HighCostDrugsDemo/progress.txt

# Progress Log - Drug-Aware Indication Matching

## Project Context

This project extends the indication-based pathway charts (Phase 1-5 complete) with drug-aware matching.

**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.

**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.

## Key Data/Patterns

### DimSearchTerm.csv
- Located at `data/DimSearchTerm.csv`
- Columns: Search_Term, CleanedDrugName (pipe-separated), PrimaryDirectorate
- ~165 rows mapping clinical conditions to drug name fragments
- Drug fragments are substrings that match standardized drug names from HCD data
- Some entries have generic fragments: INHALED, CONTINUOUS, STANDARD-DOSE, PEGYLATED

### Current get_patient_indication_groups() in diagnosis_lookup.py
- Uses CLUSTER_MAPPING_SQL as CTE in Snowflake query
- Returns ONLY the most recent match per patient (QUALIFY ROW_NUMBER() = 1)
- Needs to return ALL matching Search_Terms per patient (remove QUALIFY)
- Batches 500 patients per query

### Modified UPID approach
- Current: UPID = Provider Code[:3] + PersonKey (e.g., "RMV12345")
- New: UPID = original + "|" + search_term (e.g., "RMV12345|rheumatoid arthritis")
- The pipe delimiter "|" is safe because existing UPIDs are alphanumeric
- generate_icicle_chart_indication() treats UPID as an opaque identifier — modified UPIDs work transparently
- The " - " delimiter in pathway ids is used for hierarchy levels, not within UPIDs

### PseudoNHSNoLinked mapping
- HCD data has PseudoNHSNoLinked column that matches PatientPseudonym in GP records
- PersonKey is provider-specific local ID — do NOT use for GP matching
- One PseudoNHSNoLinked can map to multiple UPIDs (multi-provider patients)
- GP match lookup: PseudoNHSNoLinked → list of matched Search_Terms

### Drug matching logic
- For each HCD row (UPID + Drug Name):
  1. Get patient's GP-matched Search_Terms with code_frequency (via PseudoNHSNoLinked)
  2. Get which Search_Terms list this drug (from DimSearchTerm.csv)
  3. Intersection = valid indications
  4. If 1: use it. If multiple: pick highest code_frequency (most GP coding = most likely indication). If 0: fallback to directory.
- Modified UPID groups drugs under same indication together naturally
- code_frequency = COUNT(*) of matching SNOMED codes per Search_Term per patient in GP records
- GP code time range: only count codes from MIN(Intervention Date) onwards (the HCD data window)
  - Reduces noise from old/irrelevant diagnoses, makes frequency more meaningful
  - Pass earliest_hcd_date as parameter to get_patient_indication_groups()
- Tiebreaker rationale: 47 RA codes vs 2 crohn's codes → RA is clearly the active condition

### Known edge cases
- Some DimSearchTerm drug fragments are generic (INHALED, ORAL, CONTINUOUS)
  - These could match broadly but are constrained by GP diagnosis requirement
- A patient visiting multiple providers has multiple UPIDs
  - Each UPID gets its own drug-indication matching independently
- Same Search_Term appears twice in DimSearchTerm.csv with different directorates
  - e.g., "diabetes" → DIABETIC MEDICINE and OPHTHALMOLOGY
  - For indication charts, we use Search_Term not directorate, so this is fine

## Iteration Log

## Iteration 1 — 2026-02-05
### Task: 1.3 — 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 1.3)"
### 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 1.2: Merge asthma Search_Terms in CLUSTER_MAPPING_SQL and load_drug_indication_mapping()
  - Merge "allergic asthma", "asthma", "severe persistent allergic asthma" → "asthma"
  - Keep "urticaria" separate
  - This is self-contained and testable locally
- OR 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 1.1, skip to Task 2.1 (assign_drug_indications function) which can be built and tested with mock data
### Blocked items:
- None

## Iteration 2 — 2026-02-05
### Task: 1.2 — Merge related asthma Search_Terms in CLUSTER_MAPPING_SQL
### Why this task:
- Previous iteration recommended this as the next task (self-contained, testable locally)
- Both CLUSTER_MAPPING_SQL and load_drug_indication_mapping() need consistent Search_Term names
- Must be done before Task 1.1 (Snowflake query) to ensure GP lookups return "asthma" not "allergic asthma"
### Status: COMPLETE
### What was done:
- Updated CLUSTER_MAPPING_SQL: changed 'allergic asthma' → 'asthma' (AST_COD) and 'severe persistent allergic asthma' → 'asthma' (SEVAST_COD)
  - Now 3 rows for 'asthma': AST_COD, eFI2_Asthma, SEVAST_COD
  - urticaria (XSAL_COD) stays separate
- Added SEARCH_TERM_MERGE_MAP constant: {"allergic asthma": "asthma", "severe persistent allergic asthma": "asthma"}
- Updated load_drug_indication_mapping() to apply SEARCH_TERM_MERGE_MAP when loading CSV
  - Normalizes Search_Term before accumulating fragments
  - Drug fragments from all 3 original rows combined under "asthma" key
- Exported SEARCH_TERM_MERGE_MAP in __all__
### Validation results:
- Tier 1 (Code): py_compile passed, import check passed
- Tier 2 (Data):
  - "asthma" fragments: OMALIZUMAB, BENRALIZUMAB, DUPILUMAB, INHALED, MEPOLIZUMAB, RESLIZUMAB (complete combined list)
  - "allergic asthma" no longer exists as separate key
  - "severe persistent allergic asthma" no longer exists as separate key
  - "urticaria" → ['OMALIZUMAB'] — correctly separate
  - OMALIZUMAB maps to: ['asthma', 'urticaria'] — correct
  - Total Search_Terms: 162 (was 164, 3 asthma entries → 1)
  - Total fragments: 346 (unchanged)
- Tier 3 (Functional): N/A (no UI changes)
### Files changed:
- data_processing/diagnosis_lookup.py (CLUSTER_MAPPING_SQL, SEARCH_TERM_MERGE_MAP, load_drug_indication_mapping)
- IMPLEMENTATION_PLAN.md (marked 1.2 subtasks [x])
### Committed: b0a8a9d "feat: merge asthma Search_Term variants in CLUSTER_MAPPING_SQL and drug mapping (Task 1.2)"
### Patterns discovered:
- SEARCH_TERM_MERGE_MAP is reusable: any future module that receives Search_Terms from Snowflake can apply the same normalization
- The merge approach (normalize at load time) is cleaner than post-hoc deduplication
### 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 1467 uses `QUALIFY ROW_NUMBER() OVER (PARTITION BY pc."PatientPseudonym" ORDER BY pc."EventDateTime" DESC) = 1`
  - Replace with GROUP BY + COUNT(*) for code_frequency
  - Add `earliest_hcd_date` parameter to restrict GP codes to HCD data window
  - Return columns: PatientPseudonym, Search_Term, code_frequency
  - Empty DataFrame columns should match new return type
  - This requires Snowflake connectivity to fully test, but code changes can be verified with py_compile and import checks
- OR work on Task 2.1: Create assign_drug_indications() — can be built and tested with mock data
  - This is independent of Task 1.1 if you mock the gp_matches_df input
### Blocked items:
- None

