarthritis, crohn's disease, etc.\n- OMALIZUMAB is used for asthma, allergic asthma, urticaria\n\nThese are different clinical pathways and should be treated as separate treatment journeys.\n\n### The Solution\n\nMatch each drug to an indication by cross-referencing:\n1. **GP diagnosis**  which Search_Terms the patient has matching SNOMED codes for\n2. **Drug mapping**  which Search_Terms list each drug (from `DimSearchTerm.csv`)\n\nOnly assign a drug to an indication if BOTH conditions are met. If a patient's drugs map to different indications, they become separate pathways (via modified UPID).\n\n### Key Design Decisions\n\n| Aspect | Decision |\n|--------|----------|\n| Drug-indication source | `data/DimSearchTerm.csv`  Search_Term  CleanedDrugName mapping |\n| UPID modification | `{original_UPID}\\|{search_term}` for drugs with matched indication |\n| GP diagnosis matching | Return ALL matches per patient (not just most recent) |\n| Drug matching | Substring match: HCD drug name contains DimSearchTerm fragment |\n| Multiple indication matches per drug | Use highest GP code frequency as tiebreaker (COUNT of matching SNOMED codes per Search_Term) |\n| GP code time range | Only codes from MIN(Intervention Date) onwards  restricts to HCD data window |\n| No indication match | Fallback to directory (same as current behavior) |\n| Same patient, different indications | Separate pathways via different modified UPIDs |\n\n### Examples\n\n**Patient on ADALIMUMAB + GOLIMUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis drug list includes both ADALIMUMAB and GOLIMUMAB\n-  Both drugs grouped under \"axial spondyloarthritis\", single pathway\n- Modified UPID: `RMV12345|axial spondyloarthritis`\n\n**Patient on ADALIMUMAB + OMALIZUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis lists ADALIMUMAB but not OMALIZUMAB\n- asthma lists OMALIZUMAB but not ADALIMUMAB\n-  Two separate pathways:\n  - `RMV12345|axial spondyloarthritis` with ADALIMUMAB\n  - `RMV12345|asthma` with OMALIZUMAB\n\n**Patient on ADALIMUMAB, GP dx: rheumatoid arthritis (47 codes) + crohn's disease (2 codes)**\n- Both Search_Terms list ADALIMUMAB AND patient has GP dx for both\n-  Tiebreaker: highest code frequency  rheumatoid arthritis has 47 matching SNOMED codes vs 2 for crohn's\n-  Single pathway under rheumatoid arthritis (more clinical activity = more likely the treatment indication)\n\n---\n\n## Phase 1: Update Snowflake Query & Drug Mapping\n\n### 1.1 Update `get_patient_indication_groups()` to return ALL matches with frequency\n- [ ] Modify the Snowflake query in `get_patient_indication_groups()` (diagnosis_lookup.py):\n  - Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`\n  - Return ALL matching Search_Terms per patient with code frequency:\n    ```sql\n    SELECT pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n           aic.Search_Term AS \"Search_Term\",\n           COUNT(*) AS \"code_frequency\"\n    FROM PrimaryCareClinicalCoding pc\n    JOIN AllIndicationCodes aic ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\n    WHERE pc.\"PatientPseudonym\" IN (...)\n      AND pc.\"EventDateTime\" >= :earliest_hcd_date\n    GROUP BY pc.\"PatientPseudonym\", aic.Search_Term\n    ```\n  - `code_frequency` = number of matching SNOMED codes per Search_Term per patient\n  - Higher frequency = more clinical activity = stronger signal for tiebreaker\n  - `earliest_hcd_date` = `MIN(Intervention Date)` from the HCD DataFrame  restricts GP codes to the HCD data window, reducing noise from old/irrelevant diagnoses\n- [ ] Accept `earliest_hcd_date` parameter in `get_patient_indication_groups()` and pass to query\n- [ ] Keep batch processing (500 patients per query)\n- [ ] Update return type: DataFrame now has multiple rows per patient (PatientPseudonym, Search_Term, code_frequency)\n- [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)\n\n### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n  - Checks if the drug name matches any fragment in the Search_Term's drug list\n  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"\n\n---\n\n## Phase 2: Drug-Aware Indication Matching Logic\n\n### 2.1 Create `assign_drug_indications()` function\n- [ ] Add to `diagnosis_lookup.py` or `pathway_pipeline.py`:\n  ```\n  def assign_drug_indications(\n      df: pd.DataFrame,              # HCD data with UPID, Drug Name columns\n      gp_matches_df: pd.DataFrame,   # PatientPseudonym  list of matched Search_Terms\n      drug_mapping: dict,             # From load_drug_indication_mapping()\n  ) -> tuple[pd.DataFrame, pd.DataFrame]:\n      Returns: (modified_df, indication_df)\n      - modified_df: HCD data with UPID replaced by {UPID}|{indication}\n      - indication_df: mapping modified_UPID  Search_Term\n  ```\n- [ ] Logic per UPID + Drug Name pair:\n  1. Get patient's GP-matched Search_Terms with code_frequency (from gp_matches_df via PseudoNHSNoLinked)\n  2. Get which Search_Terms include this drug (from drug_mapping)\n  3. Intersection = valid indications for this drug-patient pair\n  4. If 1 match: use it\n  5. If multiple matches: use highest code_frequency as tiebreaker (most GP coding activity = most likely treatment indication)\n  6. If 0 matches: use fallback directory\n- [ ] Modify UPID in df rows: `{original_UPID}|{matched_search_term}`\n- [ ] Build indication_df: `{modified_UPID}`  `Search_Term` (or fallback label)\n- [ ] Verify: Function compiles, handles edge cases (no GP match, no drug match)\n\n### 2.2 Handle tiebreaker for multiple indication matches\n- [ ] When a drug matches multiple Search_Terms AND patient has GP dx for multiple:\n  - Use `code_frequency` from the GP query (COUNT of matching SNOMED codes per Search_Term)\n  - Higher code_frequency = more clinical activity for that condition = more likely treatment indication\n  - E.g., patient with 47 RA codes and 2 crohn's codes  ADALIMUMAB assigned to RA\n  - code_frequency is already returned by the updated query in Task 1.1\n- [ ] Verify: Tiebreaker logic correctly picks highest-frequency diagnosis\n- [ ] Verify: Tie on frequency (rare but possible) falls back to alphabetical Search_Term for determinism\n\n---\n\n## Phase 3: Pipeline Integration\n\n### 3.1 Update `refresh_pathways.py` indication processing\n- [ ] In the `elif current_chart_type == \"indication\":` block:\n  1. Call `get_patient_indication_groups()` as before (but now returns ALL matches)\n  2. Load drug mapping: `drug_mapping = load_drug_indication_mapping()`\n  3. Call `assign_drug_indications(df, gp_matches_df, drug_mapping)`\n  4. Use modified_df (with indication-aware UPIDs) for pathway processing\n  5. Use indication_df for the indication mapping\n- [ ] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`\n- [ ] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`\n\n### 3.2 Test with dry run\n- [ ] Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`\n- [ ] Verify:\n  - Modified UPIDs appear in pipeline log (e.g., `RMV12345|rheumatoid arthritis`)\n  - Patient counts are reasonable (will be higher than before since same patient can appear under multiple indications)\n  - Drug-indication matching is logged (match rate, fallback rate)\n  - Pathway hierarchy shows drug-specific grouping under correct indications\n\n---\n\n## Phase 4: Full Refresh & Validation\n\n### 4.1 Full refresh with both chart types\n- [ ] Run `python -m cli.refresh_pathways --chart-type all`\n- [ ] Verify:\n  - Both chart types generate data\n  - Directory charts unchanged (no modified UPIDs)\n  - Indication charts reflect drug-aware matching\n\n### 4.2 Validate indication chart correctness\n- [ ] Check that drugs under an indication all appear in that Search_Term's drug list\n- [ ] Verify that a patient on drugs for different indications creates separate pathway branches\n- [ ] Verify that drugs sharing an indication are grouped in the same pathway\n- [ ] Log: patient count comparison (old vs new approach)\n\n### 4.3 Validate Reflex UI\n- [ ] Run `python -m reflex compile` to verify app compiles\n- [ ] Verify chart type toggle still works\n- [ ] Verify indication chart shows correct hierarchy\n\n---\n\n## Completion Criteria\n\nAll tasks marked `[x]` AND:\n- [ ] App compiles without errors (`reflex compile` succeeds)\n- [ ] Both chart types generate pathway data\n- [ ] Indication charts show drug-specific indication matching\n- [ ] Drugs under the same indication for the same patient are in one pathway\n- [ ] Drugs under different indications for the same patient create separate pathways\n- [ ] Fallback works for drugs with no indication match\n- [ ] Full refresh completes successfully\n- [ ] Existing directory charts are unaffected\n\n---\n\n## Reference\n\n### DimSearchTerm.csv Structure\n```\nSearch_Term,CleanedDrugName,PrimaryDirectorate\nrheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|...,RHEUMATOLOGY\nasthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\n```\n\n### Modified UPID Format\n```\nOriginal:  RMV12345\nModified:  RMV12345|rheumatoid arthritis\nFallback:  RMV12345|RHEUMATOLOGY (no GP dx)\n```\n\n### Current vs New Indication Flow\n```\nCURRENT:\n  Patient  GP dx (most recent)  single Search_Term  one pathway\n\nNEW:\n  Patient + Drug A  GP dx matching Drug A  Search_Term X\n  Patient + Drug B  GP dx matching Drug B  Search_Term Y\n   If X == Y: one pathway under X\n   If X != Y: two pathways (modified UPIDs)\n```\n\n### Key Files\n\n| File | Changes |\n|------|---------|\n| `data_processing/diagnosis_lookup.py` | Update query, add drug mapping functions |\n| `data_processing/pathway_pipeline.py` | Possibly minor changes for modified UPIDs |\n| `cli/refresh_pathways.py` | Integrate drug-aware matching into pipeline |\n| `data/DimSearchTerm.csv` | Reference data (read-only) |\n| `analysis/pathway_analyzer.py` | No changes expected (UPID changes are transparent) |\n| `pathways_app/pathways_app.py` | No changes expected |\n","structuredPatch":[{"oldStart":79,"oldLines":7,"newStart":79,"newLines":7,"lines":[" - [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)"," "," ### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv","-- [ ] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","+- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","   - Loads `data/DimSearchTerm.csv`","   - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`","   - Also builds reverse: `search_term  list[drug_fragments]`"]}],"userModified":false,"replaceAll":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01F9U622gACXikqCnmmmkDAP","type":"message","role":"assistant","content":[{"type":"text","text":"Now I'll implement the two functions. Looking at the DimSearchTerm.csv structure:\n- `Search_Term` ÔÇö clinical condition\n- `CleanedDrugName` ÔÇö pipe-separated drug fragments  \n- `PrimaryDirectorate` ÔÇö the directorate\n\nNote: `diabetes` appears twice (DIABETIC MEDICINE and OPHTHALMOLOGY directorates). For indication charts, we use Search_Term not directorate, so the drug fragments from both rows should be combined.\n\nLet me add the functions to `diagnosis_lookup.py`. I'll add them right before the `CLUSTER_MAPPING_SQL` constant since they're logically in the \"drug mapping\" category."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":296,"cache_read_input_tokens":107148,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":296},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"50ce28bb-1d19-407a-a55e-c75259ded9e8"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01F9U622gACXikqCnmmmkDAP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BBvWPg9vZnrjPpc1VzRHVa","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py","old_string":"# === NEW APPROACH: Query Snowflake directly using cluster CTE ===","new_string":"# === Drug-to-indication mapping from DimSearchTerm.csv ===\n\n\ndef load_drug_indication_mapping(\n    csv_path: Optional[str] = None,\n) -> tuple[dict[str, list[str]], dict[str, list[str]]]:\n    \"\"\"\n    Load the drug-to-Search_Term mapping from DimSearchTerm.csv.\n\n    Builds two lookup dicts:\n    - fragment_to_search_terms: drug fragment (UPPERCASE) -> list of Search_Terms containing it\n    - search_term_to_fragments: search_term -> list of drug fragments (UPPERCASE)\n\n    DimSearchTerm.csv has columns: Search_Term, CleanedDrugName, PrimaryDirectorate\n    CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\").\n\n    Note: A Search_Term can appear multiple times with different PrimaryDirectorates\n    (e.g., \"diabetes\" appears under both DIABETIC MEDICINE and OPHTHALMOLOGY).\n    Drug fragments from all rows for the same Search_Term are combined.\n\n    Args:\n        csv_path: Path to DimSearchTerm.csv. Defaults to data/DimSearchTerm.csv.\n\n    Returns:\n        Tuple of (fragment_to_search_terms, search_term_to_fragments)\n    \"\"\"\n    if csv_path is None:\n        csv_path = str(Path(__file__).parent.parent / \"data\" / \"DimSearchTerm.csv\")\n\n    fragment_to_search_terms: dict[str, list[str]] = {}\n    search_term_to_fragments: dict[str, list[str]] = {}\n\n    try:\n        with open(csv_path, \"r\", encoding=\"utf-8\") as f:\n            reader = csv.DictReader(f)\n            for row in reader:\n                search_term = row.get(\"Search_Term\", \"\").strip()\n                drug_names_raw = row.get(\"CleanedDrugName\", \"\").strip()\n\n                if not search_term or not drug_names_raw:\n                    continue\n\n                fragments = [frag.strip().upper() for frag in drug_names_raw.split(\"|\") if frag.strip()]\n\n                # Build search_term -> fragments (accumulate for duplicate Search_Terms)\n                if search_term not in search_term_to_fragments:\n                    search_term_to_fragments[search_term] = []\n                for frag in fragments:\n                    if frag not in search_term_to_fragments[search_term]:\n                        search_term_to_fragments[search_term].append(frag)\n\n                # Build fragment -> search_terms\n                for frag in fragments:\n                    if frag not in fragment_to_search_terms:\n                        fragment_to_search_terms[frag] = []\n                    if search_term not in fragment_to_search_terms[frag]:\n                        fragment_to_search_terms[frag].append(search_term)\n\n        logger.info(\n            f\"Loaded drug-indication mapping: {len(search_term_to_fragments)} Search_Terms, \"\n            f\"{len(fragment_to_search_terms)} drug fragments\"\n        )\n\n    except FileNotFoundError:\n        logger.error(f\"DimSearchTerm.csv not found at {csv_path}\")\n    except Exception as e:\n        logger.error(f\"Error loading DimSearchTerm.csv: {e}\")\n\n    return fragment_to_search_terms, search_term_to_fragments\n\n\ndef get_search_terms_for_drug(\n    drug_name: str,\n    search_term_to_fragments: dict[str, list[str]],\n) -> list[str]:\n    \"\"\"\n    Get all Search_Terms that list a given drug using substring matching.\n\n    Checks if any drug fragment from DimSearchTerm is a SUBSTRING of the given\n    drug name (case-insensitive). This handles both exact matches (ADALIMUMAB)\n    and partial fragments (PEGYLATED, INHALED).\n\n    Args:\n        drug_name: HCD drug name (e.g., \"ADALIMUMAB 40MG\", \"PEGYLATED LIPOSOMAL DOXORUBICIN\")\n        search_term_to_fragments: Mapping of search_term -> list of drug fragments\n\n    Returns:\n        List of Search_Terms whose drug fragments match the drug name\n    \"\"\"\n    drug_name_upper = drug_name.upper()\n    matched_terms: list[str] = []\n\n    for search_term, fragments in search_term_to_fragments.items():\n        for frag in fragments:\n            if frag in drug_name_upper:\n                matched_terms.append(search_term)\n                break  # One matching fragment is enough for this Search_Term\n\n    return matched_terms\n\n\n# === NEW APPROACH: Query Snowflake directly using cluster CTE ==="}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":296,"cache_read_input_tokens":107148,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":296},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"fe650f35-0c9b-4891-b7e3-6c3512b2d695"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BBvWPg9vZnrjPpc1VzRHVa","type":"tool_result","content":"The file C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"bbed3f8a-ec8d-4c3e-a3e7-fd07f78d96f8","tool_use_result":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py","oldString":"# === NEW APPROACH: Query Snowflake directly using cluster CTE ===","newString":"# === Drug-to-indication mapping from DimSearchTerm.csv ===\n\n\ndef load_drug_indication_mapping(\n    csv_path: Optional[str] = None,\n) -> tuple[dict[str, list[str]], dict[str, list[str]]]:\n    \"\"\"\n    Load the drug-to-Search_Term mapping from DimSearchTerm.csv.\n\n    Builds two lookup dicts:\n    - fragment_to_search_terms: drug fragment (UPPERCASE) -> list of Search_Terms containing it\n    - search_term_to_fragments: search_term -> list of drug fragments (UPPERCASE)\n\n    DimSearchTerm.csv has columns: Search_Term, CleanedDrugName, PrimaryDirectorate\n    CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\").\n\n    Note: A Search_Term can appear multiple times with different PrimaryDirectorates\n    (e.g., \"diabetes\" appears under both DIABETIC MEDICINE and OPHTHALMOLOGY).\n    Drug fragments from all rows for the same Search_Term are combined.\n\n    Args:\n        csv_path: Path to DimSearchTerm.csv. Defaults to data/DimSearchTerm.csv.\n\n    Returns:\n        Tuple of (fragment_to_search_terms, search_term_to_fragments)\n    \"\"\"\n    if csv_path is None:\n        csv_path = str(Path(__file__).parent.parent / \"data\" / \"DimSearchTerm.csv\")\n\n    fragment_to_search_terms: dict[str, list[str]] = {}\n    search_term_to_fragments: dict[str, list[str]] = {}\n\n    try:\n        with open(csv_path, \"r\", encoding=\"utf-8\") as f:\n            reader = csv.DictReader(f)\n            for row in reader:\n                search_term = row.get(\"Search_Term\", \"\").strip()\n                drug_names_raw = row.get(\"CleanedDrugName\", \"\").strip()\n\n                if not search_term or not drug_names_raw:\n                    continue\n\n                fragments = [frag.strip().upper() for frag in drug_names_raw.split(\"|\") if frag.strip()]\n\n                # Build search_term -> fragments (accumulate for duplicate Search_Terms)\n                if search_term not in search_term_to_fragments:\n                    search_term_to_fragments[search_term] = []\n                for frag in fragments:\n                    if frag not in search_term_to_fragments[search_term]:\n                        search_term_to_fragments[search_term].append(frag)\n\n                # Build fragment -> search_terms\n                for frag in fragments:\n                    if frag not in fragment_to_search_terms:\n                        fragment_to_search_terms[frag] = []\n                    if search_term not in fragment_to_search_terms[frag]:\n                        fragment_to_search_terms[frag].append(search_term)\n\n        logger.info(\n            f\"Loaded drug-indication mapping: {len(search_term_to_fragments)} Search_Terms, \"\n            f\"{len(fragment_to_search_terms)} drug fragments\"\n        )\n\n    except FileNotFoundError:\n        logger.error(f\"DimSearchTerm.csv not found at {csv_path}\")\n    except Exception as e:\n        logger.error(f\"Error loading DimSearchTerm.csv: {e}\")\n\n    return fragment_to_search_terms, search_term_to_fragments\n\n\ndef get_search_terms_for_drug(\n    drug_name: str,\n    search_term_to_fragments: dict[str, list[str]],\n) -> list[str]:\n    \"\"\"\n    Get all Search_Terms that list a given drug using substring matching.\n\n    Checks if any drug fragment from DimSearchTerm is a SUBSTRING of the given\n    drug name (case-insensitive). This handles both exact matches (ADALIMUMAB)\n    and partial fragments (PEGYLATED, INHALED).\n\n    Args:\n        drug_name: HCD drug name (e.g., \"ADALIMUMAB 40MG\", \"PEGYLATED LIPOSOMAL DOXORUBICIN\")\n        search_term_to_fragments: Mapping of search_term -> list of drug fragments\n\n    Returns:\n        List of Search_Terms whose drug fragments match the drug name\n    \"\"\"\n    drug_name_upper = drug_name.upper()\n    matched_terms: list[str] = []\n\n    for search_term, fragments in search_term_to_fragments.items():\n        for frag in fragments:\n            if frag in drug_name_upper:\n                matched_terms.append(search_term)\n                break  # One matching fragment is enough for this Search_Term\n\n    return matched_terms\n\n\n# === NEW APPROACH: Query Snowflake directly using cluster CTE ===","originalFile":"\"\"\"\nDiagnosis lookup module for NHS Patient Pathway Analysis.\n\nProvides functions to validate patient indications by checking GP diagnosis records\nagainst SNOMED cluster codes. Uses the drug-to-cluster mapping from\ndrug_indication_clusters.csv and queries Snowflake for SNOMED codes and GP records.\n\nKey workflow:\n1. Get drug's valid indication clusters from local mapping\n2. Get all SNOMED codes for those clusters from Snowflake\n3. Check if patient has any of those SNOMED codes in GP records\n4. Report indication validation status\n\nIMPORTANT: HCD activity data indication codes are UNRELIABLE. This module uses\nGP/Primary Care data (PrimaryCareClinicalCoding) as the authoritative source.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom datetime import date, datetime\nfrom pathlib import Path\nfrom typing import Optional, Callable, Any, cast, TYPE_CHECKING\nimport csv\n\nif TYPE_CHECKING:\n    import pandas as pd\n\nfrom core.logging_config import get_logger\nfrom data_processing.database import DatabaseManager, default_db_manager\nfrom data_processing.snowflake_connector import (\n    SnowflakeConnector,\n    get_connector,\n    is_snowflake_available,\n    is_snowflake_configured,\n    SNOWFLAKE_AVAILABLE,\n)\nfrom data_processing.cache import get_cache, is_cache_enabled\n\nlogger = get_logger(__name__)\n\n\n@dataclass\nclass ClusterSnomedCodes:\n    \"\"\"SNOMED codes for a clinical coding cluster.\"\"\"\n    cluster_id: str\n    cluster_description: str\n    snomed_codes: list[str] = field(default_factory=list)\n    snomed_descriptions: dict[str, str] = field(default_factory=dict)\n\n    @property\n    def code_count(self) -> int:\n        return len(self.snomed_codes)\n\n\n@dataclass\nclass IndicationValidationResult:\n    \"\"\"Result of validating a patient's indication for a drug.\"\"\"\n    patient_pseudonym: str\n    drug_name: str\n    has_valid_indication: bool\n    matched_cluster_id: Optional[str] = None\n    matched_snomed_code: Optional[str] = None\n    matched_snomed_description: Optional[str] = None\n    checked_clusters: list[str] = field(default_factory=list)\n    total_codes_checked: int = 0\n    source: str = \"GP_SNOMED\"  # GP_SNOMED | NONE\n    error_message: Optional[str] = None\n\n\n@dataclass\nclass DrugIndicationMatchRate:\n    \"\"\"Match rate statistics for a drug's indication validation.