HighCostDrugsDemo/IMPLEMENTATION_PLAN.md

Implementation Plan - Indication-Based Pathway Charts

Project Overview

Extend the pathway analysis application to show indication-based icicle charts alongside directory-based charts. Patient diagnoses are matched from GP records using SNOMED cluster codes.

Key Design Decisions

Aspect	Decision
SNOMED source	Query ClinicalCodingClusterSnomedCodes clusters directly in Snowflake
Grouping level	Search_Term from cluster mapping (~148 conditions)
Chart types	Two: "By Directory" (existing) and "By Indication" (new toggle)
No-match display	Show assigned directorate in indication chart (mixed labels)
Multiple matches	Use most recent SNOMED code by GP record date
Data storage	No local SNOMED mapping — query Snowflake at refresh time

SNOMED Cluster Query

The snomed_indication_mapping_query.sql file contains the master query:

• Maps Search_Term → Cluster_ID for ~148 conditions
• Joins ClinicalCodingClusterSnomedCodes to get SNOMED codes per cluster
• Includes explicit manual mappings for conditions not in clusters
• Returns: Search_Term, SNOMEDCode, SNOMEDDescription

Quality Checks

Run after each task:

# Syntax check
python -m py_compile <modified_file.py>

# Import verification
python -c "from data_processing.diagnosis_lookup import *"
python -c "from data_processing.pathway_pipeline import *"

# For Reflex changes
python -m reflex compile

---

Phase 1: Snowflake Integration

1.1 Create Indication Lookup Query

• Add get_patient_indication_groups() function to data_processing/diagnosis_lookup.py:
  • Takes: list of patient pseudonyms (PseudoNHSNoLinked values)
  • Uses the cluster query from snomed_indication_mapping_query.sql as a CTE
  • Joins with PrimaryCareClinicalCoding to find patients with matching diagnoses
  • Returns: DataFrame with PatientPseudonym, Search_Term, EventDateTime
  • Uses most recent match per patient (ORDER BY EventDateTime DESC)
• Handle edge cases: Snowflake unavailable, empty patient list
• Verify: Function returns expected Search_Terms for test patients

1.2 Update Data Pipeline to Include Indications

• Modify cli/refresh_pathways.py to call indication lookup during refresh:
  • After fetching HCD data, extract unique PseudoNHSNoLinked values
  • Call get_patient_indication_groups() with patient list
  • Create indication_df mapping UPID → Indication_Group
  • For patients with no GP match: Indication_Group = fallback directorate
• Log coverage: X% diagnosis-matched, Y% fallback
• Verify: indication_df has correct structure for pathway processing

---

Phase 2: Schema & Processing Updates

2.1 Add Chart Type Support to Schema

• Add chart_type column to pathway_nodes table (ALREADY DONE)
• Update UNIQUE constraint to include chart_type (ALREADY DONE)
• Add indexes for chart_type filtering (ALREADY DONE)
• Verify: Existing migration works correctly

2.2 Create Indication Pathway Processing

• Add generate_icicle_chart_indication() to pathway_analyzer.py (ALREADY DONE)
• Add process_indication_pathway_for_date_filter() to pathway_pipeline.py (ALREADY DONE)
• Add extract_indication_fields() for denormalized columns (ALREADY DONE)
• Update convert_to_records() with chart_type parameter (ALREADY DONE)
• Verify: Code compiles, imports work correctly

2.3 Update Refresh Command for Dual Charts

• Add --chart-type argument: "all", "directory", "indication" (ALREADY DONE)
• Update indication processing to use new get_patient_indication_groups():
  • Replace batch_lookup_indication_groups() with the new Snowflake-direct approach
  • Pass indication_df to process_indication_pathway_for_date_filter()
• Process all 6 date filters for both chart types (existing loop already handles this)
• Verify: Both chart types generate pathway data

---

Phase 3: Test Full Pipeline

3.1 Test Refresh with Real Data

• [~] Run python -m cli.refresh_pathways --chart-type all with Snowflake
• Verify pathway_nodes table has both chart_type values:
  • SELECT chart_type, COUNT(*) FROM pathway_nodes GROUP BY chart_type
• Verify indication hierarchy: Trust → Search_Term → Drug → Pathway
• Verify unmatched patients show with directorate fallback label
• Document: Processing time, record counts, coverage percentages

---

Phase 4: Reflex UI Updates

4.1 Add Chart Type State

• Add state variables to AppState:
  • selected_chart_type: str = "directory" (options: "directory", "indication")
  • chart_type_options: list[dict] for dropdown
• Add set_chart_type() event handler
• Update load_pathway_data() to filter by chart_type
• Verify: State changes correctly, data queries include chart_type filter