## Iteration 3 — 2026-02-05
### Task: 1.1 — Update get_patient_indication_groups() to return ALL matches with code_frequency
### Why this task:
- Previous iteration recommended this as the next task
- This is the last remaining Phase 1 dependency — Tasks 2.1/2.2 need the GP matches data in the right format (multiple rows per patient with code_frequency)
- Task 1.1 changes the query fundamentals; better to do this before building assign_drug_indications() which consumes the output
### Status: COMPLETE
### What was done:
- Modified `get_patient_indication_groups()` in `diagnosis_lookup.py`:
  1. **Removed** `QUALIFY ROW_NUMBER() OVER (PARTITION BY pc."PatientPseudonym" ORDER BY pc."EventDateTime" DESC) = 1`
  2. **Replaced** with `GROUP BY pc."PatientPseudonym", aic.Search_Term` + `COUNT(*) AS "code_frequency"`
  3. **Added** `earliest_hcd_date: Optional[str] = None` parameter
  4. **Added** optional `AND pc."EventDateTime" >= %s` when earliest_hcd_date is provided
  5. **Updated** return columns from `(PatientPseudonym, Search_Term, EventDateTime)` to `(PatientPseudonym, Search_Term, code_frequency)`
  6. **Updated** all empty DataFrame returns to use new column names
  7. **Updated** logging to show multiple-rows-per-patient stats (avg indications per patient)
  8. **Updated** docstring to describe new behavior and parameters
- Backward compatible: `earliest_hcd_date` defaults to `None`, existing callers still work
- Note: caller in `refresh_pathways.py` (line 424-428) does `dict(zip(...))` which will only keep last match per patient with new multi-row format — this will be updated in Task 3.1
### Validation results:
- Tier 1 (Code): py_compile PASSED, import check PASSED, function signature verified
- Tier 2 (Data): Empty DataFrame returns correct columns ['PatientPseudonym', 'Search_Term', 'code_frequency']; live Snowflake test deferred to Phase 3/4
- Tier 3 (Functional): N/A (no UI changes)
### Files changed:
- data_processing/diagnosis_lookup.py (modified get_patient_indication_groups function)
- IMPLEMENTATION_PLAN.md (marked 1.1 subtasks [x])
### Committed: c93417f "feat: return ALL GP matches with code_frequency in get_patient_indication_groups (Task 1.1)"
### Patterns discovered:
- The `earliest_hcd_date` parameter is passed as a string in ISO format (YYYY-MM-DD) via Snowflake %s placeholder — Snowflake handles string-to-timestamp comparison implicitly
- The GROUP BY approach naturally deduplicates SNOMED codes within the same Search_Term — a patient with the same SNOMED code recorded 5 times gets code_frequency=5 (reflecting clinical activity intensity)
- params list is built dynamically: `batch_pseudonyms + [earliest_hcd_date]` only when date filter is active
### Next iteration should:
- Work on Task 2.1: Create `assign_drug_indications()` function
  - This is now unblocked since 1.1 is complete (return format is known)
  - Input: HCD df, gp_matches_df (PatientPseudonym, Search_Term, code_frequency), drug_mapping from load_drug_indication_mapping()
  - Output: (modified_df with UPID|search_term, indication_df mapping modified_UPID → Search_Term)
  - Can be built and tested with mock data (no Snowflake needed)
  - Key logic: for each UPID+Drug pair, intersect drug's Search_Terms with patient's GP matches, pick highest code_frequency as tiebreaker
  - The function needs PseudoNHSNoLinked to look up GP matches, so the df must have that column
  - Task 2.2 (tiebreaker logic) can be done within 2.1 or as a follow-up
- The final Phase 1 subtask (1.1 verify with live Snowflake) will be tested during Phase 3/4 integration
### Blocked items:
- Task 1.1 final subtask "Verify: Query returns more rows" requires live Snowflake — deferred to Phase 3/4