\"\"\"\n    drug_name: str\n    total_patients: int\n    patients_with_indication: int\n    patients_without_indication: int\n    match_rate: float  # 0.0 to 1.0\n    clusters_checked: list[str] = field(default_factory=list)\n    sample_unmatched: list[str] = field(default_factory=list)  # Sample patient IDs\n\n\n@dataclass\nclass DrugSnomedMapping:\n    \"\"\"SNOMED code mapping for a drug from ref_drug_snomed_mapping.\"\"\"\n    snomed_code: str\n    snomed_description: str\n    search_term: str\n    primary_directorate: str\n    indication: str = \"\"\n    ta_id: str = \"\"\n\n\n@dataclass\nclass DirectSnomedMatchResult:\n    \"\"\"Result of direct SNOMED code lookup in GP records.\"\"\"\n    patient_pseudonym: str\n    matched: bool\n    snomed_code: Optional[str] = None\n    snomed_description: Optional[str] = None\n    search_term: Optional[str] = None\n    primary_directorate: Optional[str] = None\n    event_date: Optional[datetime] = None\n    source: str = \"DIRECT_SNOMED\"  # DIRECT_SNOMED | NONE\n\n\n@dataclass\nclass DirectorateAssignment:\n    \"\"\"Result of directorate assignment for a patient-drug combination.\"\"\"\n    upid: str\n    drug_name: str\n    directorate: Optional[str]\n    search_term: Optional[str] = None\n    source: str = \"FALLBACK\"  # DIAGNOSIS | FALLBACK\n    snomed_code: Optional[str] = None\n    event_date: Optional[datetime] = None\n\n\ndef get_drug_clusters(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[dict]:\n    \"\"\"\n    Get all SNOMED cluster mappings for a drug from local SQLite.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of dicts with keys: drug_name, indication, cluster_id,\n        cluster_description, nice_ta_reference\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT drug_name, indication, cluster_id, cluster_description, nice_ta_reference\n        FROM ref_drug_indication_clusters\n        WHERE UPPER(drug_name) = UPPER(?)\n        ORDER BY indication, cluster_id\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name,))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append({\n                    \"drug_name\": row[\"drug_name\"],\n                    \"indication\": row[\"indication\"],\n                    \"cluster_id\": row[\"cluster_id\"],\n                    \"cluster_description\": row[\"cluster_description\"],\n                    \"nice_ta_reference\": row[\"nice_ta_reference\"],\n                })\n\n            logger.debug(f\"Found {len(results)} cluster mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting clusters for drug '{drug_name}': {e}\")\n        return []\n\n\ndef get_drug_cluster_ids(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[str]:\n    \"\"\"\n    Get unique cluster IDs for a drug.\n\n    Args:\n        drug_name: Drug name to look up\n        db_manager: Optional DatabaseManager\n\n    Returns:\n        List of unique cluster IDs\n    \"\"\"\n    clusters = get_drug_clusters(drug_name, db_manager)\n    return list(set(c[\"cluster_id\"] for c in clusters))\n\n\ndef get_drug_snomed_codes(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[DrugSnomedMapping]:\n    \"\"\"\n    Get all SNOMED codes for a drug from local ref_drug_snomed_mapping table.\n\n    This uses the enriched mapping CSV data loaded into SQLite, which provides\n    direct SNOMED-to-drug mappings with Search_Term and PrimaryDirectorate.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive, matches cleaned_drug_name)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of DrugSnomedMapping with snomed_code, snomed_description,\n        search_term, primary_directorate, indication, ta_id\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT DISTINCT\n            snomed_code,\n            snomed_description,\n            search_term,\n            primary_directorate,\n            indication,\n            ta_id\n        FROM ref_drug_snomed_mapping\n        WHERE UPPER(cleaned_drug_name) = UPPER(?)\n           OR UPPER(drug_name) = UPPER(?)\n        ORDER BY search_term, snomed_code\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name, drug_name))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append(DrugSnomedMapping(\n                    snomed_code=row[\"snomed_code\"],\n                    snomed_description=row[\"snomed_description\"] or \"\",\n                    search_term=row[\"search_term\"] or \"\",\n                    primary_directorate=row[\"primary_directorate\"] or \"\",\n                    indication=row[\"indication\"] or \"\",\n                    ta_id=row[\"ta_id\"] or \"\",\n                ))\n\n            logger.debug(f\"Found {len(results)} SNOMED mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for drug '{drug_name}': {e}\")\n        return []\n\n\ndef patient_has_indication_direct(\n    patient_pseudonym: str,\n    drug_snomed_mappings: list[DrugSnomedMapping],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> DirectSnomedMatchResult:\n    \"\"\"\n    Check if patient has any of the SNOMED codes in their GP records.\n\n    This is the direct SNOMED lookup - it queries PrimaryCareClinicalCoding\n    for exact SNOMED code matches (not via cluster). Returns the most recent\n    match by EventDateTime if multiple matches exist.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_snomed_mappings: List of DrugSnomedMapping from get_drug_snomed_codes()\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectSnomedMatchResult with match details (most recent by EventDateTime)\n    \"\"\"\n    result = DirectSnomedMatchResult(\n        patient_pseudonym=patient_pseudonym,\n        matched=False,\n        source=\"NONE\",\n    )\n\n    if not drug_snomed_mappings:\n        return result\n\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot check GP records\")\n        return result\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build lookup dict for mapping snomed_code -> (search_term, primary_directorate, snomed_description)\n    snomed_lookup = {\n        m.snomed_code: (m.search_term, m.primary_directorate, m.snomed_description)\n        for m in drug_snomed_mappings\n    }\n\n    # Get unique SNOMED codes\n    snomed_codes = list(snomed_lookup.keys())\n\n    # Build placeholders for SNOMED codes\n    placeholders = \", \".join([\"%s\"] * len(snomed_codes))\n\n    # Query to find most recent matching SNOMED code in GP records\n    query = f'''\n        SELECT\n            \"SNOMEDCode\",\n            \"EventDateTime\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n        WHERE \"PatientPseudonym\" = %s\n            AND \"SNOMEDCode\" IN ({placeholders})\n    '''\n\n    params: list = [patient_pseudonym] + snomed_codes\n\n    if before_date:\n        query += ' AND \"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' ORDER BY \"EventDateTime\" DESC LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            matched_code = row.get(\"SNOMEDCode\")\n            event_dt = row.get(\"EventDateTime\")\n\n            if matched_code and matched_code in snomed_lookup:\n                search_term, primary_dir, snomed_desc = snomed_lookup[matched_code]\n\n                return DirectSnomedMatchResult(\n                    patient_pseudonym=patient_pseudonym,\n                    matched=True,\n                    snomed_code=matched_code,\n                    snomed_description=snomed_desc,\n                    search_term=search_term,\n                    primary_directorate=primary_dir,\n                    event_date=event_dt,\n                    source=\"DIRECT_SNOMED\",\n                )\n\n        return result\n\n    except Exception as e:\n        logger.error(f\"Error checking direct SNOMED for patient '{patient_pseudonym}': {e}\")\n        return result\n\n\ndef get_directorate_from_diagnosis(\n    upid: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> DirectorateAssignment:\n    \"\"\"\n    Get directorate assignment for a patient-drug combination using diagnosis-based lookup.\n\n    This function attempts to assign a directorate based on the patient's GP records\n    (direct SNOMED code matching). If no match is found, it returns a FALLBACK result\n    indicating that the caller should use alternative assignment methods (e.g.,\n    department_identification() from tools/data.py).\n\n    Workflow:\n    1. Get all SNOMED codes for the drug from ref_drug_snomed_mapping\n    2. Query patient's GP records for matching SNOMED codes\n    3. If match found  return diagnosis-based directorate and search_term\n    4. If no match  return FALLBACK result (caller handles fallback logic)\n\n    Args:\n        upid: Patient's unique patient ID (Provider Code[:3] + PersonKey)\n        drug_name: Drug name to look up\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectorateAssignment with directorate, search_term, and source\n    \"\"\"\n    result = DirectorateAssignment(\n        upid=upid,\n        drug_name=drug_name,\n        directorate=None,\n        source=\"FALLBACK\",\n    )\n\n    # Step 1: Get SNOMED codes for the drug\n    drug_snomed_mappings = get_drug_snomed_codes(drug_name, db_manager)\n\n    if not drug_snomed_mappings:\n        logger.debug(f\"No SNOMED mappings found for drug '{drug_name}' - using fallback\")\n        return result\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.debug(\"Snowflake not available - using fallback\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.debug(\"Snowflake not configured - using fallback\")\n        return result\n\n    # Step 3: Get patient pseudonym from UPID\n    # UPID format is Provider Code (3 chars) + PersonKey\n    # We need to query Snowflake to get the PatientPseudonym for this PersonKey\n    # However, patient_has_indication_direct expects PatientPseudonym, not UPID\n    # For now, we'll use UPID as the identifier - the actual integration\n    # will need to happen at the DataFrame level where we have PersonKey\n    #\n    # NOTE: This function will be called from the pipeline where we have\n    # access to PatientPseudonym. The UPID is passed for logging/tracking.\n\n    # Actually, looking at the pipeline, we need PatientPseudonym, not UPID.\n    # The caller should pass the PatientPseudonym or we need to look it up.\n    # For now, let's assume the caller will use this in a batch context\n    # where they can map UPID -> PatientPseudonym.\n\n    # Let me reconsider: the function signature takes UPID but we need\n    # PatientPseudonym for Snowflake. In the pipeline context (fetch_and_transform_data),\n    # we'll have the PersonKey column which IS the PatientPseudonym.\n    # So UPID = ProviderCode[:3] + PersonKey, and PersonKey = PatientPseudonym.\n    #\n    # We can extract PatientPseudonym from UPID by removing the first 3 chars.\n    patient_pseudonym = upid[3:] if len(upid) > 3 else upid\n\n    # Step 4: Check patient's GP records for matching SNOMED codes\n    match_result = patient_has_indication_direct(\n        patient_pseudonym=patient_pseudonym,\n        drug_snomed_mappings=drug_snomed_mappings,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    if match_result.matched and match_result.primary_directorate:\n        return DirectorateAssignment(\n            upid=upid,\n            drug_name=drug_name,\n            directorate=match_result.primary_directorate,\n            search_term=match_result.search_term,\n            source=\"DIAGNOSIS\",\n            snomed_code=match_result.snomed_code,\n            event_date=match_result.event_date,\n        )\n\n    # No match found - return fallback result\n    return result\n\n\ndef get_cluster_snomed_codes(\n    cluster_id: str,\n    connector: Optional[SnowflakeConnector] = None,\n    use_cache: bool = True,\n) -> ClusterSnomedCodes:\n    \"\"\"\n    Get all SNOMED codes for a cluster from Snowflake.\n\n    Queries the ClinicalCodingClusterSnomedCodes table to get all SNOMED codes\n    that belong to the specified cluster.\n\n    Args:\n        cluster_id: Cluster ID to look up (e.g., 'RARTH_COD', 'PSORIASIS_COD')\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        use_cache: Whether to use cached results (default True)\n\n    Returns:\n        ClusterSnomedCodes with list of SNOMED codes and descriptions\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot get cluster codes\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    # Check cache first\n    cache_key = f\"cluster_snomed_{cluster_id}\"\n    if use_cache and is_cache_enabled():\n        cache = get_cache()\n        cached = cache.get(cache_key)\n        if cached is not None and len(cached) > 0:\n            logger.debug(f\"Using cached SNOMED codes for cluster '{cluster_id}'\")\n            cached_dict = cached[0]  # First element is our data dict\n            return ClusterSnomedCodes(\n                cluster_id=cluster_id,\n                cluster_description=str(cached_dict.get(\"description\", \"\")),\n                snomed_codes=list(cached_dict.get(\"codes\", [])),\n                snomed_descriptions=dict(cached_dict.get(\"descriptions\", {})),\n            )\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT DISTINCT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            \"SNOMEDCode\",\n            \"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        WHERE \"Cluster_ID\" = %s\n        ORDER BY \"SNOMEDCode\"\n    '''\n\n    try:\n        results = connector.execute_dict(query, (cluster_id,))\n\n        if not results:\n            logger.warning(f\"No SNOMED codes found for cluster '{cluster_id}'\")\n            return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n        codes = []\n        descriptions = {}\n        description = results[0].get(\"Cluster_Description\", \"\") if results else \"\"\n\n        for row in results:\n            code = row.get(\"SNOMEDCode\")\n            if code:\n                codes.append(code)\n                descriptions[code] = row.get(\"SNOMEDDescription\", \"\")\n\n        logger.info(f\"Found {len(codes)} SNOMED codes for cluster '{cluster_id}'\")\n\n        # Cache the results (using query-based cache with fake params)\n        if use_cache and is_cache_enabled():\n            cache = get_cache()\n            cache_data = [{\n                \"description\": description,\n                \"codes\": codes,\n                \"descriptions\": descriptions,\n            }]\n            cache.set(cache_key, None, cache_data)  # type: ignore[arg-type]\n\n        return ClusterSnomedCodes(\n            cluster_id=cluster_id,\n            cluster_description=description,\n            snomed_codes=codes,\n            snomed_descriptions=descriptions,\n        )\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for cluster '{cluster_id}': {e}\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n\ndef patient_has_indication(\n    patient_pseudonym: str,\n    cluster_ids: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> tuple[bool, Optional[str], Optional[str], Optional[str]]:\n    \"\"\"\n    Check if a patient has any SNOMED codes from the specified clusters in GP records.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        cluster_ids: List of cluster IDs to check against\n        connector: Optional SnowflakeConnector\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        Tuple of (has_indication, matched_cluster_id, matched_snomed_code, matched_description)\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        return False, None, None, None\n\n    if not cluster_ids:\n        return False, None, None, None\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build placeholders for cluster IDs\n    placeholders = \", \".join([\"%s\"] * len(cluster_ids))\n\n    # Query to check if patient has any matching SNOMED code\n    query = f'''\n        SELECT\n            pc.\"SNOMEDCode\",\n            cc.\"Cluster_ID\",\n            cc.\"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\n        INNER JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" cc\n            ON pc.\"SNOMEDCode\" = cc.\"SNOMEDCode\"\n        WHERE pc.\"PatientPseudonym\" = %s\n            AND cc.\"Cluster_ID\" IN ({placeholders})\n    '''\n\n    params = [patient_pseudonym] + cluster_ids\n\n    if before_date:\n        query += ' AND pc.\"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            return (\n                True,\n                row.get(\"Cluster_ID\"),\n                row.get(\"SNOMEDCode\"),\n                row.get(\"SNOMEDDescription\"),\n            )\n\n        return False, None, None, None\n\n    except Exception as e:\n        logger.error(f\"Error checking indication for patient '{patient_pseudonym}': {e}\")\n        return False, None, None, None\n\n\ndef validate_indication(\n    patient_pseudonym: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> IndicationValidationResult:\n    \"\"\"\n    Validate that a patient has an appropriate indication for a drug.\n\n    Full validation workflow:\n    1. Get drug's valid indication clusters from local mapping\n    2. Check if patient has any matching SNOMED codes in GP records\n    3. Return detailed validation result\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_name: Drug name to validate indication for\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        IndicationValidationResult with validation details\n    \"\"\"\n    result = IndicationValidationResult(\n        patient_pseudonym=patient_pseudonym,\n        drug_name=drug_name,\n        has_valid_indication=False,\n    )\n\n    # Step 1: Get drug's cluster mappings\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    if not cluster_ids:\n        result.error_message = f\"No cluster mappings found for drug '{drug_name}'\"\n        result.source = \"NONE\"\n        return result\n\n    result.checked_clusters = cluster_ids\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        result.error_message = \"Snowflake connector not installed\"\n        result.source = \"NONE\"\n        return result\n\n    if not is_snowflake_configured():\n        result.error_message = \"Snowflake not configured\"\n        result.source = \"NONE\"\n        return result\n\n    # Step 3: Check patient GP records\n    has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n        patient_pseudonym=patient_pseudonym,\n        cluster_ids=cluster_ids,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    result.has_valid_indication = has_indication\n    result.matched_cluster_id = matched_cluster\n    result.matched_snomed_code = matched_code\n    result.matched_snomed_description = matched_desc\n    result.source = \"GP_SNOMED\" if has_indication else \"NONE\"\n\n    return result\n\n\ndef get_indication_match_rate(\n    drug_name: str,\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    sample_unmatched_count: int = 10,\n) -> DrugIndicationMatchRate:\n    \"\"\"\n    Calculate indication match rate for a drug across a list of patients.\n\n    Args:\n        drug_name: Drug name to check\n        patient_pseudonyms: List of patient pseudonymised NHS numbers\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        sample_unmatched_count: Number of unmatched patient IDs to include in sample\n\n    Returns:\n        DrugIndicationMatchRate with match statistics\n    \"\"\"\n    if connector is None and SNOWFLAKE_AVAILABLE and is_snowflake_configured():\n        connector = get_connector()\n\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    total = len(patient_pseudonyms)\n    matched = 0\n    unmatched = 0\n    sample_unmatched: list[str] = []\n\n    if not cluster_ids:\n        logger.warning(f\"No cluster mappings for drug '{drug_name}' - all patients will be unmatched\")\n        return DrugIndicationMatchRate(\n            drug_name=drug_name,\n            total_patients=total,\n            patients_with_indication=0,\n            patients_without_indication=total,\n            match_rate=0.0,\n            clusters_checked=[],\n            sample_unmatched=patient_pseudonyms[:sample_unmatched_count],\n        )\n\n    for i, pseudonym in enumerate(patient_pseudonyms):\n        if i > 0 and i % 100 == 0:\n            logger.info(f\"Validating indications: {i}/{total} ({100*i/total:.1f}%)\")\n\n        has_indication, _, _, _ = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        if has_indication:\n            matched += 1\n        else:\n            unmatched += 1\n            if len(sample_unmatched) < sample_unmatched_count:\n                sample_unmatched.append(pseudonym)\n\n    match_rate = matched / total if total > 0 else 0.0\n\n    logger.info(f\"Indication match rate for '{drug_name}': {100*match_rate:.1f}% ({matched}/{total})\")\n\n    return DrugIndicationMatchRate(\n        drug_name=drug_name,\n        total_patients=total,\n        patients_with_indication=matched,\n        patients_without_indication=unmatched,\n        match_rate=match_rate,\n        clusters_checked=cluster_ids,\n        sample_unmatched=sample_unmatched,\n    )\n\n\ndef batch_validate_indications(\n    patient_drug_pairs: list[tuple[str, str]],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    progress_callback: Optional[Callable[[int, int], None]] = None,\n) -> list[IndicationValidationResult]:\n    \"\"\"\n    Validate indications for multiple patient-drug pairs efficiently.\n\n    Args:\n        patient_drug_pairs: List of (patient_pseudonym, drug_name) tuples\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        progress_callback: Optional callback(current, total) for progress updates\n\n    Returns:\n        List of IndicationValidationResult for each pair\n    \"\"\"\n    results = []\n    total = len(patient_drug_pairs)\n\n    # Cache cluster lookups by drug\n    drug_clusters_cache = {}\n\n    for i, (pseudonym, drug_name) in enumerate(patient_drug_pairs):\n        if progress_callback:\n            progress_callback(i + 1, total)\n\n        # Get clusters from cache or lookup\n        drug_upper = drug_name.upper()\n        if drug_upper not in drug_clusters_cache:\n            drug_clusters_cache[drug_upper] = get_drug_cluster_ids(drug_name, db_manager)\n\n        cluster_ids = drug_clusters_cache[drug_upper]\n\n        if not cluster_ids:\n            results.append(IndicationValidationResult(\n                patient_pseudonym=pseudonym,\n                drug_name=drug_name,\n                has_valid_indication=False,\n                source=\"NONE\",\n                error_message=f\"No cluster mappings for drug '{drug_name}'\",\n            ))\n            continue\n\n        # Check patient indication\n        has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        results.append(IndicationValidationResult(\n            patient_pseudonym=pseudonym,\n            drug_name=drug_name,\n            has_valid_indication=has_indication,\n            matched_cluster_id=matched_cluster,\n            matched_snomed_code=matched_code,\n            matched_snomed_description=matched_desc,\n            checked_clusters=cluster_ids,\n            source=\"GP_SNOMED\" if has_indication else \"NONE\",\n        ))\n\n    matched_count = sum(1 for r in results if r.has_valid_indication)\n    logger.info(f\"Batch validation complete: {matched_count}/{total} ({100*matched_count/total:.1f}%) with valid indications\")\n\n    return results\n\n\ndef get_available_clusters(\n    connector: Optional[SnowflakeConnector] = None,\n) -> list[dict]:\n    \"\"\"\n    Get list of all available SNOMED clusters from Snowflake.