4.2 Add Chart Type Toggle UI

• Create chart_type_toggle() component:
  • Radio buttons or segmented control: "By Directory" | "By Indication"
  • Place in filter strip or chart header area
• Wire to set_chart_type() handler
• Verify: Toggle switches chart data, UI updates reactively

4.3 Update Chart Display for Indication Labels

• Ensure icicle chart handles mixed labels:
  • Search_Term labels (e.g., "rheumatoid arthritis") for matched patients
  • Directorate labels (e.g., "RHEUMATOLOGY (no GP dx)") for unmatched
• Update hover templates if needed for indication context
• Verify: Chart renders correctly with both label types

---

Phase 5: Validation & Documentation

5.1 End-to-End Validation

• Run full app with both chart types
• Verify chart toggle works correctly
• Verify filter interactions (drugs, directorates) work for both types
• Verify KPIs update correctly for both chart types
• Test at multiple viewport sizes

5.2 Update Documentation

• Update CLAUDE.md with new architecture
• Document new CLI arguments
• Document chart_type toggle behavior
• Update data flow diagrams

---

Completion Criteria

All tasks marked [x] AND:

• App compiles without errors (reflex compile succeeds)
• Both chart types generate pathway data (12 total: 6 dates × 2 types)
• Chart type toggle switches between Directory and Indication views
• GP diagnosis matching works via Snowflake cluster query
• Unmatched patients show in indication chart with directorate fallback label
• Coverage metrics logged (% diagnosis-matched vs fallback)
• All filters work correctly for both chart types
• Performance acceptable (< 10 min full refresh, < 500ms filter change)

---

Reference

SNOMED Cluster Query Structure

-- From snomed_indication_mapping_query.sql
WITH SearchTermClusters AS (
    SELECT Search_Term, Cluster_ID FROM (VALUES
        ('rheumatoid arthritis', 'eFI2_InflammatoryArthritis'),
        ('macular degeneration', 'CUST_ICB_VISUAL_IMPAIRMENT'),
        -- ... ~148 mappings
    ) AS t(Search_Term, Cluster_ID)
),
ClusterCodes AS (
    SELECT stc.Search_Term, c."SNOMEDCode", c."SNOMEDDescription"
    FROM SearchTermClusters stc
    JOIN DATA_HUB.PHM."ClinicalCodingClusterSnomedCodes" c
        ON stc.Cluster_ID = c."Cluster_ID"
    WHERE c."SNOMEDCode" IS NOT NULL
),
ExplicitCodes AS (
    -- Manual mappings for conditions not in clusters
    SELECT Search_Term, SNOMEDCode, SNOMEDDescription FROM (VALUES
        ('ankylosing spondylitis', '162930007', 'Manual mapping'),
        -- ...
    ) AS t(Search_Term, SNOMEDCode, SNOMEDDescription)
)
SELECT * FROM ClusterCodes
UNION ALL
SELECT * FROM ExplicitCodes

Current Pathway Hierarchy (Directory-based)

Root (N&W ICS)
└── Trust (NNUH, QEH, JPH, etc.)
    └── Directory (RHEUMATOLOGY, OPHTHALMOLOGY, etc.)
        └── Drug (ADALIMUMAB, RANIBIZUMAB, etc.)
            └── Pathway (drug sequences)

New Pathway Hierarchy (Indication-based)

Root (N&W ICS)
└── Trust (NNUH, QEH, JPH, etc.)
    └── Search_Term (rheumatoid arthritis, macular degeneration, etc.)
        │   OR Directorate (RHEUMATOLOGY - for unmatched patients)
        └── Drug (ADALIMUMAB, RANIBIZUMAB, etc.)
            └── Pathway (drug sequences)

Key Files

File	Purpose
snomed_indication_mapping_query.sql	Master SNOMED cluster query
data_processing/diagnosis_lookup.py	GP diagnosis lookup functions
data_processing/pathway_pipeline.py	Indication pathway processing
cli/refresh_pathways.py	CLI for dual chart type refresh
pathways_app/pathways_app.py	Reflex UI with chart type toggle

Expected Data Volumes

Metric	Expected
Search_Term conditions	~148 (from cluster mapping)
Pathway nodes (directory, per date filter)	~300
Pathway nodes (indication, per date filter)	~400-600 (more granular)
Total pathway nodes (6 dates × 2 types)	~4,000-5,000