HighCostDrugsDemo/IMPLEMENTATION_PLAN.md

Implementation Plan - Direct SNOMED Indication Mapping

Project Overview

Extend the pathway analysis application to use direct SNOMED code matching from GP records to:

1. Improve directorate assignment - Use diagnosis-based directorate as primary method
2. Add indication-based icicle chart - New chart type showing Trust → Search_Term → Drug → Pathway

Data Source

data/drug_snomed_mapping_enriched.csv - 163K rows mapping:

• Drug → Indication → TA_ID → Search_Term → SNOMEDCode → PrimaryDirectorate

Key Design Decisions

Aspect	Decision
Primary directorate method	Diagnosis-based (SNOMED match → PrimaryDirectorate)
Fallback	department_identification() chain
Grouping level	Search_Term column (187 unique values)
Chart types	Two: "By Directory" and "By Indication" (user 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	SQLite table ref_drug_snomed_mapping, accessed at ingestion

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: Data Infrastructure

1.1 Create SQLite Table for SNOMED Mapping

• Add REF_DRUG_SNOMED_MAPPING_SCHEMA to data_processing/schema.py:
  • Columns: drug_name, indication, ta_id, search_term, snomed_code, snomed_description, cleaned_drug_name, primary_directorate, all_directorates
  • Index on: cleaned_drug_name, snomed_code, search_term
• Add create_drug_snomed_mapping_table() helper function
• Add to ALL_TABLES_SCHEMA and migration
• Verify: python -m data_processing.migrate creates table

1.2 Load Enriched Mapping Data

• Create data_processing/load_snomed_mapping.py script:
  • Read data/drug_snomed_mapping_enriched.csv
  • Insert into ref_drug_snomed_mapping table
  • Log: row count, unique drugs, unique search terms
• Add CLI entry point: python -m data_processing.load_snomed_mapping
• Verify: Query confirms 163K+ rows, 187 search terms

1.3 Extend Diagnosis Lookup Module

• Add get_drug_snomed_codes(drug_name) to diagnosis_lookup.py:
  • Query ref_drug_snomed_mapping for all SNOMED codes for a drug
  • Return list of (snomed_code, snomed_description, search_term, primary_directorate)
• Add patient_has_indication_direct(patient_pseudonym, snomed_codes, connector):
  • Query PrimaryCareClinicalCoding directly for exact SNOMED code matches
  • Return most recent match by EventDateTime
  • Return: (matched_code, search_term, primary_directorate, event_date) or None
• Verify: Unit test with known drug/patient pair

---

Phase 2: Pathway Processing Updates

2.1 Update Directorate Assignment Logic

• Modify tools/data.py department_identification() or create wrapper:
  • Add get_directorate_from_diagnosis(upid, drug_name, connector) function
  • Logic: Try diagnosis-based first → fallback to department_identification()
  • Return: (directorate, source) where source is "DIAGNOSIS" or "FALLBACK"
• Track assignment source for metrics (how many diagnosis-based vs fallback)
• Verify: Test with sample patient data

2.2 Add Chart Type Support to Schema

• Add chart_type column to pathway_nodes table:
  • Values: "directory" (existing), "indication" (new)
  • Update schema in data_processing/schema.py
• Update pathway_date_filters or create pathway_chart_types reference table
• Verify: Migration adds column, existing data defaults to "directory"

2.3 Create Indication Pathway Processing

• Add process_indication_pathways() to pathway_pipeline.py:
  • Group by: Trust → Search_Term → Drug → Pathway
  • For unmatched patients: use directorate name as Search_Term fallback
  • Output: Same structure as directory pathways but with indication grouping
• Add extract_indication_fields() for denormalized columns:
  • Extract: trust_name, search_term (or fallback_directorate), drug_sequence
• Verify: Process sample data, check hierarchy structure

---

Phase 3: CLI & Data Refresh Updates

3.1 Update Refresh Command for Dual Chart Types

• Modify cli/refresh_pathways.py:
  • Process both "directory" and "indication" chart types
  • For each of 6 date filters: generate 2 chart datasets
  • Total: 12 pathway datasets (6 dates × 2 chart types)
• Add --chart-type argument: "all" (default), "directory", "indication"
• Update progress logging to show both chart types
• Verify: Dry run shows both chart types being processed

3.2 Integrate Diagnosis-Based Directorate in Pipeline

• Update fetch_and_transform_data() to include diagnosis lookup:
  • After UPID creation, batch lookup SNOMED matches for all patients
  • Store: matched_search_term, matched_directorate, match_source
• Handle Snowflake connection for GP record queries (batched for performance)
• Log coverage: X% diagnosis-matched, Y% fallback
• Verify: Test refresh with --dry-run, check coverage stats

3.3 Test Full Refresh Pipeline

• Run python -m cli.refresh_pathways with real data
• Verify pathway_nodes table has both chart_type values
• Verify indication chart has expected hierarchy (Trust → SearchTerm → Drug)
• Verify unmatched patients appear 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 Measure Coverage Improvement

• Compare match rates: cluster-only vs cluster+direct SNOMED
• Generate report: % of patients with diagnosis-based directorate
• Identify drugs with best/worst coverage improvement
• Document results in progress.txt

5.2 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

5.3 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
• Diagnosis-based directorate is primary method with fallback working
• 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

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
data_processing/schema.py	SQLite schema for ref_drug_snomed_mapping
data_processing/diagnosis_lookup.py	Direct SNOMED 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
data/drug_snomed_mapping_enriched.csv	Source mapping data

Expected Data Volumes

Metric	Expected
SNOMED mapping rows	~163K
Unique Search_Terms	187
Unique drugs	~364
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