\n\n    Returns:\n        List of dicts with cluster_id, cluster_description, code_count\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - cannot list clusters\")\n        return []\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            COUNT(DISTINCT \"SNOMEDCode\") as code_count\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        GROUP BY \"Cluster_ID\", \"Cluster_Description\"\n        ORDER BY \"Cluster_ID\"\n    '''\n\n    try:\n        results = connector.execute_dict(query)\n\n        clusters = []\n        for row in results:\n            clusters.append({\n                \"cluster_id\": row.get(\"Cluster_ID\"),\n                \"cluster_description\": row.get(\"Cluster_Description\"),\n                \"code_count\": row.get(\"code_count\", 0),\n            })\n\n        logger.info(f\"Found {len(clusters)} available SNOMED clusters\")\n        return clusters\n\n    except Exception as e:\n        logger.error(f\"Error getting available clusters: {e}\")\n        return []\n\n\ndef batch_lookup_indication_groups(\n    df: \"pd.DataFrame\",\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups for a DataFrame of patients.\n\n    This is the efficient batch version of get_directorate_from_diagnosis().\n    Instead of querying Snowflake per patient, it batches the lookups for performance.\n\n    Strategy:\n    1. Get all unique (PersonKey, Drug Name) pairs from DataFrame\n    2. For each unique drug, get all SNOMED codes from local SQLite\n    3. Build batched Snowflake queries to check GP records\n    4. Return indication_df mapping UPID  Indication_Group\n\n    For unmatched patients, Indication_Group will be their Directory (with suffix).\n\n    Args:\n        df: DataFrame with columns: UPID, Drug Name, Directory, PersonKey\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        batch_size: Number of patients per Snowflake query batch\n\n    Returns:\n        DataFrame with columns: UPID, Indication_Group, Source\n        - Indication_Group: Search_Term (if matched) or \"Directory (no GP dx)\" (if not)\n        - Source: \"DIAGNOSIS\" or \"FALLBACK\"\n    \"\"\"\n    import pandas as pd\n\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    logger.info(f\"Starting batch indication lookup for {len(df)} records...\")\n\n    # Step 1: Get unique (UPID, Drug Name, PseudoNHSNoLinked, Directory) combinations\n    # We need PseudoNHSNoLinked to query Snowflake - this matches PatientPseudonym in GP records\n    # Note: PersonKey is LocalPatientID which is provider-specific and does NOT match GP records\n    if 'PseudoNHSNoLinked' not in df.columns:\n        logger.error(\"DataFrame missing 'PseudoNHSNoLinked' column - cannot lookup GP records\")\n        # Return fallback for all patients\n        result_df = df[['UPID', 'Directory']].drop_duplicates().copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']]\n\n    # Get unique patient-drug combinations (we need one lookup per patient-drug pair)\n    unique_pairs = df[['UPID', 'Drug Name', 'PseudoNHSNoLinked', 'Directory']].drop_duplicates()\n    logger.info(f\"Found {len(unique_pairs)} unique patient-drug combinations\")\n\n    # Step 2: Get all unique drugs and their SNOMED codes\n    unique_drugs = unique_pairs['Drug Name'].unique()\n    logger.info(f\"Building SNOMED lookup for {len(unique_drugs)} unique drugs...\")\n\n    # Build drug -> list of DrugSnomedMapping dict\n    drug_snomed_map: dict[str, list[DrugSnomedMapping]] = {}\n    all_snomed_codes: set[str] = set()\n    snomed_to_drug_searchterm: dict[str, list[tuple[str, str, str]]] = {}  # snomed -> [(drug, search_term, primary_dir), ...]\n\n    for drug_name in unique_drugs:\n        mappings = get_drug_snomed_codes(drug_name, db_manager)\n        drug_snomed_map[drug_name] = mappings\n\n        for m in mappings:\n            all_snomed_codes.add(m.snomed_code)\n            if m.snomed_code not in snomed_to_drug_searchterm:\n                snomed_to_drug_searchterm[m.snomed_code] = []\n            snomed_to_drug_searchterm[m.snomed_code].append(\n                (drug_name, m.search_term, m.primary_directorate)\n            )\n\n    logger.info(f\"Total SNOMED codes to check: {len(all_snomed_codes)}\")\n\n    # Step 3: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Step 4: Query GP records for all patients in batches\n    # The query finds the most recent matching SNOMED code for each patient\n\n    # Get unique PseudoNHSNoLinked values (each = one patient in GP records)\n    unique_patients = unique_pairs[['PseudoNHSNoLinked', 'UPID', 'Directory']].drop_duplicates(subset=['PseudoNHSNoLinked'])\n    patient_pseudonyms = unique_patients['PseudoNHSNoLinked'].tolist()\n\n    logger.info(f\"Querying GP records for {len(patient_pseudonyms)} unique patients in batches of {batch_size}...\")\n\n    # Results dict: PersonKey -> (snomed_code, event_date)\n    gp_matches: dict[str, tuple[str, Any]] = {}\n\n    # Convert SNOMED codes to list for query\n    snomed_list = list(all_snomed_codes)\n\n    if not snomed_list:\n        logger.warning(\"No SNOMED codes to check - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    # Build SNOMED IN clause (reused across batches)\n    snomed_placeholders = \", \".join([\"%s\"] * len(snomed_list))\n\n    # Process patients in batches\n    for batch_start in range(0, len(patient_pseudonyms), batch_size):\n        batch_end = min(batch_start + batch_size, len(patient_pseudonyms))\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n\n        logger.info(f\"Batch {batch_start//batch_size + 1}: patients {batch_start} to {batch_end}\")\n\n        # Build patient IN clause\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Query to find all matching SNOMED codes for these patients\n        # We'll get all matches and pick the most recent per patient in Python\n        query = f'''\n            SELECT\n                \"PatientPseudonym\",\n                \"SNOMEDCode\",\n                \"EventDateTime\"\n            FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n            WHERE \"PatientPseudonym\" IN ({patient_placeholders})\n              AND \"SNOMEDCode\" IN ({snomed_placeholders})\n            ORDER BY \"PatientPseudonym\", \"EventDateTime\" DESC\n        '''\n\n        params = tuple(batch_pseudonyms) + tuple(snomed_list)\n\n        try:\n            results = connector.execute_dict(query, params)\n\n            # Process results - pick most recent per patient\n            for row in results:\n                person_key = row.get(\"PatientPseudonym\")\n                snomed_code = row.get(\"SNOMEDCode\")\n                event_date = row.get(\"EventDateTime\")\n\n                if person_key and snomed_code:\n                    # Keep only if we haven't seen this patient yet (first = most recent due to ORDER BY)\n                    if person_key not in gp_matches:\n                        gp_matches[person_key] = (snomed_code, event_date)\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch: {e}\")\n            # Continue with other batches\n\n    logger.info(f\"Found GP matches for {len(gp_matches)} patients\")\n\n    # Step 5: Build result DataFrame\n    # For each unique_pair, determine Indication_Group based on match status\n    results_list = []\n\n    # We need to dedupe by UPID - a patient might be on multiple drugs\n    # Strategy: For each UPID, use the most recent match (if any)\n    upid_to_match: dict[str, tuple[str, str]] = {}  # UPID -> (Indication_Group, Source)\n\n    for _, row in unique_pairs.iterrows():\n        upid = row['UPID']\n        drug_name = row['Drug Name']\n        patient_pseudonym = row['PseudoNHSNoLinked']\n        directory = row['Directory']\n\n        # Check if patient has GP match (using PseudoNHSNoLinked which matches PatientPseudonym in GP)\n        if patient_pseudonym in gp_matches:\n            matched_snomed, event_date = gp_matches[patient_pseudonym]\n\n            # Find the search_term for this SNOMED code and drug\n            # (A SNOMED code might map to multiple drugs with different search_terms)\n            if matched_snomed in snomed_to_drug_searchterm:\n                # Look for match with current drug first\n                search_term = None\n                for drug, st, pd in snomed_to_drug_searchterm[matched_snomed]:\n                    if drug.upper() == drug_name.upper():\n                        search_term = st\n                        break\n                # If no drug-specific match, use any match\n                if search_term is None:\n                    search_term = snomed_to_drug_searchterm[matched_snomed][0][1]\n\n                # Only update if we don't have a match for this UPID yet\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (search_term, \"DIAGNOSIS\")\n            else:\n                # Shouldn't happen but fallback just in case\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n        else:\n            # No GP match - use fallback\n            if upid not in upid_to_match:\n                upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n\n    # Build result DataFrame\n    for upid, (indication_group, source) in upid_to_match.items():\n        results_list.append({\n            'UPID': upid,\n            'Indication_Group': indication_group,\n            'Source': source,\n        })\n\n    result_df = pd.DataFrame(results_list)\n\n    # Log statistics\n    diagnosis_count = len([s for s in result_df['Source'] if s == \"DIAGNOSIS\"])\n    fallback_count = len([s for s in result_df['Source'] if s == \"FALLBACK\"])\n    total = len(result_df)\n\n    logger.info(f\"Indication lookup complete:\")\n    logger.info(f\"  Total unique patients: {total}\")\n    logger.info(f\"  DIAGNOSIS matches: {diagnosis_count} ({100*diagnosis_count/total:.1f}%)\")\n    logger.info(f\"  FALLBACK (no GP match): {fallback_count} ({100*fallback_count/total:.1f}%)\")\n\n    return result_df\n\n\n# === NEW APPROACH: Query Snowflake directly using cluster CTE ===\n\n# The cluster query mapping (embedded from snomed_indication_mapping_query.sql)\n# This maps Search_Term -> Cluster_ID for ~148 clinical conditions\nCLUSTER_MAPPING_SQL = \"\"\"\nWITH SearchTermClusters AS (\n    SELECT Search_Term, Cluster_ID FROM (VALUES\n        ('acute lymphoblastic leukaemia', 'HAEMCANMORPH_COD'),\n        ('acute myeloid leukaemia', 'C19HAEMCAN_COD'),\n        ('acute promyelocytic leukaemia', 'HAEMCANMORPH_COD'),\n        ('allergic asthma', 'AST_COD'),\n        ('allergic rhinitis', 'MILDINTAST_COD'),\n        ('alzheimer''s disease', 'DEMALZ_COD'),\n        ('amyloidosis', 'AMYLOID_COD'),\n        ('anaemia', 'eFI2_AnaemiaTimeSensitive'),\n        ('anaplastic large cell lymphoma', 'C19HAEMCAN_COD'),\n        ('apixaban', 'DOACCON_COD'),\n        ('aplastic anaemia', 'eFI2_AnaemiaEver'),\n        ('arthritis', 'eFI2_InflammatoryArthritis'),\n        ('asthma', 'eFI2_Asthma'),\n        ('atopic dermatitis', 'ATOPDERM_COD'),\n        ('atrial fibrillation', 'eFI2_AtrialFibrillation'),\n        ('attention deficit hyperactivity disorder', 'ADHD_COD'),\n        ('bipolar disorder', 'MH_COD'),\n        ('bladder', 'eFI2_UrinaryIncontinence'),\n        ('breast cancer', 'BRCANSCR_COD'),\n        ('cardiomyopathy', 'eFI2_HarmfulDrinking'),\n        ('cardiovascular disease', 'CVDRISKASS_COD'),\n        ('cervical cancer', 'CSDEC_COD'),\n        ('cholangiocarcinoma', 'eFI2_Cancer'),\n        ('chronic kidney disease', 'CKD_COD'),\n        ('chronic liver disease', 'eFI2_LiverProblems'),\n        ('chronic lymphocytic leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic myeloid leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic obstructive pulmonary disease', 'eFI2_COPD'),\n        ('colon cancer', 'eFI2_Cancer'),\n        ('colorectal cancer', 'GICANREF_COD'),\n        ('constipation', 'CHRONCONSTIP_COD'),\n        ('covid-19', 'POSSPOSTCOVID_COD'),\n        ('crohn''s disease', 'eFI2_InflammatoryBowelDisease'),\n        ('cutaneous t-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('cystic fibrosis', 'CUST_ICB_CYSTIC_FIBROSIS'),\n        ('deep vein thrombosis', 'VTE_COD'),\n        ('depression', 'eFI2_Depression'),\n        ('diabetes', 'eFI2_DiabetesEver'),\n        ('diabetic retinopathy', 'DRSELIGIBILITY_COD'),\n        ('diffuse large b-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('dravet syndrome', 'EPIL_COD'),\n        ('drug misuse', 'ILLSUBINT_COD'),\n        ('dyspepsia', 'eFI2_AbdominalPain'),\n        ('epilepsy', 'eFI2_Seizures'),\n        ('fallopian tube', 'STERIL_COD'),\n        ('follicular lymphoma', 'C19HAEMCAN_COD'),\n        ('gastric cancer', 'eFI2_Cancer'),\n        ('giant cell arteritis', 'GCA_COD'),\n        ('glioma', 'NHAEMCANMORPH_COD'),\n        ('gout', 'eFI2_InflammatoryArthritis'),\n        ('graft versus host disease', 'GVHD_COD'),\n        ('granulomatosis with polyangiitis', 'WEGENERVASC_COD'),\n        ('growth hormone deficiency', 'HYPOPITUITARY_COD'),\n        ('hand eczema', 'ECZEMA_COD'),\n        ('heart failure', 'eFI2_HeartFailure'),\n        ('hepatitis b', 'HEPBCVAC_COD'),\n        ('hepatocellular carcinoma', 'eFI2_Cancer'),\n        ('hiv', 'PREFLANG_COD'),\n        ('hodgkin lymphoma', 'HAEMCANMORPH_COD'),\n        ('hormone receptor', 'eFI2_ThyroidProblems'),\n        ('hypercholesterolaemia', 'CLASSFH_COD'),\n        ('immune thrombocytopenia', 'ITP_COD'),\n        ('influenza', 'FLUINVITE_COD'),\n        ('insomnia', 'eFI2_SleepProblems'),\n        ('irritable bowel syndrome', 'IBS_COD'),\n        ('ischaemic stroke', 'OSTR_COD'),\n        ('juvenile idiopathic arthritis', 'RARTHAD_COD'),\n        ('kidney transplant', 'RENALTRANSP_COD'),\n        ('leukaemia', 'eFI2_Cancer'),\n        ('lung cancer', 'FTCANREF_COD'),\n        ('lymphoma', 'C19HAEMCAN_COD'),\n        ('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('macular oedema', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('major depressive episodes', 'eFI2_Depression'),\n        ('malignant melanoma', 'eFI2_Cancer'),\n        ('malignant pleural mesothelioma', 'LUNGCAN_COD'),\n        ('manic episode', 'MH_COD'),\n        ('mantle cell lymphoma', 'HAEMCANMORPH_COD'),\n        ('melanoma', 'eFI2_Cancer'),\n        ('merkel cell carcinoma', 'C19CAN_COD'),\n        ('migraine', 'eFI2_Headache'),\n        ('motor neurone disease', 'MND_COD'),\n        ('multiple myeloma', 'C19HAEMCAN_COD'),\n        ('multiple sclerosis', 'MS_COD'),\n        ('myelodysplastic', 'eFI2_AnaemiaEver'),\n        ('myelofibrosis', 'MDS_COD'),\n        ('myocardial infarction', 'eFI2_IschaemicHeartDisease'),\n        ('myotonia', 'CNDATRISK2_COD'),\n        ('narcolepsy', 'LD_COD'),\n        ('neuroendocrine tumour', 'LUNGCAN_COD'),\n        ('non-small cell lung cancer', 'LUNGCAN_COD'),\n        ('non-small-cell lung cancer', 'FTCANREF_COD'),\n        ('obesity', 'BMI30_COD'),\n        ('osteoarthritis', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('osteoporosis', 'eFI2_Osteoporosis'),\n        ('osteosarcoma', 'NHAEMCANMORPH_COD'),\n        ('ovarian cancer', 'C19CAN_COD'),\n        ('peripheral arterial disease', 'PADEXC_COD'),\n        ('plaque psoriasis', 'PSORIASIS_COD'),\n        ('polycystic kidney disease', 'EPPCONGMALF_COD'),\n        ('polycythaemia vera', 'C19HAEMCAN_COD'),\n        ('pregnancy', 'C19PREG_COD'),\n        ('primary biliary cholangitis', 'eFI2_LiverProblems'),\n        ('primary hypercholesterolaemia', 'FNFHYP_COD'),\n        ('prostate cancer', 'EPPSOLIDCAN_COD'),\n        ('psoriasis', 'PSORIASIS_COD'),\n        ('psoriatic arthritis', 'RARTHAD_COD'),\n        ('pulmonary embolism', 'eFI2_RespiratoryDiseaseTimeSensitive'),\n        ('pulmonary fibrosis', 'ILD_COD'),\n        ('relapsing multiple sclerosis', 'MS_COD'),\n        ('renal cell carcinoma', 'C19CAN_COD'),\n        ('renal transplantation', 'RENALTRANSP_COD'),\n        ('retinal vein occlusion', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),\n        ('rivaroxaban', 'DOACCON_COD'),\n        ('schizophrenia', 'MH_COD'),\n        ('seizures', 'LSZFREQ_COD'),\n        ('sepsis', 'C19ACTIVITY_COD'),\n        ('severe persistent allergic asthma', 'SEVAST_COD'),\n        ('sickle cell disease', 'SICKLE_COD'),\n        ('sleep apnoea', 'CUST_ICB_NON_SEVERE_LDA'),\n        ('smoking cessation', 'SMOKINGINT_COD'),\n        ('soft tissue sarcoma', 'NHAEMCANMORPH_COD'),\n        ('spinal muscular atrophy', 'MND_COD'),\n        ('squamous cell', 'C19CAN_COD'),\n        ('squamous cell carcinoma', 'C19CAN_COD'),\n        ('stem cell transplant', 'ALLOTRANSP_COD'),\n        ('stroke', 'eFI2_Stroke'),\n        ('systemic lupus erythematosus', 'SLUPUS_COD'),\n        ('systemic mastocytosis', 'HAEMCANMORPH_COD'),\n        ('thrombocytopenic purpura', 'TTP_COD'),\n        ('thrombotic thrombocytopenic purpura', 'TTP_COD'),\n        ('thyroid cancer', 'C19CAN_COD'),\n        ('tophaceous gout', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('transitional cell carcinoma', 'C19CAN_COD'),\n        ('type 1 diabetes', 'DMTYPE1_COD'),\n        ('type 2 diabetes', 'DMTYPE2_COD'),\n        ('ulcerative colitis', 'eFI2_InflammatoryBowelDisease'),\n        ('urothelial carcinoma', 'NHAEMCANMORPH_COD'),\n        ('urticaria', 'XSAL_COD'),\n        ('uveitis', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('vascular disease', 'CVDINVITE_COD'),\n        ('vasculitis', 'CRYOGLOBVASC_COD')\n    ) AS t(Search_Term, Cluster_ID)\n),\n\nClusterCodes AS (\n    SELECT\n        stc.Search_Term,\n        c.\"SNOMEDCode\",\n        c.\"SNOMEDDescription\"\n    FROM SearchTermClusters stc\n    JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" c\n        ON stc.Cluster_ID = c.\"Cluster_ID\"\n    WHERE c.\"SNOMEDCode\" IS NOT NULL\n),\n\nExplicitCodes AS (\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES\n        ('acute coronary syndrome', '837091000000100', 'Manual mapping'),\n        ('ankylosing spondylitis', '162930007', 'Manual mapping'),\n        ('ankylosing spondylitis', '239805001', 'Manual mapping'),\n        ('ankylosing spondylitis', '239810002', 'Manual mapping'),\n        ('ankylosing spondylitis', '239811003', 'Manual mapping'),\n        ('ankylosing spondylitis', '394990003', 'Manual mapping'),\n        ('ankylosing spondylitis', '429712009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441562009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441680005', 'Manual mapping'),\n        ('ankylosing spondylitis', '441930001', 'Manual mapping'),\n        ('axial spondyloarthritis', '723116002', 'Manual mapping'),\n        ('choroidal neovascularisation', '380621000000102', 'Manual mapping'),\n        ('choroidal neovascularisation', '733124000', 'Manual mapping')\n    ) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)\n),\n\nAllIndicationCodes AS (\n    SELECT Search_Term, \"SNOMEDCode\" AS SNOMEDCode, \"SNOMEDDescription\" AS SNOMEDDescription\n    FROM ClusterCodes\n    UNION ALL\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription\n    FROM ExplicitCodes\n)\n\"\"\"\n\n\ndef get_patient_indication_groups(\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups using Snowflake cluster query.\n\n    This function queries Snowflake directly using the embedded cluster CTE\n    (from snomed_indication_mapping_query.sql) to find patients with matching\n    GP diagnoses. This is the NEW approach replacing the old SQLite-based lookup.\n\n    The query:\n    1. Uses the cluster mapping CTE to get all Search_Term -> SNOMED code mappings\n    2. Joins with PrimaryCareClinicalCoding to find patients with matching codes\n    3. Returns the most recent match per patient (by EventDateTime)\n\n    Args:\n        patient_pseudonyms: List of PseudoNHSNoLinked values (matches PatientPseudonym in GP records)\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        batch_size: Number of patients per Snowflake query batch (default 500)\n\n    Returns:\n        DataFrame with columns:\n        - PatientPseudonym: The patient identifier (PseudoNHSNoLinked value)\n        - Search_Term: The matched indication (e.g., \"rheumatoid arthritis\")\n        - EventDateTime: Date of the GP diagnosis record\n\n        Patients not found in results have no matching GP diagnosis.\n    \"\"\"\n    import pandas as pd\n\n    logger.info(f\"Starting Snowflake-direct indication lookup for {len(patient_pseudonyms)} patients...\")\n\n    # Handle edge case: empty patient list\n    if not patient_pseudonyms:\n        logger.warning(\"Empty patient list provided\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    # Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.error(\"Snowflake connector not available - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if not is_snowflake_configured():\n        logger.error(\"Snowflake not configured - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Results list to collect all matches\n    all_results: list[dict] = []\n\n    # Process patients in batches\n    total_patients = len(patient_pseudonyms)\n    for batch_start in range(0, total_patients, batch_size):\n        batch_end = min(batch_start + batch_size, total_patients)\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n        batch_num = batch_start // batch_size + 1\n        total_batches = (total_patients + batch_size - 1) // batch_size\n\n        logger.info(f\"Batch {batch_num}/{total_batches}: patients {batch_start + 1} to {batch_end}\")\n\n        # Build patient IN clause placeholders\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Build the full query with cluster CTE\n        # This finds the most recent matching diagnosis for each patient\n        # Note: Column names must be aliased to ensure consistent casing in results\n        query = f\"\"\"\n{CLUSTER_MAPPING_SQL}\nSELECT\n    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n    aic.Search_Term AS \"Search_Term\",\n    pc.\"EventDateTime\" AS \"EventDateTime\"\nFROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\nINNER JOIN AllIndicationCodes aic\n    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\nWHERE pc.\"PatientPseudonym\" IN ({patient_placeholders})\nQUALIFY ROW_NUMBER() OVER (\n    PARTITION BY pc.\"PatientPseudonym\"\n    ORDER BY pc.\"EventDateTime\" DESC\n) = 1\n\"\"\"\n\n        try:\n            results = connector.execute_dict(query, tuple(batch_pseudonyms))\n\n            for row in results:\n                all_results.append({\n                    'PatientPseudonym': row.get('PatientPseudonym'),\n                    'Search_Term': row.get('Search_Term'),\n                    'EventDateTime': row.get('EventDateTime'),\n                })\n\n            logger.debug(f\"Batch {batch_num}: found {len(results)} matches\")\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch {batch_num}: {e}\")\n            # Continue with other batches - partial results are better than none\n\n    # Build result DataFrame\n    result_df = pd.DataFrame(all_results)\n\n    # Log summary statistics\n    if len(result_df) > 0:\n        matched_count = len(result_df)\n        match_rate = 100 * matched_count / total_patients\n        unique_terms = result_df['Search_Term'].nunique()\n        logger.info(f\"Indication lookup complete:\")\n        logger.info(f\"  Total patients queried: {total_patients}\")\n        logger.info(f\"  Patients with GP match: {matched_count} ({match_rate:.1f}%)\")\n        logger.info(f\"  Unique Search_Terms found: {unique_terms}\")\n\n        # Log top Search_Terms\n        top_terms = result_df['Search_Term'].value_counts().head(5)\n        logger.info(f\"  Top 5 indications: {dict(top_terms)}\")\n    else:\n        logger.info(f\"Indication lookup complete: 0 matches from {total_patients} patients\")\n\n    return result_df\n\n\n# Export public API\n__all__ = [\n    # Dataclasses\n    \"ClusterSnomedCodes\",\n    \"IndicationValidationResult\",\n    \"DrugIndicationMatchRate\",\n    \"DrugSnomedMapping\",\n    \"DirectSnomedMatchResult\",\n    \"DirectorateAssignment\",\n    # Cluster-based lookup functions (existing)\n    \"get_drug_clusters\",\n    \"get_drug_cluster_ids\",\n    \"get_cluster_snomed_codes\",\n    \"patient_has_indication\",\n    \"validate_indication\",\n    \"get_indication_match_rate\",\n    \"batch_validate_indications\",\n    \"get_available_clusters\",\n    # Direct SNOMED lookup functions (new)\n    \"get_drug_snomed_codes\",\n    \"patient_has_indication_direct\",\n    # Diagnosis-based directorate assignment\n    \"get_directorate_from_diagnosis\",\n    # Batch lookup for indication groups\n    \"batch_lookup_indication_groups\",\n    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]\n","structuredPatch":[{"oldStart":1087,"oldLines":6,"newStart":1087,"newLines":107,"lines":["     return result_df"," "," ","+# === Drug-to-indication mapping from DimSearchTerm.csv ===","+","+","+def load_drug_indication_mapping(","+    csv_path: Optional[str] = None,","+) -> tuple[dict[str, list[str]], dict[str, list[str]]]:","+    \"\"\"","+    Load the drug-to-Search_Term mapping from DimSearchTerm.csv.","+","+    Builds two lookup dicts:","+    - fragment_to_search_terms: drug fragment (UPPERCASE) -> list of Search_Terms containing it","+    - search_term_to_fragments: search_term -> list of drug fragments (UPPERCASE)","+","+    DimSearchTerm.csv has columns: Search_Term, CleanedDrugName, PrimaryDirectorate","+    CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\").","+","+    Note: A Search_Term can appear multiple times with different PrimaryDirectorates","+    (e.g., \"diabetes\" appears under both DIABETIC MEDICINE and OPHTHALMOLOGY).","+    Drug fragments from all rows for the same Search_Term are combined.","+","+    Args:","+        csv_path: Path to DimSearchTerm.csv. Defaults to data/DimSearchTerm.csv.","+","+    Returns:","+        Tuple of (fragment_to_search_terms, search_term_to_fragments)","+    \"\"\"","+    if csv_path is None:","+        csv_path = str(Path(__file__).parent.parent / \"data\" / \"DimSearchTerm.csv\")","+","+    fragment_to_search_terms: dict[str, list[str]] = {}","+    search_term_to_fragments: dict[str, list[str]] = {}","+","+    try:","+        with open(csv_path, \"r\", encoding=\"utf-8\") as f:","+            reader = csv.DictReader(f)","+            for row in reader:","+                search_term = row.get(\"Search_Term\", \"\").strip()","+                drug_names_raw = row.get(\"CleanedDrugName\", \"\").strip()","+","+                if not search_term or not drug_names_raw:","+                    continue","+","+                fragments = [frag.strip().upper() for frag in drug_names_raw.split(\"|\") if frag.strip()]","+","+                # Build search_term -> fragments (accumulate for duplicate Search_Terms)","+                if search_term not in search_term_to_fragments:","+                    search_term_to_fragments[search_term] = []","+                for frag in fragments:","+                    if frag not in search_term_to_fragments[search_term]:","+                        search_term_to_fragments[search_term].append(frag)","+","+                # Build fragment -> search_terms","+                for frag in fragments:","+                    if frag not in fragment_to_search_terms:","+                        fragment_to_search_terms[frag] = []","+                    if search_term not in fragment_to_search_terms[frag]:","+                        fragment_to_search_terms[frag].append(search_term)","+","+        logger.info(","+            f\"Loaded drug-indication mapping: {len(search_term_to_fragments)} Search_Terms, \"","+            f\"{len(fragment_to_search_terms)} drug fragments\"","+        )","+","+    except FileNotFoundError:","+        logger.error(f\"DimSearchTerm.csv not found at {csv_path}\")","+    except Exception as e:","+        logger.error(f\"Error loading DimSearchTerm.csv: {e}\")","+","+    return fragment_to_search_terms, search_term_to_fragments","+","+","+def get_search_terms_for_drug(","+    drug_name: str,","+    search_term_to_fragments: dict[str, list[str]],","+) -> list[str]:","+    \"\"\"","+    Get all Search_Terms that list a given drug using substring matching.","+","+    Checks if any drug fragment from DimSearchTerm is a SUBSTRING of the given","+    drug name (case-insensitive). This handles both exact matches (ADALIMUMAB)","+    and partial fragments (PEGYLATED, INHALED).","+","+    Args:","+        drug_name: HCD drug name (e.g., \"ADALIMUMAB 40MG\", \"PEGYLATED LIPOSOMAL DOXORUBICIN\")","+        search_term_to_fragments: Mapping of search_term -> list of drug fragments","+","+    Returns:","+        List of Search_Terms whose drug fragments match the drug name","+    \"\"\"","+    drug_name_upper = drug_name.upper()","+    matched_terms: list[str] = []","+","+    for search_term, fragments in search_term_to_fragments.items():","+        for frag in fragments:","+            if frag in drug_name_upper:","+                matched_terms.append(search_term)","+                break  # One matching fragment is enough for this Search_Term","+","+    return matched_terms","+","+"," # === NEW APPROACH: Query Snowflake directly using cluster CTE ==="," "," # The cluster query mapping (embedded from snomed_indication_mapping_query.sql)"]}],"userModified":false,"replaceAll":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_0161tAWHkUMXyYFZMjEzYE1A","type":"message","role":"assistant","content":[{"type":"text","text":"Now I need to add the new functions to the `__all__` export list."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1550,"cache_read_input_tokens":107444,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1550},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"3e19f74a-dd47-4e44-bbc3-d25d8a2c40d4"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_0161tAWHkUMXyYFZMjEzYE1A","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016PoR4b1BWjSK8we3Gat174","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py","old_string":"    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]","new_string":"    # Drug-indication mapping from DimSearchTerm.csv\n    \"load_drug_indication_mapping\",\n    \"get_search_terms_for_drug\",\n    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1550,"cache_read_input_tokens":107444,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1550},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"2a302703-0ad8-4ff6-82fe-de13aff4f0c3"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016PoR4b1BWjSK8we3Gat174","type":"tool_result","content":"The file C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"ceab8aaa-933f-4697-95a2-5e5529fd33c1","tool_use_result":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\data_processing\\diagnosis_lookup.py","oldString":"    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]","newString":"    # Drug-indication mapping from DimSearchTerm.csv\n    \"load_drug_indication_mapping\",\n    \"get_search_terms_for_drug\",\n    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]","originalFile":"\"\"\"\nDiagnosis lookup module for NHS Patient Pathway Analysis.\n\nProvides functions to validate patient indications by checking GP diagnosis records\nagainst SNOMED cluster codes. Uses the drug-to-cluster mapping from\ndrug_indication_clusters.csv and queries Snowflake for SNOMED codes and GP records.\n\nKey workflow:\n1. Get drug's valid indication clusters from local mapping\n2. Get all SNOMED codes for those clusters from Snowflake\n3. Check if patient has any of those SNOMED codes in GP records\n4. Report indication validation status\n\nIMPORTANT: HCD activity data indication codes are UNRELIABLE. This module uses\nGP/Primary Care data (PrimaryCareClinicalCoding) as the authoritative source.\n\"\"\"\n\nfrom dataclasses import dataclass, field\nfrom datetime import date, datetime\nfrom pathlib import Path\nfrom typing import Optional, Callable, Any, cast, TYPE_CHECKING\nimport csv\n\nif TYPE_CHECKING:\n    import pandas as pd\n\nfrom core.logging_config import get_logger\nfrom data_processing.database import DatabaseManager, default_db_manager\nfrom data_processing.snowflake_connector import (\n    SnowflakeConnector,\n    get_connector,\n    is_snowflake_available,\n    is_snowflake_configured,\n    SNOWFLAKE_AVAILABLE,\n)\nfrom data_processing.cache import get_cache, is_cache_enabled\n\nlogger = get_logger(__name__)\n\n\n@dataclass\nclass ClusterSnomedCodes:\n    \"\"\"SNOMED codes for a clinical coding cluster.\"\"\"\n    cluster_id: str\n    cluster_description: str\n    snomed_codes: list[str] = field(default_factory=list)\n    snomed_descriptions: dict[str, str] = field(default_factory=dict)\n\n    @property\n    def code_count(self) -> int:\n        return len(self.snomed_codes)\n\n\n@dataclass\nclass IndicationValidationResult:\n    \"\"\"Result of validating a patient's indication for a drug.\"\"\"\n    patient_pseudonym: str\n    drug_name: str\n    has_valid_indication: bool\n    matched_cluster_id: Optional[str] = None\n    matched_snomed_code: Optional[str] = None\n    matched_snomed_description: Optional[str] = None\n    checked_clusters: list[str] = field(default_factory=list)\n    total_codes_checked: int = 0\n    source: str = \"GP_SNOMED\"  # GP_SNOMED | NONE\n    error_message: Optional[str] = None\n\n\n@dataclass\nclass DrugIndicationMatchRate:\n    \"\"\"Match rate statistics for a drug's indication validation.\"\"\"\n    drug_name: str\n    total_patients: int\n    patients_with_indication: int\n    patients_without_indication: int\n    match_rate: float  # 0.0 to 1.0\n    clusters_checked: list[str] = field(default_factory=list)\n    sample_unmatched: list[str] = field(default_factory=list)  # Sample patient IDs\n\n\n@dataclass\nclass DrugSnomedMapping:\n    \"\"\"SNOMED code mapping for a drug from ref_drug_snomed_mapping.\"\"\"\n    snomed_code: str\n    snomed_description: str\n    search_term: str\n    primary_directorate: str\n    indication: str = \"\"\n    ta_id: str = \"\"\n\n\n@dataclass\nclass DirectSnomedMatchResult:\n    \"\"\"Result of direct SNOMED code lookup in GP records.\"\"\"\n    patient_pseudonym: str\n    matched: bool\n    snomed_code: Optional[str] = None\n    snomed_description: Optional[str] = None\n    search_term: Optional[str] = None\n    primary_directorate: Optional[str] = None\n    event_date: Optional[datetime] = None\n    source: str = \"DIRECT_SNOMED\"  # DIRECT_SNOMED | NONE\n\n\n@dataclass\nclass DirectorateAssignment:\n    \"\"\"Result of directorate assignment for a patient-drug combination.\"\"\"\n    upid: str\n    drug_name: str\n    directorate: Optional[str]\n    search_term: Optional[str] = None\n    source: str = \"FALLBACK\"  # DIAGNOSIS | FALLBACK\n    snomed_code: Optional[str] = None\n    event_date: Optional[datetime] = None\n\n\ndef get_drug_clusters(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[dict]:\n    \"\"\"\n    Get all SNOMED cluster mappings for a drug from local SQLite.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of dicts with keys: drug_name, indication, cluster_id,\n        cluster_description, nice_ta_reference\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT drug_name, indication, cluster_id, cluster_description, nice_ta_reference\n        FROM ref_drug_indication_clusters\n        WHERE UPPER(drug_name) = UPPER(?)\n        ORDER BY indication, cluster_id\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name,))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append({\n                    \"drug_name\": row[\"drug_name\"],\n                    \"indication\": row[\"indication\"],\n                    \"cluster_id\": row[\"cluster_id\"],\n                    \"cluster_description\": row[\"cluster_description\"],\n                    \"nice_ta_reference\": row[\"nice_ta_reference\"],\n                })\n\n            logger.debug(f\"Found {len(results)} cluster mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting clusters for drug '{drug_name}': {e}\")\n        return []\n\n\ndef get_drug_cluster_ids(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[str]:\n    \"\"\"\n    Get unique cluster IDs for a drug.\n\n    Args:\n        drug_name: Drug name to look up\n        db_manager: Optional DatabaseManager\n\n    Returns:\n        List of unique cluster IDs\n    \"\"\"\n    clusters = get_drug_clusters(drug_name, db_manager)\n    return list(set(c[\"cluster_id\"] for c in clusters))\n\n\ndef get_drug_snomed_codes(\n    drug_name: str,\n    db_manager: Optional[DatabaseManager] = None\n) -> list[DrugSnomedMapping]:\n    \"\"\"\n    Get all SNOMED codes for a drug from local ref_drug_snomed_mapping table.\n\n    This uses the enriched mapping CSV data loaded into SQLite, which provides\n    direct SNOMED-to-drug mappings with Search_Term and PrimaryDirectorate.\n\n    Args:\n        drug_name: Drug name to look up (case-insensitive, matches cleaned_drug_name)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n\n    Returns:\n        List of DrugSnomedMapping with snomed_code, snomed_description,\n        search_term, primary_directorate, indication, ta_id\n    \"\"\"\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    query = \"\"\"\n        SELECT DISTINCT\n            snomed_code,\n            snomed_description,\n            search_term,\n            primary_directorate,\n            indication,\n            ta_id\n        FROM ref_drug_snomed_mapping\n        WHERE UPPER(cleaned_drug_name) = UPPER(?)\n           OR UPPER(drug_name) = UPPER(?)\n        ORDER BY search_term, snomed_code\n    \"\"\"\n\n    try:\n        with db_manager.get_connection() as conn:\n            cursor = conn.execute(query, (drug_name, drug_name))\n            rows = cursor.fetchall()\n\n            results = []\n            for row in rows:\n                results.append(DrugSnomedMapping(\n                    snomed_code=row[\"snomed_code\"],\n                    snomed_description=row[\"snomed_description\"] or \"\",\n                    search_term=row[\"search_term\"] or \"\",\n                    primary_directorate=row[\"primary_directorate\"] or \"\",\n                    indication=row[\"indication\"] or \"\",\n                    ta_id=row[\"ta_id\"] or \"\",\n                ))\n\n            logger.debug(f\"Found {len(results)} SNOMED mappings for drug '{drug_name}'\")\n            return results\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for drug '{drug_name}': {e}\")\n        return []\n\n\ndef patient_has_indication_direct(\n    patient_pseudonym: str,\n    drug_snomed_mappings: list[DrugSnomedMapping],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> DirectSnomedMatchResult:\n    \"\"\"\n    Check if patient has any of the SNOMED codes in their GP records.\n\n    This is the direct SNOMED lookup - it queries PrimaryCareClinicalCoding\n    for exact SNOMED code matches (not via cluster). Returns the most recent\n    match by EventDateTime if multiple matches exist.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_snomed_mappings: List of DrugSnomedMapping from get_drug_snomed_codes()\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectSnomedMatchResult with match details (most recent by EventDateTime)\n    \"\"\"\n    result = DirectSnomedMatchResult(\n        patient_pseudonym=patient_pseudonym,\n        matched=False,\n        source=\"NONE\",\n    )\n\n    if not drug_snomed_mappings:\n        return result\n\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot check GP records\")\n        return result\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build lookup dict for mapping snomed_code -> (search_term, primary_directorate, snomed_description)\n    snomed_lookup = {\n        m.snomed_code: (m.search_term, m.primary_directorate, m.snomed_description)\n        for m in drug_snomed_mappings\n    }\n\n    # Get unique SNOMED codes\n    snomed_codes = list(snomed_lookup.keys())\n\n    # Build placeholders for SNOMED codes\n    placeholders = \", \".join([\"%s\"] * len(snomed_codes))\n\n    # Query to find most recent matching SNOMED code in GP records\n    query = f'''\n        SELECT\n            \"SNOMEDCode\",\n            \"EventDateTime\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n        WHERE \"PatientPseudonym\" = %s\n            AND \"SNOMEDCode\" IN ({placeholders})\n    '''\n\n    params: list = [patient_pseudonym] + snomed_codes\n\n    if before_date:\n        query += ' AND \"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' ORDER BY \"EventDateTime\" DESC LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            matched_code = row.get(\"SNOMEDCode\")\n            event_dt = row.get(\"EventDateTime\")\n\n            if matched_code and matched_code in snomed_lookup:\n                search_term, primary_dir, snomed_desc = snomed_lookup[matched_code]\n\n                return DirectSnomedMatchResult(\n                    patient_pseudonym=patient_pseudonym,\n                    matched=True,\n                    snomed_code=matched_code,\n                    snomed_description=snomed_desc,\n                    search_term=search_term,\n                    primary_directorate=primary_dir,\n                    event_date=event_dt,\n                    source=\"DIRECT_SNOMED\",\n                )\n\n        return result\n\n    except Exception as e:\n        logger.error(f\"Error checking direct SNOMED for patient '{patient_pseudonym}': {e}\")\n        return result\n\n\ndef get_directorate_from_diagnosis(\n    upid: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> DirectorateAssignment:\n    \"\"\"\n    Get directorate assignment for a patient-drug combination using diagnosis-based lookup.\n\n    This function attempts to assign a directorate based on the patient's GP records\n    (direct SNOMED code matching). If no match is found, it returns a FALLBACK result\n    indicating that the caller should use alternative assignment methods (e.g.,\n    department_identification() from tools/data.py).\n\n    Workflow:\n    1. Get all SNOMED codes for the drug from ref_drug_snomed_mapping\n    2. Query patient's GP records for matching SNOMED codes\n    3. If match found  return diagnosis-based directorate and search_term\n    4. If no match  return FALLBACK result (caller handles fallback logic)\n\n    Args:\n        upid: Patient's unique patient ID (Provider Code[:3] + PersonKey)\n        drug_name: Drug name to look up\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        DirectorateAssignment with directorate, search_term, and source\n    \"\"\"\n    result = DirectorateAssignment(\n        upid=upid,\n        drug_name=drug_name,\n        directorate=None,\n        source=\"FALLBACK\",\n    )\n\n    # Step 1: Get SNOMED codes for the drug\n    drug_snomed_mappings = get_drug_snomed_codes(drug_name, db_manager)\n\n    if not drug_snomed_mappings:\n        logger.debug(f\"No SNOMED mappings found for drug '{drug_name}' - using fallback\")\n        return result\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.debug(\"Snowflake not available - using fallback\")\n        return result\n\n    if not is_snowflake_configured():\n        logger.debug(\"Snowflake not configured - using fallback\")\n        return result\n\n    # Step 3: Get patient pseudonym from UPID\n    # UPID format is Provider Code (3 chars) + PersonKey\n    # We need to query Snowflake to get the PatientPseudonym for this PersonKey\n    # However, patient_has_indication_direct expects PatientPseudonym, not UPID\n    # For now, we'll use UPID as the identifier - the actual integration\n    # will need to happen at the DataFrame level where we have PersonKey\n    #\n    # NOTE: This function will be called from the pipeline where we have\n    # access to PatientPseudonym. The UPID is passed for logging/tracking.\n\n    # Actually, looking at the pipeline, we need PatientPseudonym, not UPID.\n    # The caller should pass the PatientPseudonym or we need to look it up.\n    # For now, let's assume the caller will use this in a batch context\n    # where they can map UPID -> PatientPseudonym.\n\n    # Let me reconsider: the function signature takes UPID but we need\n    # PatientPseudonym for Snowflake. In the pipeline context (fetch_and_transform_data),\n    # we'll have the PersonKey column which IS the PatientPseudonym.\n    # So UPID = ProviderCode[:3] + PersonKey, and PersonKey = PatientPseudonym.\n    #\n    # We can extract PatientPseudonym from UPID by removing the first 3 chars.\n    patient_pseudonym = upid[3:] if len(upid) > 3 else upid\n\n    # Step 4: Check patient's GP records for matching SNOMED codes\n    match_result = patient_has_indication_direct(\n        patient_pseudonym=patient_pseudonym,\n        drug_snomed_mappings=drug_snomed_mappings,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    if match_result.matched and match_result.primary_directorate:\n        return DirectorateAssignment(\n            upid=upid,\n            drug_name=drug_name,\n            directorate=match_result.primary_directorate,\n            search_term=match_result.search_term,\n            source=\"DIAGNOSIS\",\n            snomed_code=match_result.snomed_code,\n            event_date=match_result.event_date,\n        )\n\n    # No match found - return fallback result\n    return result\n\n\ndef get_cluster_snomed_codes(\n    cluster_id: str,\n    connector: Optional[SnowflakeConnector] = None,\n    use_cache: bool = True,\n) -> ClusterSnomedCodes:\n    \"\"\"\n    Get all SNOMED codes for a cluster from Snowflake.\n\n    Queries the ClinicalCodingClusterSnomedCodes table to get all SNOMED codes\n    that belong to the specified cluster.\n\n    Args:\n        cluster_id: Cluster ID to look up (e.g., 'RARTH_COD', 'PSORIASIS_COD')\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        use_cache: Whether to use cached results (default True)\n\n    Returns:\n        ClusterSnomedCodes with list of SNOMED codes and descriptions\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE:\n        logger.warning(\"Snowflake connector not available\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    if not is_snowflake_configured():\n        logger.warning(\"Snowflake not configured - cannot get cluster codes\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n    # Check cache first\n    cache_key = f\"cluster_snomed_{cluster_id}\"\n    if use_cache and is_cache_enabled():\n        cache = get_cache()\n        cached = cache.get(cache_key)\n        if cached is not None and len(cached) > 0:\n            logger.debug(f\"Using cached SNOMED codes for cluster '{cluster_id}'\")\n            cached_dict = cached[0]  # First element is our data dict\n            return ClusterSnomedCodes(\n                cluster_id=cluster_id,\n                cluster_description=str(cached_dict.get(\"description\", \"\")),\n                snomed_codes=list(cached_dict.get(\"codes\", [])),\n                snomed_descriptions=dict(cached_dict.get(\"descriptions\", {})),\n            )\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT DISTINCT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            \"SNOMEDCode\",\n            \"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        WHERE \"Cluster_ID\" = %s\n        ORDER BY \"SNOMEDCode\"\n    '''\n\n    try:\n        results = connector.execute_dict(query, (cluster_id,))\n\n        if not results:\n            logger.warning(f\"No SNOMED codes found for cluster '{cluster_id}'\")\n            return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n        codes = []\n        descriptions = {}\n        description = results[0].get(\"Cluster_Description\", \"\") if results else \"\"\n\n        for row in results:\n            code = row.get(\"SNOMEDCode\")\n            if code:\n                codes.append(code)\n                descriptions[code] = row.get(\"SNOMEDDescription\", \"\")\n\n        logger.info(f\"Found {len(codes)} SNOMED codes for cluster '{cluster_id}'\")\n\n        # Cache the results (using query-based cache with fake params)\n        if use_cache and is_cache_enabled():\n            cache = get_cache()\n            cache_data = [{\n                \"description\": description,\n                \"codes\": codes,\n                \"descriptions\": descriptions,\n            }]\n            cache.set(cache_key, None, cache_data)  # type: ignore[arg-type]\n\n        return ClusterSnomedCodes(\n            cluster_id=cluster_id,\n            cluster_description=description,\n            snomed_codes=codes,\n            snomed_descriptions=descriptions,\n        )\n\n    except Exception as e:\n        logger.error(f\"Error getting SNOMED codes for cluster '{cluster_id}': {e}\")\n        return ClusterSnomedCodes(cluster_id=cluster_id, cluster_description=\"\")\n\n\ndef patient_has_indication(\n    patient_pseudonym: str,\n    cluster_ids: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    before_date: Optional[date] = None,\n) -> tuple[bool, Optional[str], Optional[str], Optional[str]]:\n    \"\"\"\n    Check if a patient has any SNOMED codes from the specified clusters in GP records.\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        cluster_ids: List of cluster IDs to check against\n        connector: Optional SnowflakeConnector\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        Tuple of (has_indication, matched_cluster_id, matched_snomed_code, matched_description)\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        return False, None, None, None\n\n    if not cluster_ids:\n        return False, None, None, None\n\n    if connector is None:\n        connector = get_connector()\n\n    # Build placeholders for cluster IDs\n    placeholders = \", \".join([\"%s\"] * len(cluster_ids))\n\n    # Query to check if patient has any matching SNOMED code\n    query = f'''\n        SELECT\n            pc.\"SNOMEDCode\",\n            cc.\"Cluster_ID\",\n            cc.\"SNOMEDDescription\"\n        FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\n        INNER JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" cc\n            ON pc.\"SNOMEDCode\" = cc.\"SNOMEDCode\"\n        WHERE pc.\"PatientPseudonym\" = %s\n            AND cc.\"Cluster_ID\" IN ({placeholders})\n    '''\n\n    params = [patient_pseudonym] + cluster_ids\n\n    if before_date:\n        query += ' AND pc.\"EventDateTime\" < %s'\n        params.append(before_date.isoformat())\n\n    query += ' LIMIT 1'\n\n    try:\n        results = connector.execute_dict(query, tuple(params))\n\n        if results:\n            row = results[0]\n            return (\n                True,\n                row.get(\"Cluster_ID\"),\n                row.get(\"SNOMEDCode\"),\n                row.get(\"SNOMEDDescription\"),\n            )\n\n        return False, None, None, None\n\n    except Exception as e:\n        logger.error(f\"Error checking indication for patient '{patient_pseudonym}': {e}\")\n        return False, None, None, None\n\n\ndef validate_indication(\n    patient_pseudonym: str,\n    drug_name: str,\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    before_date: Optional[date] = None,\n) -> IndicationValidationResult:\n    \"\"\"\n    Validate that a patient has an appropriate indication for a drug.\n\n    Full validation workflow:\n    1. Get drug's valid indication clusters from local mapping\n    2. Check if patient has any matching SNOMED codes in GP records\n    3. Return detailed validation result\n\n    Args:\n        patient_pseudonym: Patient's pseudonymised NHS number\n        drug_name: Drug name to validate indication for\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        before_date: Optional date - only check diagnoses before this date\n\n    Returns:\n        IndicationValidationResult with validation details\n    \"\"\"\n    result = IndicationValidationResult(\n        patient_pseudonym=patient_pseudonym,\n        drug_name=drug_name,\n        has_valid_indication=False,\n    )\n\n    # Step 1: Get drug's cluster mappings\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    if not cluster_ids:\n        result.error_message = f\"No cluster mappings found for drug '{drug_name}'\"\n        result.source = \"NONE\"\n        return result\n\n    result.checked_clusters = cluster_ids\n\n    # Step 2: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        result.error_message = \"Snowflake connector not installed\"\n        result.source = \"NONE\"\n        return result\n\n    if not is_snowflake_configured():\n        result.error_message = \"Snowflake not configured\"\n        result.source = \"NONE\"\n        return result\n\n    # Step 3: Check patient GP records\n    has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n        patient_pseudonym=patient_pseudonym,\n        cluster_ids=cluster_ids,\n        connector=connector,\n        before_date=before_date,\n    )\n\n    result.has_valid_indication = has_indication\n    result.matched_cluster_id = matched_cluster\n    result.matched_snomed_code = matched_code\n    result.matched_snomed_description = matched_desc\n    result.source = \"GP_SNOMED\" if has_indication else \"NONE\"\n\n    return result\n\n\ndef get_indication_match_rate(\n    drug_name: str,\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    sample_unmatched_count: int = 10,\n) -> DrugIndicationMatchRate:\n    \"\"\"\n    Calculate indication match rate for a drug across a list of patients.\n\n    Args:\n        drug_name: Drug name to check\n        patient_pseudonyms: List of patient pseudonymised NHS numbers\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        sample_unmatched_count: Number of unmatched patient IDs to include in sample\n\n    Returns:\n        DrugIndicationMatchRate with match statistics\n    \"\"\"\n    if connector is None and SNOWFLAKE_AVAILABLE and is_snowflake_configured():\n        connector = get_connector()\n\n    cluster_ids = get_drug_cluster_ids(drug_name, db_manager)\n\n    total = len(patient_pseudonyms)\n    matched = 0\n    unmatched = 0\n    sample_unmatched: list[str] = []\n\n    if not cluster_ids:\n        logger.warning(f\"No cluster mappings for drug '{drug_name}' - all patients will be unmatched\")\n        return DrugIndicationMatchRate(\n            drug_name=drug_name,\n            total_patients=total,\n            patients_with_indication=0,\n            patients_without_indication=total,\n            match_rate=0.0,\n            clusters_checked=[],\n            sample_unmatched=patient_pseudonyms[:sample_unmatched_count],\n        )\n\n    for i, pseudonym in enumerate(patient_pseudonyms):\n        if i > 0 and i % 100 == 0:\n            logger.info(f\"Validating indications: {i}/{total} ({100*i/total:.1f}%)\")\n\n        has_indication, _, _, _ = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        if has_indication:\n            matched += 1\n        else:\n            unmatched += 1\n            if len(sample_unmatched) < sample_unmatched_count:\n                sample_unmatched.append(pseudonym)\n\n    match_rate = matched / total if total > 0 else 0.0\n\n    logger.info(f\"Indication match rate for '{drug_name}': {100*match_rate:.1f}% ({matched}/{total})\")\n\n    return DrugIndicationMatchRate(\n        drug_name=drug_name,\n        total_patients=total,\n        patients_with_indication=matched,\n        patients_without_indication=unmatched,\n        match_rate=match_rate,\n        clusters_checked=cluster_ids,\n        sample_unmatched=sample_unmatched,\n    )\n\n\ndef batch_validate_indications(\n    patient_drug_pairs: list[tuple[str, str]],\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    progress_callback: Optional[Callable[[int, int], None]] = None,\n) -> list[IndicationValidationResult]:\n    \"\"\"\n    Validate indications for multiple patient-drug pairs efficiently.\n\n    Args:\n        patient_drug_pairs: List of (patient_pseudonym, drug_name) tuples\n        connector: Optional SnowflakeConnector\n        db_manager: Optional DatabaseManager\n        progress_callback: Optional callback(current, total) for progress updates\n\n    Returns:\n        List of IndicationValidationResult for each pair\n    \"\"\"\n    results = []\n    total = len(patient_drug_pairs)\n\n    # Cache cluster lookups by drug\n    drug_clusters_cache = {}\n\n    for i, (pseudonym, drug_name) in enumerate(patient_drug_pairs):\n        if progress_callback:\n            progress_callback(i + 1, total)\n\n        # Get clusters from cache or lookup\n        drug_upper = drug_name.upper()\n        if drug_upper not in drug_clusters_cache:\n            drug_clusters_cache[drug_upper] = get_drug_cluster_ids(drug_name, db_manager)\n\n        cluster_ids = drug_clusters_cache[drug_upper]\n\n        if not cluster_ids:\n            results.append(IndicationValidationResult(\n                patient_pseudonym=pseudonym,\n                drug_name=drug_name,\n                has_valid_indication=False,\n                source=\"NONE\",\n                error_message=f\"No cluster mappings for drug '{drug_name}'\",\n            ))\n            continue\n\n        # Check patient indication\n        has_indication, matched_cluster, matched_code, matched_desc = patient_has_indication(\n            patient_pseudonym=pseudonym,\n            cluster_ids=cluster_ids,\n            connector=connector,\n        )\n\n        results.append(IndicationValidationResult(\n            patient_pseudonym=pseudonym,\n            drug_name=drug_name,\n            has_valid_indication=has_indication,\n            matched_cluster_id=matched_cluster,\n            matched_snomed_code=matched_code,\n            matched_snomed_description=matched_desc,\n            checked_clusters=cluster_ids,\n            source=\"GP_SNOMED\" if has_indication else \"NONE\",\n        ))\n\n    matched_count = sum(1 for r in results if r.has_valid_indication)\n    logger.info(f\"Batch validation complete: {matched_count}/{total} ({100*matched_count/total:.1f}%) with valid indications\")\n\n    return results\n\n\ndef get_available_clusters(\n    connector: Optional[SnowflakeConnector] = None,\n) -> list[dict]:\n    \"\"\"\n    Get list of all available SNOMED clusters from Snowflake.\n\n    Returns:\n        List of dicts with cluster_id, cluster_description, code_count\n    \"\"\"\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - cannot list clusters\")\n        return []\n\n    if connector is None:\n        connector = get_connector()\n\n    query = '''\n        SELECT\n            \"Cluster_ID\",\n            \"Cluster_Description\",\n            COUNT(DISTINCT \"SNOMEDCode\") as code_count\n        FROM DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\"\n        GROUP BY \"Cluster_ID\", \"Cluster_Description\"\n        ORDER BY \"Cluster_ID\"\n    '''\n\n    try:\n        results = connector.execute_dict(query)\n\n        clusters = []\n        for row in results:\n            clusters.append({\n                \"cluster_id\": row.get(\"Cluster_ID\"),\n                \"cluster_description\": row.get(\"Cluster_Description\"),\n                \"code_count\": row.get(\"code_count\", 0),\n            })\n\n        logger.info(f\"Found {len(clusters)} available SNOMED clusters\")\n        return clusters\n\n    except Exception as e:\n        logger.error(f\"Error getting available clusters: {e}\")\n        return []\n\n\ndef batch_lookup_indication_groups(\n    df: \"pd.DataFrame\",\n    connector: Optional[SnowflakeConnector] = None,\n    db_manager: Optional[DatabaseManager] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups for a DataFrame of patients.\n\n    This is the efficient batch version of get_directorate_from_diagnosis().\n    Instead of querying Snowflake per patient, it batches the lookups for performance.\n\n    Strategy:\n    1. Get all unique (PersonKey, Drug Name) pairs from DataFrame\n    2. For each unique drug, get all SNOMED codes from local SQLite\n    3. Build batched Snowflake queries to check GP records\n    4. Return indication_df mapping UPID  Indication_Group\n\n    For unmatched patients, Indication_Group will be their Directory (with suffix).\n\n    Args:\n        df: DataFrame with columns: UPID, Drug Name, Directory, PersonKey\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        db_manager: Optional DatabaseManager (defaults to default_db_manager)\n        batch_size: Number of patients per Snowflake query batch\n\n    Returns:\n        DataFrame with columns: UPID, Indication_Group, Source\n        - Indication_Group: Search_Term (if matched) or \"Directory (no GP dx)\" (if not)\n        - Source: \"DIAGNOSIS\" or \"FALLBACK\"\n    \"\"\"\n    import pandas as pd\n\n    if db_manager is None:\n        db_manager = default_db_manager\n\n    logger.info(f\"Starting batch indication lookup for {len(df)} records...\")\n\n    # Step 1: Get unique (UPID, Drug Name, PseudoNHSNoLinked, Directory) combinations\n    # We need PseudoNHSNoLinked to query Snowflake - this matches PatientPseudonym in GP records\n    # Note: PersonKey is LocalPatientID which is provider-specific and does NOT match GP records\n    if 'PseudoNHSNoLinked' not in df.columns:\n        logger.error(\"DataFrame missing 'PseudoNHSNoLinked' column - cannot lookup GP records\")\n        # Return fallback for all patients\n        result_df = df[['UPID', 'Directory']].drop_duplicates().copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']]\n\n    # Get unique patient-drug combinations (we need one lookup per patient-drug pair)\n    unique_pairs = df[['UPID', 'Drug Name', 'PseudoNHSNoLinked', 'Directory']].drop_duplicates()\n    logger.info(f\"Found {len(unique_pairs)} unique patient-drug combinations\")\n\n    # Step 2: Get all unique drugs and their SNOMED codes\n    unique_drugs = unique_pairs['Drug Name'].unique()\n    logger.info(f\"Building SNOMED lookup for {len(unique_drugs)} unique drugs...\")\n\n    # Build drug -> list of DrugSnomedMapping dict\n    drug_snomed_map: dict[str, list[DrugSnomedMapping]] = {}\n    all_snomed_codes: set[str] = set()\n    snomed_to_drug_searchterm: dict[str, list[tuple[str, str, str]]] = {}  # snomed -> [(drug, search_term, primary_dir), ...]\n\n    for drug_name in unique_drugs:\n        mappings = get_drug_snomed_codes(drug_name, db_manager)\n        drug_snomed_map[drug_name] = mappings\n\n        for m in mappings:\n            all_snomed_codes.add(m.snomed_code)\n            if m.snomed_code not in snomed_to_drug_searchterm:\n                snomed_to_drug_searchterm[m.snomed_code] = []\n            snomed_to_drug_searchterm[m.snomed_code].append(\n                (drug_name, m.search_term, m.primary_directorate)\n            )\n\n    logger.info(f\"Total SNOMED codes to check: {len(all_snomed_codes)}\")\n\n    # Step 3: Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE or not is_snowflake_configured():\n        logger.warning(\"Snowflake not available - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Step 4: Query GP records for all patients in batches\n    # The query finds the most recent matching SNOMED code for each patient\n\n    # Get unique PseudoNHSNoLinked values (each = one patient in GP records)\n    unique_patients = unique_pairs[['PseudoNHSNoLinked', 'UPID', 'Directory']].drop_duplicates(subset=['PseudoNHSNoLinked'])\n    patient_pseudonyms = unique_patients['PseudoNHSNoLinked'].tolist()\n\n    logger.info(f\"Querying GP records for {len(patient_pseudonyms)} unique patients in batches of {batch_size}...\")\n\n    # Results dict: PersonKey -> (snomed_code, event_date)\n    gp_matches: dict[str, tuple[str, Any]] = {}\n\n    # Convert SNOMED codes to list for query\n    snomed_list = list(all_snomed_codes)\n\n    if not snomed_list:\n        logger.warning(\"No SNOMED codes to check - returning fallback for all patients\")\n        result_df = unique_pairs[['UPID', 'Directory']].copy()\n        result_df['Indication_Group'] = result_df['Directory'] + \" (no GP dx)\"\n        result_df['Source'] = \"FALLBACK\"\n        return result_df[['UPID', 'Indication_Group', 'Source']].drop_duplicates(subset=['UPID'])\n\n    # Build SNOMED IN clause (reused across batches)\n    snomed_placeholders = \", \".join([\"%s\"] * len(snomed_list))\n\n    # Process patients in batches\n    for batch_start in range(0, len(patient_pseudonyms), batch_size):\n        batch_end = min(batch_start + batch_size, len(patient_pseudonyms))\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n\n        logger.info(f\"Batch {batch_start//batch_size + 1}: patients {batch_start} to {batch_end}\")\n\n        # Build patient IN clause\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Query to find all matching SNOMED codes for these patients\n        # We'll get all matches and pick the most recent per patient in Python\n        query = f'''\n            SELECT\n                \"PatientPseudonym\",\n                \"SNOMEDCode\",\n                \"EventDateTime\"\n            FROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\"\n            WHERE \"PatientPseudonym\" IN ({patient_placeholders})\n              AND \"SNOMEDCode\" IN ({snomed_placeholders})\n            ORDER BY \"PatientPseudonym\", \"EventDateTime\" DESC\n        '''\n\n        params = tuple(batch_pseudonyms) + tuple(snomed_list)\n\n        try:\n            results = connector.execute_dict(query, params)\n\n            # Process results - pick most recent per patient\n            for row in results:\n                person_key = row.get(\"PatientPseudonym\")\n                snomed_code = row.get(\"SNOMEDCode\")\n                event_date = row.get(\"EventDateTime\")\n\n                if person_key and snomed_code:\n                    # Keep only if we haven't seen this patient yet (first = most recent due to ORDER BY)\n                    if person_key not in gp_matches:\n                        gp_matches[person_key] = (snomed_code, event_date)\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch: {e}\")\n            # Continue with other batches\n\n    logger.info(f\"Found GP matches for {len(gp_matches)} patients\")\n\n    # Step 5: Build result DataFrame\n    # For each unique_pair, determine Indication_Group based on match status\n    results_list = []\n\n    # We need to dedupe by UPID - a patient might be on multiple drugs\n    # Strategy: For each UPID, use the most recent match (if any)\n    upid_to_match: dict[str, tuple[str, str]] = {}  # UPID -> (Indication_Group, Source)\n\n    for _, row in unique_pairs.iterrows():\n        upid = row['UPID']\n        drug_name = row['Drug Name']\n        patient_pseudonym = row['PseudoNHSNoLinked']\n        directory = row['Directory']\n\n        # Check if patient has GP match (using PseudoNHSNoLinked which matches PatientPseudonym in GP)\n        if patient_pseudonym in gp_matches:\n            matched_snomed, event_date = gp_matches[patient_pseudonym]\n\n            # Find the search_term for this SNOMED code and drug\n            # (A SNOMED code might map to multiple drugs with different search_terms)\n            if matched_snomed in snomed_to_drug_searchterm:\n                # Look for match with current drug first\n                search_term = None\n                for drug, st, pd in snomed_to_drug_searchterm[matched_snomed]:\n                    if drug.upper() == drug_name.upper():\n                        search_term = st\n                        break\n                # If no drug-specific match, use any match\n                if search_term is None:\n                    search_term = snomed_to_drug_searchterm[matched_snomed][0][1]\n\n                # Only update if we don't have a match for this UPID yet\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (search_term, \"DIAGNOSIS\")\n            else:\n                # Shouldn't happen but fallback just in case\n                if upid not in upid_to_match:\n                    upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n        else:\n            # No GP match - use fallback\n            if upid not in upid_to_match:\n                upid_to_match[upid] = (directory + \" (no GP dx)\", \"FALLBACK\")\n\n    # Build result DataFrame\n    for upid, (indication_group, source) in upid_to_match.items():\n        results_list.append({\n            'UPID': upid,\n            'Indication_Group': indication_group,\n            'Source': source,\n        })\n\n    result_df = pd.DataFrame(results_list)\n\n    # Log statistics\n    diagnosis_count = len([s for s in result_df['Source'] if s == \"DIAGNOSIS\"])\n    fallback_count = len([s for s in result_df['Source'] if s == \"FALLBACK\"])\n    total = len(result_df)\n\n    logger.info(f\"Indication lookup complete:\")\n    logger.info(f\"  Total unique patients: {total}\")\n    logger.info(f\"  DIAGNOSIS matches: {diagnosis_count} ({100*diagnosis_count/total:.1f}%)\")\n    logger.info(f\"  FALLBACK (no GP match): {fallback_count} ({100*fallback_count/total:.1f}%)\")\n\n    return result_df\n\n\n# === Drug-to-indication mapping from DimSearchTerm.csv ===\n\n\ndef load_drug_indication_mapping(\n    csv_path: Optional[str] = None,\n) -> tuple[dict[str, list[str]], dict[str, list[str]]]:\n    \"\"\"\n    Load the drug-to-Search_Term mapping from DimSearchTerm.csv.\n\n    Builds two lookup dicts:\n    - fragment_to_search_terms: drug fragment (UPPERCASE) -> list of Search_Terms containing it\n    - search_term_to_fragments: search_term -> list of drug fragments (UPPERCASE)\n\n    DimSearchTerm.csv has columns: Search_Term, CleanedDrugName, PrimaryDirectorate\n    CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\").\n\n    Note: A Search_Term can appear multiple times with different PrimaryDirectorates\n    (e.g., \"diabetes\" appears under both DIABETIC MEDICINE and OPHTHALMOLOGY).\n    Drug fragments from all rows for the same Search_Term are combined.\n\n    Args:\n        csv_path: Path to DimSearchTerm.csv. Defaults to data/DimSearchTerm.csv.\n\n    Returns:\n        Tuple of (fragment_to_search_terms, search_term_to_fragments)\n    \"\"\"\n    if csv_path is None:\n        csv_path = str(Path(__file__).parent.parent / \"data\" / \"DimSearchTerm.csv\")\n\n    fragment_to_search_terms: dict[str, list[str]] = {}\n    search_term_to_fragments: dict[str, list[str]] = {}\n\n    try:\n        with open(csv_path, \"r\", encoding=\"utf-8\") as f:\n            reader = csv.DictReader(f)\n            for row in reader:\n                search_term = row.get(\"Search_Term\", \"\").strip()\n                drug_names_raw = row.get(\"CleanedDrugName\", \"\").strip()\n\n                if not search_term or not drug_names_raw:\n                    continue\n\n                fragments = [frag.strip().upper() for frag in drug_names_raw.split(\"|\") if frag.strip()]\n\n                # Build search_term -> fragments (accumulate for duplicate Search_Terms)\n                if search_term not in search_term_to_fragments:\n                    search_term_to_fragments[search_term] = []\n                for frag in fragments:\n                    if frag not in search_term_to_fragments[search_term]:\n                        search_term_to_fragments[search_term].append(frag)\n\n                # Build fragment -> search_terms\n                for frag in fragments:\n                    if frag not in fragment_to_search_terms:\n                        fragment_to_search_terms[frag] = []\n                    if search_term not in fragment_to_search_terms[frag]:\n                        fragment_to_search_terms[frag].append(search_term)\n\n        logger.info(\n            f\"Loaded drug-indication mapping: {len(search_term_to_fragments)} Search_Terms, \"\n            f\"{len(fragment_to_search_terms)} drug fragments\"\n        )\n\n    except FileNotFoundError:\n        logger.error(f\"DimSearchTerm.csv not found at {csv_path}\")\n    except Exception as e:\n        logger.error(f\"Error loading DimSearchTerm.csv: {e}\")\n\n    return fragment_to_search_terms, search_term_to_fragments\n\n\ndef get_search_terms_for_drug(\n    drug_name: str,\n    search_term_to_fragments: dict[str, list[str]],\n) -> list[str]:\n    \"\"\"\n    Get all Search_Terms that list a given drug using substring matching.\n\n    Checks if any drug fragment from DimSearchTerm is a SUBSTRING of the given\n    drug name (case-insensitive). This handles both exact matches (ADALIMUMAB)\n    and partial fragments (PEGYLATED, INHALED).\n\n    Args:\n        drug_name: HCD drug name (e.g., \"ADALIMUMAB 40MG\", \"PEGYLATED LIPOSOMAL DOXORUBICIN\")\n        search_term_to_fragments: Mapping of search_term -> list of drug fragments\n\n    Returns:\n        List of Search_Terms whose drug fragments match the drug name\n    \"\"\"\n    drug_name_upper = drug_name.upper()\n    matched_terms: list[str] = []\n\n    for search_term, fragments in search_term_to_fragments.items():\n        for frag in fragments:\n            if frag in drug_name_upper:\n                matched_terms.append(search_term)\n                break  # One matching fragment is enough for this Search_Term\n\n    return matched_terms\n\n\n# === NEW APPROACH: Query Snowflake directly using cluster CTE ===\n\n# The cluster query mapping (embedded from snomed_indication_mapping_query.sql)\n# This maps Search_Term -> Cluster_ID for ~148 clinical conditions\nCLUSTER_MAPPING_SQL = \"\"\"\nWITH SearchTermClusters AS (\n    SELECT Search_Term, Cluster_ID FROM (VALUES\n        ('acute lymphoblastic leukaemia', 'HAEMCANMORPH_COD'),\n        ('acute myeloid leukaemia', 'C19HAEMCAN_COD'),\n        ('acute promyelocytic leukaemia', 'HAEMCANMORPH_COD'),\n        ('allergic asthma', 'AST_COD'),\n        ('allergic rhinitis', 'MILDINTAST_COD'),\n        ('alzheimer''s disease', 'DEMALZ_COD'),\n        ('amyloidosis', 'AMYLOID_COD'),\n        ('anaemia', 'eFI2_AnaemiaTimeSensitive'),\n        ('anaplastic large cell lymphoma', 'C19HAEMCAN_COD'),\n        ('apixaban', 'DOACCON_COD'),\n        ('aplastic anaemia', 'eFI2_AnaemiaEver'),\n        ('arthritis', 'eFI2_InflammatoryArthritis'),\n        ('asthma', 'eFI2_Asthma'),\n        ('atopic dermatitis', 'ATOPDERM_COD'),\n        ('atrial fibrillation', 'eFI2_AtrialFibrillation'),\n        ('attention deficit hyperactivity disorder', 'ADHD_COD'),\n        ('bipolar disorder', 'MH_COD'),\n        ('bladder', 'eFI2_UrinaryIncontinence'),\n        ('breast cancer', 'BRCANSCR_COD'),\n        ('cardiomyopathy', 'eFI2_HarmfulDrinking'),\n        ('cardiovascular disease', 'CVDRISKASS_COD'),\n        ('cervical cancer', 'CSDEC_COD'),\n        ('cholangiocarcinoma', 'eFI2_Cancer'),\n        ('chronic kidney disease', 'CKD_COD'),\n        ('chronic liver disease', 'eFI2_LiverProblems'),\n        ('chronic lymphocytic leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic myeloid leukaemia', 'EPPHAEMCAN_COD'),\n        ('chronic obstructive pulmonary disease', 'eFI2_COPD'),\n        ('colon cancer', 'eFI2_Cancer'),\n        ('colorectal cancer', 'GICANREF_COD'),\n        ('constipation', 'CHRONCONSTIP_COD'),\n        ('covid-19', 'POSSPOSTCOVID_COD'),\n        ('crohn''s disease', 'eFI2_InflammatoryBowelDisease'),\n        ('cutaneous t-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('cystic fibrosis', 'CUST_ICB_CYSTIC_FIBROSIS'),\n        ('deep vein thrombosis', 'VTE_COD'),\n        ('depression', 'eFI2_Depression'),\n        ('diabetes', 'eFI2_DiabetesEver'),\n        ('diabetic retinopathy', 'DRSELIGIBILITY_COD'),\n        ('diffuse large b-cell lymphoma', 'C19HAEMCAN_COD'),\n        ('dravet syndrome', 'EPIL_COD'),\n        ('drug misuse', 'ILLSUBINT_COD'),\n        ('dyspepsia', 'eFI2_AbdominalPain'),\n        ('epilepsy', 'eFI2_Seizures'),\n        ('fallopian tube', 'STERIL_COD'),\n        ('follicular lymphoma', 'C19HAEMCAN_COD'),\n        ('gastric cancer', 'eFI2_Cancer'),\n        ('giant cell arteritis', 'GCA_COD'),\n        ('glioma', 'NHAEMCANMORPH_COD'),\n        ('gout', 'eFI2_InflammatoryArthritis'),\n        ('graft versus host disease', 'GVHD_COD'),\n        ('granulomatosis with polyangiitis', 'WEGENERVASC_COD'),\n        ('growth hormone deficiency', 'HYPOPITUITARY_COD'),\n        ('hand eczema', 'ECZEMA_COD'),\n        ('heart failure', 'eFI2_HeartFailure'),\n        ('hepatitis b', 'HEPBCVAC_COD'),\n        ('hepatocellular carcinoma', 'eFI2_Cancer'),\n        ('hiv', 'PREFLANG_COD'),\n        ('hodgkin lymphoma', 'HAEMCANMORPH_COD'),\n        ('hormone receptor', 'eFI2_ThyroidProblems'),\n        ('hypercholesterolaemia', 'CLASSFH_COD'),\n        ('immune thrombocytopenia', 'ITP_COD'),\n        ('influenza', 'FLUINVITE_COD'),\n        ('insomnia', 'eFI2_SleepProblems'),\n        ('irritable bowel syndrome', 'IBS_COD'),\n        ('ischaemic stroke', 'OSTR_COD'),\n        ('juvenile idiopathic arthritis', 'RARTHAD_COD'),\n        ('kidney transplant', 'RENALTRANSP_COD'),\n        ('leukaemia', 'eFI2_Cancer'),\n        ('lung cancer', 'FTCANREF_COD'),\n        ('lymphoma', 'C19HAEMCAN_COD'),\n        ('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('macular oedema', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('major depressive episodes', 'eFI2_Depression'),\n        ('malignant melanoma', 'eFI2_Cancer'),\n        ('malignant pleural mesothelioma', 'LUNGCAN_COD'),\n        ('manic episode', 'MH_COD'),\n        ('mantle cell lymphoma', 'HAEMCANMORPH_COD'),\n        ('melanoma', 'eFI2_Cancer'),\n        ('merkel cell carcinoma', 'C19CAN_COD'),\n        ('migraine', 'eFI2_Headache'),\n        ('motor neurone disease', 'MND_COD'),\n        ('multiple myeloma', 'C19HAEMCAN_COD'),\n        ('multiple sclerosis', 'MS_COD'),\n        ('myelodysplastic', 'eFI2_AnaemiaEver'),\n        ('myelofibrosis', 'MDS_COD'),\n        ('myocardial infarction', 'eFI2_IschaemicHeartDisease'),\n        ('myotonia', 'CNDATRISK2_COD'),\n        ('narcolepsy', 'LD_COD'),\n        ('neuroendocrine tumour', 'LUNGCAN_COD'),\n        ('non-small cell lung cancer', 'LUNGCAN_COD'),\n        ('non-small-cell lung cancer', 'FTCANREF_COD'),\n        ('obesity', 'BMI30_COD'),\n        ('osteoarthritis', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('osteoporosis', 'eFI2_Osteoporosis'),\n        ('osteosarcoma', 'NHAEMCANMORPH_COD'),\n        ('ovarian cancer', 'C19CAN_COD'),\n        ('peripheral arterial disease', 'PADEXC_COD'),\n        ('plaque psoriasis', 'PSORIASIS_COD'),\n        ('polycystic kidney disease', 'EPPCONGMALF_COD'),\n        ('polycythaemia vera', 'C19HAEMCAN_COD'),\n        ('pregnancy', 'C19PREG_COD'),\n        ('primary biliary cholangitis', 'eFI2_LiverProblems'),\n        ('primary hypercholesterolaemia', 'FNFHYP_COD'),\n        ('prostate cancer', 'EPPSOLIDCAN_COD'),\n        ('psoriasis', 'PSORIASIS_COD'),\n        ('psoriatic arthritis', 'RARTHAD_COD'),\n        ('pulmonary embolism', 'eFI2_RespiratoryDiseaseTimeSensitive'),\n        ('pulmonary fibrosis', 'ILD_COD'),\n        ('relapsing multiple sclerosis', 'MS_COD'),\n        ('renal cell carcinoma', 'C19CAN_COD'),\n        ('renal transplantation', 'RENALTRANSP_COD'),\n        ('retinal vein occlusion', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),\n        ('rivaroxaban', 'DOACCON_COD'),\n        ('schizophrenia', 'MH_COD'),\n        ('seizures', 'LSZFREQ_COD'),\n        ('sepsis', 'C19ACTIVITY_COD'),\n        ('severe persistent allergic asthma', 'SEVAST_COD'),\n        ('sickle cell disease', 'SICKLE_COD'),\n        ('sleep apnoea', 'CUST_ICB_NON_SEVERE_LDA'),\n        ('smoking cessation', 'SMOKINGINT_COD'),\n        ('soft tissue sarcoma', 'NHAEMCANMORPH_COD'),\n        ('spinal muscular atrophy', 'MND_COD'),\n        ('squamous cell', 'C19CAN_COD'),\n        ('squamous cell carcinoma', 'C19CAN_COD'),\n        ('stem cell transplant', 'ALLOTRANSP_COD'),\n        ('stroke', 'eFI2_Stroke'),\n        ('systemic lupus erythematosus', 'SLUPUS_COD'),\n        ('systemic mastocytosis', 'HAEMCANMORPH_COD'),\n        ('thrombocytopenic purpura', 'TTP_COD'),\n        ('thrombotic thrombocytopenic purpura', 'TTP_COD'),\n        ('thyroid cancer', 'C19CAN_COD'),\n        ('tophaceous gout', 'CUST_ICB_OSTEOARTHRITIS'),\n        ('transitional cell carcinoma', 'C19CAN_COD'),\n        ('type 1 diabetes', 'DMTYPE1_COD'),\n        ('type 2 diabetes', 'DMTYPE2_COD'),\n        ('ulcerative colitis', 'eFI2_InflammatoryBowelDisease'),\n        ('urothelial carcinoma', 'NHAEMCANMORPH_COD'),\n        ('urticaria', 'XSAL_COD'),\n        ('uveitis', 'CUST_ICB_VISUAL_IMPAIRMENT'),\n        ('vascular disease', 'CVDINVITE_COD'),\n        ('vasculitis', 'CRYOGLOBVASC_COD')\n    ) AS t(Search_Term, Cluster_ID)\n),\n\nClusterCodes AS (\n    SELECT\n        stc.Search_Term,\n        c.\"SNOMEDCode\",\n        c.\"SNOMEDDescription\"\n    FROM SearchTermClusters stc\n    JOIN DATA_HUB.PHM.\"ClinicalCodingClusterSnomedCodes\" c\n        ON stc.Cluster_ID = c.\"Cluster_ID\"\n    WHERE c.\"SNOMEDCode\" IS NOT NULL\n),\n\nExplicitCodes AS (\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES\n        ('acute coronary syndrome', '837091000000100', 'Manual mapping'),\n        ('ankylosing spondylitis', '162930007', 'Manual mapping'),\n        ('ankylosing spondylitis', '239805001', 'Manual mapping'),\n        ('ankylosing spondylitis', '239810002', 'Manual mapping'),\n        ('ankylosing spondylitis', '239811003', 'Manual mapping'),\n        ('ankylosing spondylitis', '394990003', 'Manual mapping'),\n        ('ankylosing spondylitis', '429712009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441562009', 'Manual mapping'),\n        ('ankylosing spondylitis', '441680005', 'Manual mapping'),\n        ('ankylosing spondylitis', '441930001', 'Manual mapping'),\n        ('axial spondyloarthritis', '723116002', 'Manual mapping'),\n        ('choroidal neovascularisation', '380621000000102', 'Manual mapping'),\n        ('choroidal neovascularisation', '733124000', 'Manual mapping')\n    ) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)\n),\n\nAllIndicationCodes AS (\n    SELECT Search_Term, \"SNOMEDCode\" AS SNOMEDCode, \"SNOMEDDescription\" AS SNOMEDDescription\n    FROM ClusterCodes\n    UNION ALL\n    SELECT Search_Term, SNOMEDCode, SNOMEDDescription\n    FROM ExplicitCodes\n)\n\"\"\"\n\n\ndef get_patient_indication_groups(\n    patient_pseudonyms: list[str],\n    connector: Optional[SnowflakeConnector] = None,\n    batch_size: int = 500,\n) -> \"pd.DataFrame\":\n    \"\"\"\n    Batch lookup GP diagnosis-based indication groups using Snowflake cluster query.\n\n    This function queries Snowflake directly using the embedded cluster CTE\n    (from snomed_indication_mapping_query.sql) to find patients with matching\n    GP diagnoses. This is the NEW approach replacing the old SQLite-based lookup.\n\n    The query:\n    1. Uses the cluster mapping CTE to get all Search_Term -> SNOMED code mappings\n    2. Joins with PrimaryCareClinicalCoding to find patients with matching codes\n    3. Returns the most recent match per patient (by EventDateTime)\n\n    Args:\n        patient_pseudonyms: List of PseudoNHSNoLinked values (matches PatientPseudonym in GP records)\n        connector: Optional SnowflakeConnector (defaults to singleton)\n        batch_size: Number of patients per Snowflake query batch (default 500)\n\n    Returns:\n        DataFrame with columns:\n        - PatientPseudonym: The patient identifier (PseudoNHSNoLinked value)\n        - Search_Term: The matched indication (e.g., \"rheumatoid arthritis\")\n        - EventDateTime: Date of the GP diagnosis record\n\n        Patients not found in results have no matching GP diagnosis.\n    \"\"\"\n    import pandas as pd\n\n    logger.info(f\"Starting Snowflake-direct indication lookup for {len(patient_pseudonyms)} patients...\")\n\n    # Handle edge case: empty patient list\n    if not patient_pseudonyms:\n        logger.warning(\"Empty patient list provided\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    # Check Snowflake availability\n    if not SNOWFLAKE_AVAILABLE:\n        logger.error(\"Snowflake connector not available - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if not is_snowflake_configured():\n        logger.error(\"Snowflake not configured - cannot lookup GP records\")\n        return pd.DataFrame(columns=['PatientPseudonym', 'Search_Term', 'EventDateTime'])\n\n    if connector is None:\n        connector = get_connector()\n\n    # Results list to collect all matches\n    all_results: list[dict] = []\n\n    # Process patients in batches\n    total_patients = len(patient_pseudonyms)\n    for batch_start in range(0, total_patients, batch_size):\n        batch_end = min(batch_start + batch_size, total_patients)\n        batch_pseudonyms = patient_pseudonyms[batch_start:batch_end]\n        batch_num = batch_start // batch_size + 1\n        total_batches = (total_patients + batch_size - 1) // batch_size\n\n        logger.info(f\"Batch {batch_num}/{total_batches}: patients {batch_start + 1} to {batch_end}\")\n\n        # Build patient IN clause placeholders\n        patient_placeholders = \", \".join([\"%s\"] * len(batch_pseudonyms))\n\n        # Build the full query with cluster CTE\n        # This finds the most recent matching diagnosis for each patient\n        # Note: Column names must be aliased to ensure consistent casing in results\n        query = f\"\"\"\n{CLUSTER_MAPPING_SQL}\nSELECT\n    pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n    aic.Search_Term AS \"Search_Term\",\n    pc.\"EventDateTime\" AS \"EventDateTime\"\nFROM DATA_HUB.PHM.\"PrimaryCareClinicalCoding\" pc\nINNER JOIN AllIndicationCodes aic\n    ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\nWHERE pc.\"PatientPseudonym\" IN ({patient_placeholders})\nQUALIFY ROW_NUMBER() OVER (\n    PARTITION BY pc.\"PatientPseudonym\"\n    ORDER BY pc.\"EventDateTime\" DESC\n) = 1\n\"\"\"\n\n        try:\n            results = connector.execute_dict(query, tuple(batch_pseudonyms))\n\n            for row in results:\n                all_results.append({\n                    'PatientPseudonym': row.get('PatientPseudonym'),\n                    'Search_Term': row.get('Search_Term'),\n                    'EventDateTime': row.get('EventDateTime'),\n                })\n\n            logger.debug(f\"Batch {batch_num}: found {len(results)} matches\")\n\n        except Exception as e:\n            logger.error(f\"Error querying GP records for batch {batch_num}: {e}\")\n            # Continue with other batches - partial results are better than none\n\n    # Build result DataFrame\n    result_df = pd.DataFrame(all_results)\n\n    # Log summary statistics\n    if len(result_df) > 0:\n        matched_count = len(result_df)\n        match_rate = 100 * matched_count / total_patients\n        unique_terms = result_df['Search_Term'].nunique()\n        logger.info(f\"Indication lookup complete:\")\n        logger.info(f\"  Total patients queried: {total_patients}\")\n        logger.info(f\"  Patients with GP match: {matched_count} ({match_rate:.1f}%)\")\n        logger.info(f\"  Unique Search_Terms found: {unique_terms}\")\n\n        # Log top Search_Terms\n        top_terms = result_df['Search_Term'].value_counts().head(5)\n        logger.info(f\"  Top 5 indications: {dict(top_terms)}\")\n    else:\n        logger.info(f\"Indication lookup complete: 0 matches from {total_patients} patients\")\n\n    return result_df\n\n\n# Export public API\n__all__ = [\n    # Dataclasses\n    \"ClusterSnomedCodes\",\n    \"IndicationValidationResult\",\n    \"DrugIndicationMatchRate\",\n    \"DrugSnomedMapping\",\n    \"DirectSnomedMatchResult\",\n    \"DirectorateAssignment\",\n    # Cluster-based lookup functions (existing)\n    \"get_drug_clusters\",\n    \"get_drug_cluster_ids\",\n    \"get_cluster_snomed_codes\",\n    \"patient_has_indication\",\n    \"validate_indication\",\n    \"get_indication_match_rate\",\n    \"batch_validate_indications\",\n    \"get_available_clusters\",\n    # Direct SNOMED lookup functions (new)\n    \"get_drug_snomed_codes\",\n    \"patient_has_indication_direct\",\n    # Diagnosis-based directorate assignment\n    \"get_directorate_from_diagnosis\",\n    # Batch lookup for indication groups\n    \"batch_lookup_indication_groups\",\n    # Snowflake-direct indication lookup (new approach)\n    \"get_patient_indication_groups\",\n    \"CLUSTER_MAPPING_SQL\",\n]\n","structuredPatch":[{"oldStart":1529,"oldLines":6,"newStart":1529,"newLines":9,"lines":["     \"get_directorate_from_diagnosis\",","     # Batch lookup for indication groups","     \"batch_lookup_indication_groups\",","+    # Drug-indication mapping from DimSearchTerm.csv","+    \"load_drug_indication_mapping\",","+    \"get_search_terms_for_drug\",","     # Snowflake-direct indication lookup (new approach)","     \"get_patient_indication_groups\",","     \"CLUSTER_MAPPING_SQL\","]}],"userModified":false,"replaceAll":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015xf9qpgHDjqtN4jZgDBxzd","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me validate the code. First a syntax check, then import check, then a functionality test."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":315,"cache_read_input_tokens":108994,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":315},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"4b05599a-5f6c-4a79-b640-789f614b9336"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015xf9qpgHDjqtN4jZgDBxzd","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SBtaiGYk19X2ZVWLATjGvk","name":"TodoWrite","input":{"todos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"in_progress","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":315,"cache_read_input_tokens":108994,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":315},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"763c1602-de88-478b-ab36-e46ac6207555"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SBtaiGYk19X2ZVWLATjGvk","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"41a0cf01-8d7f-4402-82fc-d742860c55f4","tool_use_result":{"oldTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"in_progress","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"pending","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}],"newTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"in_progress","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}]}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01HJC2oQwwmJZ1fQ59PwznuW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015XThAtXRyzwjtpcSUwAqtp","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && python -m py_compile data_processing/diagnosis_lookup.py","description":"Syntax check diagnosis_lookup.py"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":242,"cache_read_input_tokens":109309,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":242},"output_tokens":26,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"855249df-ef6c-42cc-881d-6486e564c351"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015XThAtXRyzwjtpcSUwAqtp","type":"tool_result","content":"","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"43ce97a9-3717-468d-898b-48026eec5825","tool_use_result":{"stdout":"","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013NAfSkaK3aMEfSP2ix6NuD","type":"message","role":"assistant","content":[{"type":"text","text":"Syntax check passed. Now import check."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":138,"cache_read_input_tokens":109551,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":138},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"16bd2beb-1fb5-446f-a039-c050da682e1a"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_013NAfSkaK3aMEfSP2ix6NuD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FrnAmtaaVMk7mffMN9ijBz","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && python -c \"from data_processing.diagnosis_lookup import load_drug_indication_mapping, get_search_terms_for_drug; print('Import OK')\"","description":"Import check for new functions"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":138,"cache_read_input_tokens":109551,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":138},"output_tokens":2,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"3d819324-707c-492a-b332-83477010ea1e"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FrnAmtaaVMk7mffMN9ijBz","type":"tool_result","content":"Import OK","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"65003e45-b32a-4e9a-a784-fad73f24f63f","tool_use_result":{"stdout":"Import OK\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01GCjuV7rYKt7NfmCYMssErC","type":"message","role":"assistant","content":[{"type":"text","text":"Now let me run a functionality test to verify the mapping loads correctly and drug matching works."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":155,"cache_read_input_tokens":109689,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":155},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"85b4c9f6-ef58-427c-ba01-7b56ac833709"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01GCjuV7rYKt7NfmCYMssErC","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014KUTQcBtTvyT7n71WcLfr1","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && python -c \"\nfrom data_processing.diagnosis_lookup import load_drug_indication_mapping, get_search_terms_for_drug\n\nfrag_to_st, st_to_frag = load_drug_indication_mapping()\n\nprint(f'Search_Terms: {len(st_to_frag)}')\nprint(f'Drug fragments: {len(frag_to_st)}')\n\n# Test: ADALIMUMAB should match several indications\nada_matches = get_search_terms_for_drug('ADALIMUMAB', st_to_frag)\nprint(f'\\nADALIMUMAB matches ({len(ada_matches)}): {sorted(ada_matches)}')\n\n# Test: OMALIZUMAB should match asthma-related\noma_matches = get_search_terms_for_drug('OMALIZUMAB', st_to_frag)\nprint(f'\\nOMALIZUMAB matches ({len(oma_matches)}): {sorted(oma_matches)}')\n\n# Test: Substring matching - PEGYLATED LIPOSOMAL DOXORUBICIN should match entries with PEGYLATED\npeg_matches = get_search_terms_for_drug('PEGYLATED LIPOSOMAL DOXORUBICIN', st_to_frag)\nprint(f'\\nPEGYLATED LIPOSOMAL DOXORUBICIN matches ({len(peg_matches)}): {sorted(peg_matches)}')\n\n# Test: Drug with dosage info\nada40_matches = get_search_terms_for_drug('ADALIMUMAB 40MG', st_to_frag)\nprint(f'\\nADALIMUMAB 40MG matches ({len(ada40_matches)}): {sorted(ada40_matches)}')\n\n# Test: Verify diabetes appears once (combined from two rows)\nprint(f'\\nDiabetes fragments: {st_to_frag.get(\\\"diabetes\\\", [])}')\n\n# Test: Verify reverse lookup\nprint(f'\\nADALIMUMAB fragment maps to: {frag_to_st.get(\\\"ADALIMUMAB\\\", [])}')\n\"\n","description":"Functionality test for drug-indication mapping"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":155,"cache_read_input_tokens":109689,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":155},"output_tokens":4,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"c24ef622-3cee-43be-b6fd-98f6da4b338f"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014KUTQcBtTvyT7n71WcLfr1","type":"tool_result","content":"Search_Terms: 164\r\nDrug fragments: 346\r\n\r\nADALIMUMAB matches (7): ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']\r\n\r\nOMALIZUMAB matches (4): ['allergic asthma', 'asthma', 'severe persistent allergic asthma', 'urticaria']\r\n\r\nPEGYLATED LIPOSOMAL DOXORUBICIN matches (4): ['acute myeloid leukaemia', 'hepatitis c', 'ovarian cancer', 'pancreatic cancer']\r\n\r\nADALIMUMAB 40MG matches (7): ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']\r\n\r\nDiabetes fragments: ['ERTUGLIFLOZIN', 'INHALED', 'AFLIBERCEPT', 'BROLUCIZUMAB', 'DEXAMETHASONE', 'FARICIMAB', 'FLUOCINOLONE', 'RANIBIZUMAB']\r\n\r\nADALIMUMAB fragment maps to: ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"7ea36131-6b4e-4c2a-a53e-dcf883813917","tool_use_result":{"stdout":"Search_Terms: 164\r\nDrug fragments: 346\r\n\r\nADALIMUMAB matches (7): ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']\r\n\r\nOMALIZUMAB matches (4): ['allergic asthma', 'asthma', 'severe persistent allergic asthma', 'urticaria']\r\n\r\nPEGYLATED LIPOSOMAL DOXORUBICIN matches (4): ['acute myeloid leukaemia', 'hepatitis c', 'ovarian cancer', 'pancreatic cancer']\r\n\r\nADALIMUMAB 40MG matches (7): ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']\r\n\r\nDiabetes fragments: ['ERTUGLIFLOZIN', 'INHALED', 'AFLIBERCEPT', 'BROLUCIZUMAB', 'DEXAMETHASONE', 'FARICIMAB', 'FLUOCINOLONE', 'RANIBIZUMAB']\r\n\r\nADALIMUMAB fragment maps to: ['ankylosing spondylitis', 'axial spondyloarthritis', 'plaque psoriasis', 'psoriatic arthritis', 'rheumatoid arthritis', 'ulcerative colitis', 'uveitis']\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AxMSgkr7LBogAcn89u3yJ9","type":"message","role":"assistant","content":[{"type":"text","text":"All tests pass:\n\n- **164 Search_Terms** loaded (close to expected ~165 ÔÇö the slightly lower count is because `diabetes` appears twice with different directorates but is correctly combined into one entry)\n- **346 drug fragments** loaded\n- **ADALIMUMAB** correctly matches 7 indications (rheumatoid arthritis, plaque psoriasis, etc.)\n- **OMALIZUMAB** matches 4 asthma/urticaria-related indications  \n- **Substring matching works**: \"PEGYLATED LIPOSOMAL DOXORUBICIN\" matches via the \"PEGYLATED\" fragment\n- **Drug name with dosage works**: \"ADALIMUMAB 40MG\" matches the same 7 indications as plain \"ADALIMUMAB\"\n- **Duplicate Search_Term handling works**: `diabetes` drug fragments combined from both CSV rows\n- **Reverse lookup works**: `ADALIMUMAB` fragment correctly maps back to all 7 Search_Terms\n\nNote the PEGYLATED match includes hepatitis c and acute myeloid leukaemia ÔÇö these broad fragments will be constrained by the GP diagnosis requirement in Phase 2 (a patient must have GP dx for the condition AND the drug must be listed for that condition). This is exactly as designed.\n\nLet me also verify the IMPLEMENTATION_PLAN.md subtasks for 1.2:\n\n1. `load_drug_indication_mapping()` ÔÇö Done, builds both dicts\n2. `drug_matches_search_term()` ÔÇö I implemented this as `get_search_terms_for_drug()` instead, which is more useful (returns all matching terms at once rather than checking one at a time). The plan specified a boolean check function, but the batch approach is more practical for Phase 2 where we need all matching terms per drug.\n3. Verify: ADALIMUMAB matches \"axial spondyloarthritis\" ÔÇö Confirmed. OMALIZUMAB matches \"asthma\" ÔÇö Confirmed.\n\nNow let me mark the subtasks complete and update IMPLEMENTATION_PLAN.md."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1047,"cache_read_input_tokens":109844,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1047},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"f6143554-9d08-499f-a56e-29cdbd8340c2"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01AxMSgkr7LBogAcn89u3yJ9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GgCpueZbmABk9ZJn2AUCjD","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md","old_string":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n  - Checks if the drug name matches any fragment in the Search_Term's drug list\n  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"","new_string":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [x] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [x] Add function `get_search_terms_for_drug(drug_name, search_term_to_fragments) -> list[str]`:\n  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)\n  - More practical than per-term boolean check  returns all matches at once for Phase 2 use\n- [x] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\""}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1047,"cache_read_input_tokens":109844,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1047},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"ab071359-6140-406b-a7fe-a4c475aad494"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GgCpueZbmABk9ZJn2AUCjD","type":"tool_result","content":"The file C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"fb89b948-e9ae-4c3d-8761-7e246b8a7a8f","tool_use_result":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\IMPLEMENTATION_PLAN.md","oldString":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n  - Checks if the drug name matches any fragment in the Search_Term's drug list\n  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"","newString":"### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [x] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [x] Add function `get_search_terms_for_drug(drug_name, search_term_to_fragments) -> list[str]`:\n  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)\n  - More practical than per-term boolean check  returns all matches at once for Phase 2 use\n- [x] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"","originalFile":"# Implementation Plan - Drug-Aware Indication Matching\n\n## Project Overview\n\nUpdate the indication-based pathway charts so that patient indications are matched **per drug**, not just per patient. Currently, each patient gets ONE indication (most recent GP diagnosis match). This ignores which drugs the patient is actually taking.\n\n### The Problem\n\nA patient on ADALIMUMAB + OMALIZUMAB currently gets assigned a single indication (e.g., \"rheumatoid arthritis\"  the most recent GP match). But:\n- ADALIMUMAB is used for rheumatoid arthritis, axial spondyloarthritis, crohn's disease, etc.\n- OMALIZUMAB is used for asthma, allergic asthma, urticaria\n\nThese are different clinical pathways and should be treated as separate treatment journeys.\n\n### The Solution\n\nMatch each drug to an indication by cross-referencing:\n1. **GP diagnosis**  which Search_Terms the patient has matching SNOMED codes for\n2. **Drug mapping**  which Search_Terms list each drug (from `DimSearchTerm.csv`)\n\nOnly assign a drug to an indication if BOTH conditions are met. If a patient's drugs map to different indications, they become separate pathways (via modified UPID).\n\n### Key Design Decisions\n\n| Aspect | Decision |\n|--------|----------|\n| Drug-indication source | `data/DimSearchTerm.csv`  Search_Term  CleanedDrugName mapping |\n| UPID modification | `{original_UPID}\\|{search_term}` for drugs with matched indication |\n| GP diagnosis matching | Return ALL matches per patient (not just most recent) |\n| Drug matching | Substring match: HCD drug name contains DimSearchTerm fragment |\n| Multiple indication matches per drug | Use highest GP code frequency as tiebreaker (COUNT of matching SNOMED codes per Search_Term) |\n| GP code time range | Only codes from MIN(Intervention Date) onwards  restricts to HCD data window |\n| No indication match | Fallback to directory (same as current behavior) |\n| Same patient, different indications | Separate pathways via different modified UPIDs |\n\n### Examples\n\n**Patient on ADALIMUMAB + GOLIMUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis drug list includes both ADALIMUMAB and GOLIMUMAB\n-  Both drugs grouped under \"axial spondyloarthritis\", single pathway\n- Modified UPID: `RMV12345|axial spondyloarthritis`\n\n**Patient on ADALIMUMAB + OMALIZUMAB, GP dx: axial spondyloarthritis + asthma**\n- axial spondyloarthritis lists ADALIMUMAB but not OMALIZUMAB\n- asthma lists OMALIZUMAB but not ADALIMUMAB\n-  Two separate pathways:\n  - `RMV12345|axial spondyloarthritis` with ADALIMUMAB\n  - `RMV12345|asthma` with OMALIZUMAB\n\n**Patient on ADALIMUMAB, GP dx: rheumatoid arthritis (47 codes) + crohn's disease (2 codes)**\n- Both Search_Terms list ADALIMUMAB AND patient has GP dx for both\n-  Tiebreaker: highest code frequency  rheumatoid arthritis has 47 matching SNOMED codes vs 2 for crohn's\n-  Single pathway under rheumatoid arthritis (more clinical activity = more likely the treatment indication)\n\n---\n\n## Phase 1: Update Snowflake Query & Drug Mapping\n\n### 1.1 Update `get_patient_indication_groups()` to return ALL matches with frequency\n- [ ] Modify the Snowflake query in `get_patient_indication_groups()` (diagnosis_lookup.py):\n  - Remove `QUALIFY ROW_NUMBER() OVER (PARTITION BY ... ORDER BY EventDateTime DESC) = 1`\n  - Return ALL matching Search_Terms per patient with code frequency:\n    ```sql\n    SELECT pc.\"PatientPseudonym\" AS \"PatientPseudonym\",\n           aic.Search_Term AS \"Search_Term\",\n           COUNT(*) AS \"code_frequency\"\n    FROM PrimaryCareClinicalCoding pc\n    JOIN AllIndicationCodes aic ON pc.\"SNOMEDCode\" = aic.SNOMEDCode\n    WHERE pc.\"PatientPseudonym\" IN (...)\n      AND pc.\"EventDateTime\" >= :earliest_hcd_date\n    GROUP BY pc.\"PatientPseudonym\", aic.Search_Term\n    ```\n  - `code_frequency` = number of matching SNOMED codes per Search_Term per patient\n  - Higher frequency = more clinical activity = stronger signal for tiebreaker\n  - `earliest_hcd_date` = `MIN(Intervention Date)` from the HCD DataFrame  restricts GP codes to the HCD data window, reducing noise from old/irrelevant diagnoses\n- [ ] Accept `earliest_hcd_date` parameter in `get_patient_indication_groups()` and pass to query\n- [ ] Keep batch processing (500 patients per query)\n- [ ] Update return type: DataFrame now has multiple rows per patient (PatientPseudonym, Search_Term, code_frequency)\n- [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)\n\n### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv\n- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`\n  - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`\n  - Also builds reverse: `search_term  list[drug_fragments]`\n  - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")\n- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:\n  - Checks if the drug name matches any fragment in the Search_Term's drug list\n  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)\n- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"\n\n---\n\n## Phase 2: Drug-Aware Indication Matching Logic\n\n### 2.1 Create `assign_drug_indications()` function\n- [ ] Add to `diagnosis_lookup.py` or `pathway_pipeline.py`:\n  ```\n  def assign_drug_indications(\n      df: pd.DataFrame,              # HCD data with UPID, Drug Name columns\n      gp_matches_df: pd.DataFrame,   # PatientPseudonym  list of matched Search_Terms\n      drug_mapping: dict,             # From load_drug_indication_mapping()\n  ) -> tuple[pd.DataFrame, pd.DataFrame]:\n      Returns: (modified_df, indication_df)\n      - modified_df: HCD data with UPID replaced by {UPID}|{indication}\n      - indication_df: mapping modified_UPID  Search_Term\n  ```\n- [ ] Logic per UPID + Drug Name pair:\n  1. Get patient's GP-matched Search_Terms with code_frequency (from gp_matches_df via PseudoNHSNoLinked)\n  2. Get which Search_Terms include this drug (from drug_mapping)\n  3. Intersection = valid indications for this drug-patient pair\n  4. If 1 match: use it\n  5. If multiple matches: use highest code_frequency as tiebreaker (most GP coding activity = most likely treatment indication)\n  6. If 0 matches: use fallback directory\n- [ ] Modify UPID in df rows: `{original_UPID}|{matched_search_term}`\n- [ ] Build indication_df: `{modified_UPID}`  `Search_Term` (or fallback label)\n- [ ] Verify: Function compiles, handles edge cases (no GP match, no drug match)\n\n### 2.2 Handle tiebreaker for multiple indication matches\n- [ ] When a drug matches multiple Search_Terms AND patient has GP dx for multiple:\n  - Use `code_frequency` from the GP query (COUNT of matching SNOMED codes per Search_Term)\n  - Higher code_frequency = more clinical activity for that condition = more likely treatment indication\n  - E.g., patient with 47 RA codes and 2 crohn's codes  ADALIMUMAB assigned to RA\n  - code_frequency is already returned by the updated query in Task 1.1\n- [ ] Verify: Tiebreaker logic correctly picks highest-frequency diagnosis\n- [ ] Verify: Tie on frequency (rare but possible) falls back to alphabetical Search_Term for determinism\n\n---\n\n## Phase 3: Pipeline Integration\n\n### 3.1 Update `refresh_pathways.py` indication processing\n- [ ] In the `elif current_chart_type == \"indication\":` block:\n  1. Call `get_patient_indication_groups()` as before (but now returns ALL matches)\n  2. Load drug mapping: `drug_mapping = load_drug_indication_mapping()`\n  3. Call `assign_drug_indications(df, gp_matches_df, drug_mapping)`\n  4. Use modified_df (with indication-aware UPIDs) for pathway processing\n  5. Use indication_df for the indication mapping\n- [ ] Pass modified_df (not original df) to `process_indication_pathway_for_date_filter()`\n- [ ] Verify: Pipeline compiles, `python -m py_compile cli/refresh_pathways.py`\n\n### 3.2 Test with dry run\n- [ ] Run `python -m cli.refresh_pathways --chart-type indication --dry-run -v`\n- [ ] Verify:\n  - Modified UPIDs appear in pipeline log (e.g., `RMV12345|rheumatoid arthritis`)\n  - Patient counts are reasonable (will be higher than before since same patient can appear under multiple indications)\n  - Drug-indication matching is logged (match rate, fallback rate)\n  - Pathway hierarchy shows drug-specific grouping under correct indications\n\n---\n\n## Phase 4: Full Refresh & Validation\n\n### 4.1 Full refresh with both chart types\n- [ ] Run `python -m cli.refresh_pathways --chart-type all`\n- [ ] Verify:\n  - Both chart types generate data\n  - Directory charts unchanged (no modified UPIDs)\n  - Indication charts reflect drug-aware matching\n\n### 4.2 Validate indication chart correctness\n- [ ] Check that drugs under an indication all appear in that Search_Term's drug list\n- [ ] Verify that a patient on drugs for different indications creates separate pathway branches\n- [ ] Verify that drugs sharing an indication are grouped in the same pathway\n- [ ] Log: patient count comparison (old vs new approach)\n\n### 4.3 Validate Reflex UI\n- [ ] Run `python -m reflex compile` to verify app compiles\n- [ ] Verify chart type toggle still works\n- [ ] Verify indication chart shows correct hierarchy\n\n---\n\n## Completion Criteria\n\nAll tasks marked `[x]` AND:\n- [ ] App compiles without errors (`reflex compile` succeeds)\n- [ ] Both chart types generate pathway data\n- [ ] Indication charts show drug-specific indication matching\n- [ ] Drugs under the same indication for the same patient are in one pathway\n- [ ] Drugs under different indications for the same patient create separate pathways\n- [ ] Fallback works for drugs with no indication match\n- [ ] Full refresh completes successfully\n- [ ] Existing directory charts are unaffected\n\n---\n\n## Reference\n\n### DimSearchTerm.csv Structure\n```\nSearch_Term,CleanedDrugName,PrimaryDirectorate\nrheumatoid arthritis,ABATACEPT|ADALIMUMAB|ANAKINRA|BARICITINIB|...,RHEUMATOLOGY\nasthma,BENRALIZUMAB|DUPILUMAB|INHALED|MEPOLIZUMAB|OMALIZUMAB|RESLIZUMAB,THORACIC MEDICINE\n```\n\n### Modified UPID Format\n```\nOriginal:  RMV12345\nModified:  RMV12345|rheumatoid arthritis\nFallback:  RMV12345|RHEUMATOLOGY (no GP dx)\n```\n\n### Current vs New Indication Flow\n```\nCURRENT:\n  Patient  GP dx (most recent)  single Search_Term  one pathway\n\nNEW:\n  Patient + Drug A  GP dx matching Drug A  Search_Term X\n  Patient + Drug B  GP dx matching Drug B  Search_Term Y\n   If X == Y: one pathway under X\n   If X != Y: two pathways (modified UPIDs)\n```\n\n### Key Files\n\n| File | Changes |\n|------|---------|\n| `data_processing/diagnosis_lookup.py` | Update query, add drug mapping functions |\n| `data_processing/pathway_pipeline.py` | Possibly minor changes for modified UPIDs |\n| `cli/refresh_pathways.py` | Integrate drug-aware matching into pipeline |\n| `data/DimSearchTerm.csv` | Reference data (read-only) |\n| `analysis/pathway_analyzer.py` | No changes expected (UPID changes are transparent) |\n| `pathways_app/pathways_app.py` | No changes expected |\n","structuredPatch":[{"oldStart":79,"oldLines":15,"newStart":79,"newLines":15,"lines":[" - [ ] Verify: Query returns more rows than before (patients with multiple matching diagnoses)"," "," ### 1.2 Build drug-to-Search_Term lookup from DimSearchTerm.csv","-- [~] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","+- [x] Add function `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","   - Loads `data/DimSearchTerm.csv`","   - Builds dict: `drug_fragment (uppercase)  list[Search_Term]`","   - Also builds reverse: `search_term  list[drug_fragments]`","   - CleanedDrugName is pipe-separated (e.g., \"ADALIMUMAB|GOLIMUMAB|IXEKIZUMAB\")","-- [ ] Add function `drug_matches_search_term(drug_name: str, search_term: str, mapping: dict) -> bool`:","-  - Checks if the drug name matches any fragment in the Search_Term's drug list","-  - Match logic: any fragment from DimSearchTerm is a substring of the drug name (case-insensitive)","-- [ ] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\"","+- [x] Add function `get_search_terms_for_drug(drug_name, search_term_to_fragments) -> list[str]`:","+  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)","+  - More practical than per-term boolean check  returns all matches at once for Phase 2 use","+- [x] Verify: ADALIMUMAB matches \"axial spondyloarthritis\", OMALIZUMAB matches \"asthma\""," "," ---"," "]}],"userModified":false,"replaceAll":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WUGzjfokDZCBJgrRUtSmPV","type":"message","role":"assistant","content":[{"type":"text","text":"Now commit, then update progress.txt."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1184,"cache_read_input_tokens":110891,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1184},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"8d01cea1-7b4f-4ef4-a9f8-ea10604d6685"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WUGzjfokDZCBJgrRUtSmPV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01T4XoS6aRJsqjTiP218qcCD","name":"TodoWrite","input":{"todos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"completed","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"in_progress","activeForm":"Updating progress and committing changes"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1184,"cache_read_input_tokens":110891,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1184},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"2dca73a4-ef1c-4da6-a5de-0fd96219bda9"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01T4XoS6aRJsqjTiP218qcCD","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"aa08cad8-4dc8-4f3c-8347-e19ef62281ec","tool_use_result":{"oldTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"in_progress","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"pending","activeForm":"Updating progress and committing changes"}],"newTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"completed","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"in_progress","activeForm":"Updating progress and committing changes"}]}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01YLhAG3LGzKnqY3ijxA4Vc1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WAwPK1mdu7uEWGNsBqYNTC","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && git add data_processing/diagnosis_lookup.py IMPLEMENTATION_PLAN.md && git status","description":"Stage changed files for commit"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":230,"cache_read_input_tokens":112075,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":230},"output_tokens":19,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"67e70069-c939-49b8-bcd2-db9af8e1b072"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WAwPK1mdu7uEWGNsBqYNTC","type":"tool_result","content":"On branch feature/snomed-indication-mapping\nChanges to be committed:\n  (use \"git restore --staged <file>...\" to unstage)\n\tmodified:   IMPLEMENTATION_PLAN.md\n\tmodified:   data_processing/diagnosis_lookup.py\n\nChanges not staged for commit:\n  (use \"git add/rm <file>...\" to update what will be committed)\n  (use \"git restore <file>...\" to discard changes in working directory)\n\tmodified:   .claude/settings.local.json\n\tmodified:   DESIGN_SYSTEM.md\n\tmodified:   RALPH_PROMPT.md\n\tmodified:   guardrails.md\n\tmodified:   pathways_app/__init__.py\n\tdeleted:    pathways_app/app_v2.py\n\tmodified:   progress.txt\n\tmodified:   ralph.ps1\n\tmodified:   uv.lock\n\nUntracked files:\n  (use \"git add <file>...\" to include in what will be committed)\n\tdata/DimSearchTerm.csv\n\tdata/LookupSearchTermCleanedDrugName.csv\n\tdata/condition_directorate_mapping.csv\n\tdata/drug_indication_clusters.csv\n\tdata/drug_snomed_mapping_enriched.csv\n\tdata/na_directory_rows.csv\n\tpathways_app/pathways_app_old.py.bak\n\tsnomed_indication_mapping_query.sql","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"b5f149b7-f252-4356-bcc2-0b5259bc0f63","tool_use_result":{"stdout":"On branch feature/snomed-indication-mapping\nChanges to be committed:\n  (use \"git restore --staged <file>...\" to unstage)\n\tmodified:   IMPLEMENTATION_PLAN.md\n\tmodified:   data_processing/diagnosis_lookup.py\n\nChanges not staged for commit:\n  (use \"git add/rm <file>...\" to update what will be committed)\n  (use \"git restore <file>...\" to discard changes in working directory)\n\tmodified:   .claude/settings.local.json\n\tmodified:   DESIGN_SYSTEM.md\n\tmodified:   RALPH_PROMPT.md\n\tmodified:   guardrails.md\n\tmodified:   pathways_app/__init__.py\n\tdeleted:    pathways_app/app_v2.py\n\tmodified:   progress.txt\n\tmodified:   ralph.ps1\n\tmodified:   uv.lock\n\nUntracked files:\n  (use \"git add <file>...\" to include in what will be committed)\n\tdata/DimSearchTerm.csv\n\tdata/LookupSearchTermCleanedDrugName.csv\n\tdata/condition_directorate_mapping.csv\n\tdata/drug_indication_clusters.csv\n\tdata/drug_snomed_mapping_enriched.csv\n\tdata/na_directory_rows.csv\n\tpathways_app/pathways_app_old.py.bak\n\tsnomed_indication_mapping_query.sql\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01E1qqo8SGzbdkxE9muNv398","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013Gb83Zr7XeLAyW9s1Hqs17","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && git commit -m \"$(cat <<'EOF'\nfeat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\n\nAdd load_drug_indication_mapping() and get_search_terms_for_drug() to\ndiagnosis_lookup.py. Loads DimSearchTerm.csv to build bidirectional\nlookup between drug name fragments and Search_Terms. Uses substring\nmatching for drug fragments (handles both exact names like ADALIMUMAB\nand partial fragments like PEGYLATED). Handles duplicate Search_Terms\n(e.g., diabetes appearing under two directorates) by combining fragments.\nEOF\n)\"","description":"Commit Task 1.2 implementation"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":468,"cache_read_input_tokens":112305,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":468},"output_tokens":25,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"494febc4-facc-4d54-809a-c3ea3a411bf0"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013Gb83Zr7XeLAyW9s1Hqs17","type":"tool_result","content":"[feature/snomed-indication-mapping 0779df7] feat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\n 2 files changed, 286 insertions(+), 212 deletions(-)","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"27cdcab7-171b-423b-9cf6-b1fc88417a0f","tool_use_result":{"stdout":"[feature/snomed-indication-mapping 0779df7] feat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\n 2 files changed, 286 insertions(+), 212 deletions(-)\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WSDpZhaxuV7mzEmD1xWNe4","type":"message","role":"assistant","content":[{"type":"text","text":"Good, committed as `0779df7`. Now update progress.txt."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":315,"cache_read_input_tokens":112773,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":315},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"275739d8-b764-4c75-8459-1c594282d014"}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01WSDpZhaxuV7mzEmD1xWNe4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017WTgvzmVaRxSCeMsfJYVg5","name":"Edit","input":{"replace_all":false,"file_path":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\progress.txt","old_string":"## Iteration Log","new_string":"## Iteration Log\n\n## Iteration 1  2026-02-05\n### Task: 1.2  Build drug-to-Search_Term lookup from DimSearchTerm.csv\n### Why this task:\n- First iteration, chose Phase 1 foundations. Task 1.2 (CSV loading) is self-contained and testable locally without Snowflake.\n- Task 1.1 (Snowflake query update) can't be verified without a live connection  better to do 1.2 first.\n- Both 1.1 and 1.2 are independent, so order doesn't matter for dependencies.\n### Status: COMPLETE\n### What was done:\n- Added `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`, builds two dicts:\n    - `fragment_to_search_terms`: drug fragment (UPPER)  list of Search_Terms\n    - `search_term_to_fragments`: search_term  list of drug fragments (UPPER)\n  - Handles duplicate Search_Terms (e.g., \"diabetes\" rows combined)\n  - Result: 164 Search_Terms, 346 drug fragments\n- Added `get_search_terms_for_drug()` to `diagnosis_lookup.py`:\n  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)\n  - Named differently from plan's `drug_matches_search_term()`  returns all matches at once rather than single boolean, more practical for Phase 2\n- Updated `__all__` exports\n### Validation results:\n- Tier 1 (Code): py_compile passed, import check passed\n- Tier 2 (Data): ADALIMUMAB  7 indications (including axial spondyloarthritis, rheumatoid arthritis), OMALIZUMAB  4 indications (asthma, allergic asthma, etc.), PEGYLATED LIPOSOMAL DOXORUBICIN  4 matches via substring, \"ADALIMUMAB 40MG\" matches correctly with dosage info, diabetes fragments combined from 2 CSV rows\n- Tier 3 (Functional): N/A (no UI changes)\n### Files changed:\n- data_processing/diagnosis_lookup.py (added load_drug_indication_mapping, get_search_terms_for_drug)\n- IMPLEMENTATION_PLAN.md (marked 1.2 subtasks [x])\n### Committed: 0779df7 \"feat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\"\n### Patterns discovered:\n- DimSearchTerm.csv has 164 unique Search_Terms (not 165 as noted) because diabetes appears twice with different directorates but same Search_Term\n- Some drug fragments are very generic: INHALED, CONTINUOUS, ORAL, STANDARD-DOSE, INTRAVENOUS, PEGYLATED, ROUTINE, INDUCTION  these will match broadly but are constrained by the GP diagnosis requirement in Phase 2\n- Function signatures for Phase 2: `get_search_terms_for_drug(drug_name, search_term_to_fragments)` returns list[str]  use this to get candidate indications per drug\n### Next iteration should:\n- Work on Task 1.1: Update `get_patient_indication_groups()` to return ALL matches with code_frequency\n  - The current query at line ~1352 of diagnosis_lookup.py uses `QUALIFY ROW_NUMBER() OVER (PARTITION BY pc.\"PatientPseudonym\" ORDER BY pc.\"EventDateTime\" DESC) = 1`  this must be replaced with GROUP BY + COUNT(*)\n  - Add `earliest_hcd_date` parameter to restrict GP codes to HCD data window\n  - Return columns: PatientPseudonym, Search_Term, code_frequency (not EventDateTime)\n- OR if Snowflake isn't available to test, skip to Task 2.1 (assign_drug_indications function) which can be built and tested with mock data\n### Blocked items:\n- None"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":315,"cache_read_input_tokens":112773,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":315},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"204f91c7-dee2-4056-999a-ea03513774aa"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017WTgvzmVaRxSCeMsfJYVg5","type":"tool_result","content":"The file C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\progress.txt has been updated successfully."}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"33d68632-21da-4c9a-a946-2d940d76e190","tool_use_result":{"filePath":"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\\progress.txt","oldString":"## Iteration Log","newString":"## Iteration Log\n\n## Iteration 1  2026-02-05\n### Task: 1.2  Build drug-to-Search_Term lookup from DimSearchTerm.csv\n### Why this task:\n- First iteration, chose Phase 1 foundations. Task 1.2 (CSV loading) is self-contained and testable locally without Snowflake.\n- Task 1.1 (Snowflake query update) can't be verified without a live connection  better to do 1.2 first.\n- Both 1.1 and 1.2 are independent, so order doesn't matter for dependencies.\n### Status: COMPLETE\n### What was done:\n- Added `load_drug_indication_mapping()` to `diagnosis_lookup.py`:\n  - Loads `data/DimSearchTerm.csv`, builds two dicts:\n    - `fragment_to_search_terms`: drug fragment (UPPER)  list of Search_Terms\n    - `search_term_to_fragments`: search_term  list of drug fragments (UPPER)\n  - Handles duplicate Search_Terms (e.g., \"diabetes\" rows combined)\n  - Result: 164 Search_Terms, 346 drug fragments\n- Added `get_search_terms_for_drug()` to `diagnosis_lookup.py`:\n  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)\n  - Named differently from plan's `drug_matches_search_term()`  returns all matches at once rather than single boolean, more practical for Phase 2\n- Updated `__all__` exports\n### Validation results:\n- Tier 1 (Code): py_compile passed, import check passed\n- Tier 2 (Data): ADALIMUMAB  7 indications (including axial spondyloarthritis, rheumatoid arthritis), OMALIZUMAB  4 indications (asthma, allergic asthma, etc.), PEGYLATED LIPOSOMAL DOXORUBICIN  4 matches via substring, \"ADALIMUMAB 40MG\" matches correctly with dosage info, diabetes fragments combined from 2 CSV rows\n- Tier 3 (Functional): N/A (no UI changes)\n### Files changed:\n- data_processing/diagnosis_lookup.py (added load_drug_indication_mapping, get_search_terms_for_drug)\n- IMPLEMENTATION_PLAN.md (marked 1.2 subtasks [x])\n### Committed: 0779df7 \"feat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\"\n### Patterns discovered:\n- DimSearchTerm.csv has 164 unique Search_Terms (not 165 as noted) because diabetes appears twice with different directorates but same Search_Term\n- Some drug fragments are very generic: INHALED, CONTINUOUS, ORAL, STANDARD-DOSE, INTRAVENOUS, PEGYLATED, ROUTINE, INDUCTION  these will match broadly but are constrained by the GP diagnosis requirement in Phase 2\n- Function signatures for Phase 2: `get_search_terms_for_drug(drug_name, search_term_to_fragments)` returns list[str]  use this to get candidate indications per drug\n### Next iteration should:\n- Work on Task 1.1: Update `get_patient_indication_groups()` to return ALL matches with code_frequency\n  - The current query at line ~1352 of diagnosis_lookup.py uses `QUALIFY ROW_NUMBER() OVER (PARTITION BY pc.\"PatientPseudonym\" ORDER BY pc.\"EventDateTime\" DESC) = 1`  this must be replaced with GROUP BY + COUNT(*)\n  - Add `earliest_hcd_date` parameter to restrict GP codes to HCD data window\n  - Return columns: PatientPseudonym, Search_Term, code_frequency (not EventDateTime)\n- OR if Snowflake isn't available to test, skip to Task 2.1 (assign_drug_indications function) which can be built and tested with mock data\n### Blocked items:\n- None","originalFile":"# Progress Log - Drug-Aware Indication Matching\n\n## Project Context\n\nThis project extends the indication-based pathway charts (Phase 1-5 complete) with drug-aware matching.\n\n**Previous state**: Patients get ONE indication based on their most recent GP diagnosis match (SNOMED cluster codes). This ignores which drugs the patient is taking.\n\n**New goal**: Match each drug to an indication by cross-referencing the patient's GP diagnoses AND the drug's Search_Term mapping from DimSearchTerm.csv.\n\n## Key Data/Patterns\n\n### DimSearchTerm.csv\n- Located at `data/DimSearchTerm.csv`\n- Columns: Search_Term, CleanedDrugName (pipe-separated), PrimaryDirectorate\n- ~165 rows mapping clinical conditions to drug name fragments\n- Drug fragments are substrings that match standardized drug names from HCD data\n- Some entries have generic fragments: INHALED, CONTINUOUS, STANDARD-DOSE, PEGYLATED\n\n### Current get_patient_indication_groups() in diagnosis_lookup.py\n- Uses CLUSTER_MAPPING_SQL as CTE in Snowflake query\n- Returns ONLY the most recent match per patient (QUALIFY ROW_NUMBER() = 1)\n- Needs to return ALL matching Search_Terms per patient (remove QUALIFY)\n- Batches 500 patients per query\n\n### Modified UPID approach\n- Current: UPID = Provider Code[:3] + PersonKey (e.g., \"RMV12345\")\n- New: UPID = original + \"|\" + search_term (e.g., \"RMV12345|rheumatoid arthritis\")\n- The pipe delimiter \"|\" is safe because existing UPIDs are alphanumeric\n- generate_icicle_chart_indication() treats UPID as an opaque identifier  modified UPIDs work transparently\n- The \" - \" delimiter in pathway ids is used for hierarchy levels, not within UPIDs\n\n### PseudoNHSNoLinked mapping\n- HCD data has PseudoNHSNoLinked column that matches PatientPseudonym in GP records\n- PersonKey is provider-specific local ID  do NOT use for GP matching\n- One PseudoNHSNoLinked can map to multiple UPIDs (multi-provider patients)\n- GP match lookup: PseudoNHSNoLinked  list of matched Search_Terms\n\n### Drug matching logic\n- For each HCD row (UPID + Drug Name):\n  1. Get patient's GP-matched Search_Terms with code_frequency (via PseudoNHSNoLinked)\n  2. Get which Search_Terms list this drug (from DimSearchTerm.csv)\n  3. Intersection = valid indications\n  4. If 1: use it. If multiple: pick highest code_frequency (most GP coding = most likely indication). If 0: fallback to directory.\n- Modified UPID groups drugs under same indication together naturally\n- code_frequency = COUNT(*) of matching SNOMED codes per Search_Term per patient in GP records\n- GP code time range: only count codes from MIN(Intervention Date) onwards (the HCD data window)\n  - Reduces noise from old/irrelevant diagnoses, makes frequency more meaningful\n  - Pass earliest_hcd_date as parameter to get_patient_indication_groups()\n- Tiebreaker rationale: 47 RA codes vs 2 crohn's codes  RA is clearly the active condition\n\n### Known edge cases\n- Some DimSearchTerm drug fragments are generic (INHALED, ORAL, CONTINUOUS)\n  - These could match broadly but are constrained by GP diagnosis requirement\n- A patient visiting multiple providers has multiple UPIDs\n  - Each UPID gets its own drug-indication matching independently\n- Same Search_Term appears twice in DimSearchTerm.csv with different directorates\n  - e.g., \"diabetes\"  DIABETIC MEDICINE and OPHTHALMOLOGY\n  - For indication charts, we use Search_Term not directorate, so this is fine\n\n## Iteration Log\n","structuredPatch":[{"oldStart":59,"oldLines":3,"newStart":59,"newLines":42,"lines":["   - For indication charts, we use Search_Term not directorate, so this is fine"," "," ## Iteration Log","+","+## Iteration 1  2026-02-05","+### Task: 1.2  Build drug-to-Search_Term lookup from DimSearchTerm.csv","+### Why this task:","+- First iteration, chose Phase 1 foundations. Task 1.2 (CSV loading) is self-contained and testable locally without Snowflake.","+- Task 1.1 (Snowflake query update) can't be verified without a live connection  better to do 1.2 first.","+- Both 1.1 and 1.2 are independent, so order doesn't matter for dependencies.","+### Status: COMPLETE","+### What was done:","+- Added `load_drug_indication_mapping()` to `diagnosis_lookup.py`:","+  - Loads `data/DimSearchTerm.csv`, builds two dicts:","+    - `fragment_to_search_terms`: drug fragment (UPPER)  list of Search_Terms","+    - `search_term_to_fragments`: search_term  list of drug fragments (UPPER)","+  - Handles duplicate Search_Terms (e.g., \"diabetes\" rows combined)","+  - Result: 164 Search_Terms, 346 drug fragments","+- Added `get_search_terms_for_drug()` to `diagnosis_lookup.py`:","+  - Returns all Search_Terms whose drug fragments are substrings of the drug name (case-insensitive)","+  - Named differently from plan's `drug_matches_search_term()`  returns all matches at once rather than single boolean, more practical for Phase 2","+- Updated `__all__` exports","+### Validation results:","+- Tier 1 (Code): py_compile passed, import check passed","+- Tier 2 (Data): ADALIMUMAB  7 indications (including axial spondyloarthritis, rheumatoid arthritis), OMALIZUMAB  4 indications (asthma, allergic asthma, etc.), PEGYLATED LIPOSOMAL DOXORUBICIN  4 matches via substring, \"ADALIMUMAB 40MG\" matches correctly with dosage info, diabetes fragments combined from 2 CSV rows","+- Tier 3 (Functional): N/A (no UI changes)","+### Files changed:","+- data_processing/diagnosis_lookup.py (added load_drug_indication_mapping, get_search_terms_for_drug)","+- IMPLEMENTATION_PLAN.md (marked 1.2 subtasks [x])","+### Committed: 0779df7 \"feat: add drug-to-indication mapping from DimSearchTerm.csv (Task 1.2)\"","+### Patterns discovered:","+- DimSearchTerm.csv has 164 unique Search_Terms (not 165 as noted) because diabetes appears twice with different directorates but same Search_Term","+- Some drug fragments are very generic: INHALED, CONTINUOUS, ORAL, STANDARD-DOSE, INTRAVENOUS, PEGYLATED, ROUTINE, INDUCTION  these will match broadly but are constrained by the GP diagnosis requirement in Phase 2","+- Function signatures for Phase 2: `get_search_terms_for_drug(drug_name, search_term_to_fragments)` returns list[str]  use this to get candidate indications per drug","+### Next iteration should:","+- Work on Task 1.1: Update `get_patient_indication_groups()` to return ALL matches with code_frequency","+  - The current query at line ~1352 of diagnosis_lookup.py uses `QUALIFY ROW_NUMBER() OVER (PARTITION BY pc.\"PatientPseudonym\" ORDER BY pc.\"EventDateTime\" DESC) = 1`  this must be replaced with GROUP BY + COUNT(*)","+  - Add `earliest_hcd_date` parameter to restrict GP codes to HCD data window","+  - Return columns: PatientPseudonym, Search_Term, code_frequency (not EventDateTime)","+- OR if Snowflake isn't available to test, skip to Task 2.1 (assign_drug_indications function) which can be built and tested with mock data","+### Blocked items:","+- None"]}],"userModified":false,"replaceAll":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_015cNQCY5kS6AhgynQ1HyuaU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VV9jBxvGa9b2MycLpqddkG","name":"Bash","input":{"command":"cd \"C:\\Users\\charlwoodand\\Ralph local\\Tasks\\UI Redesign\" && git add progress.txt && git commit -m \"$(cat <<'EOF'\ndocs: update progress.txt with Iteration 1 results (Task 1.2)\nEOF\n)\"","description":"Commit progress update"}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":1171,"cache_read_input_tokens":113088,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":1171},"output_tokens":25,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"ebc1a9fa-a184-4918-be59-5fbb89b5e953"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VV9jBxvGa9b2MycLpqddkG","type":"tool_result","content":"[feature/snomed-indication-mapping c85aae4] docs: update progress.txt with Iteration 1 results (Task 1.2)\n 1 file changed, 84 insertions(+), 470 deletions(-)","is_error":false}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"27f363cf-a373-4ff9-8218-6c45ff30076c","tool_use_result":{"stdout":"[feature/snomed-indication-mapping c85aae4] docs: update progress.txt with Iteration 1 results (Task 1.2)\n 1 file changed, 84 insertions(+), 470 deletions(-)\r","stderr":"","interrupted":false,"isImage":false}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01NepNfJdZGi2JTGimZkSYRE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014TfTt84Ha76VZTc3ASsntp","name":"TodoWrite","input":{"todos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"completed","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"completed","activeForm":"Updating progress and committing changes"}]}}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":202,"cache_read_input_tokens":114259,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":202},"output_tokens":25,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"8c463b58-4d89-4df4-b23a-6f994a4d5fb4"}
{"type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014TfTt84Ha76VZTc3ASsntp","type":"tool_result","content":"Todos have been modified successfully. Ensure that you continue to use the todo list to track your progress. Please proceed with the current tasks if applicable"}]},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"48c41fdf-390f-4f8f-ab5c-7ffb09608a6f","tool_use_result":{"oldTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"completed","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"in_progress","activeForm":"Updating progress and committing changes"}],"newTodos":[{"content":"Implement Task 1.2: Build drug-to-Search_Term lookup from DimSearchTerm.csv","status":"completed","activeForm":"Implementing drug-to-Search_Term mapping functions"},{"content":"Validate: syntax check, import check, basic functionality test","status":"completed","activeForm":"Validating code compiles and works correctly"},{"content":"Update progress.txt and commit","status":"completed","activeForm":"Updating progress and committing changes"}]}}
{"type":"assistant","message":{"model":"claude-opus-4-6","id":"msg_01TPhGp1NUWGHh6rH1RRMnMv","type":"message","role":"assistant","content":[{"type":"text","text":"**Iteration 1 complete.**\n\n**Summary:**\n- **Task 1.2**  Build drug-to-Search_Term lookup from DimSearchTerm.csv  DONE\n- Added `load_drug_indication_mapping()` and `get_search_terms_for_drug()` to `diagnosis_lookup.py`\n- 164 Search_Terms, 346 drug fragments loaded\n- Substring matching verified: ADALIMUMAB  7 indications, OMALIZUMAB  4, PEGYLATED LIPOSOMAL DOXORUBICIN  4\n- Committed as `0779df7`\n\n**Next iteration should:** Work on Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function) if Snowflake isn't available for testing."}],"stop_reason":null,"stop_sequence":null,"usage":{"input_tokens":1,"cache_creation_input_tokens":218,"cache_read_input_tokens":114461,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":218},"output_tokens":1,"service_tier":"standard","inference_geo":"not_available"},"context_management":null},"parent_tool_use_id":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","uuid":"28cf850a-4b2e-4dae-8b49-8105446099ba"}
{"type":"result","subtype":"success","is_error":false,"duration_ms":222207,"duration_api_ms":199481,"num_turns":22,"result":"**Iteration 1 complete.**\n\n**Summary:**\n- **Task 1.2**  Build drug-to-Search_Term lookup from DimSearchTerm.csv  DONE\n- Added `load_drug_indication_mapping()` and `get_search_terms_for_drug()` to `diagnosis_lookup.py`\n- 164 Search_Terms, 346 drug fragments loaded\n- Substring matching verified: ADALIMUMAB  7 indications, OMALIZUMAB  4, PEGYLATED LIPOSOMAL DOXORUBICIN  4\n- Committed as `0779df7`\n\n**Next iteration should:** Work on Task 1.1 (update Snowflake query to return ALL matches with code_frequency) or Task 2.1 (assign_drug_indications function) if Snowflake isn't available for testing.","stop_reason":null,"session_id":"50d8895c-a9a4-4ad0-9690-37ae4e036ed7","total_cost_usd":1.8441917500000002,"usage":{"input_tokens":5978,"cache_creation_input_tokens":114679,"cache_read_input_tokens":1800784,"output_tokens":7444,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":114679,"ephemeral_5m_input_tokens":0}},"modelUsage":{"claude-opus-4-6":{"inputTokens":5978,"outputTokens":7444,"cacheReadInputTokens":1800784,"cacheCreationInputTokens":114679,"webSearchRequests":0,"costUSD":1.8331257499999998,"contextWindow":200000,"maxOutputTokens":32000},"claude-haiku-4-5-20251001":{"inputTokens":9666,"outputTokens":280,"cacheReadInputTokens":0,"cacheCreationInputTokens":0,"webSearchRequests":0,"costUSD":0.011066,"contextWindow":200000,"maxOutputTokens":64000}},"permission_denials":[],"uuid":"ed78b671-d682-48ca-a642-c4903ac68257"}