HighCostDrugsDemo/IMPLEMENTATION_PLAN.md

Implementation Plan - Pathway Data Architecture

Project Overview

Pre-compute patient treatment pathways from Snowflake and store in SQLite for fast Reflex filtering. This replaces the current simplified prepare_chart_data() with full pathway hierarchy support.

Architecture: Snowflake → Pathway Processing → SQLite (pre-computed) → Reflex (filter & view)

Key Benefits:

• Performance: Pathway calculation done once during data refresh, not on every filter
• Simplicity: Reflex filters pre-computed data with simple SQL WHERE clauses
• Full Pathways: Sequential treatment pathways (drug_0 → drug_1 → drug_2...) with statistics

Design Reference: See PATHWAY_DATA_ARCHITECTURE_PLAN.md for detailed architecture, schema, and data flow.

Source Code:

• Existing analysis: analysis/pathway_analyzer.py
• Existing visualization: visualization/plotly_generator.py
• Existing Reflex app: pathways_app/app_v2.py

Quality Checks

Run after each task:

# Syntax check for Python files
python -m py_compile <file.py>

# Import verification
python -c "from <module> import <class>"

# For Reflex changes
cd pathways_app && timeout 60 python -m reflex run 2>&1 | head -30

Phase 1: Schema & Data Pipeline Foundation

1.1 Extend Database Schema

• Add pathway_date_filters table with 6 pre-defined combinations:
  • all_6mo, all_12mo, 1yr_6mo, 1yr_12mo, 2yr_6mo, 2yr_12mo
• Add pathway_nodes table with:
  • Hierarchy structure (parents, ids, labels, level)
  • Patient counts and costs (value, cost, costpp, cost_pp_pa)
  • Date ranges (first_seen, last_seen, first_seen_parent, last_seen_parent)
  • Treatment statistics (average_spacing, average_administered, avg_days)
  • Denormalized filter columns (trust_name, directory, drug_sequence)
  • Foreign key to date_filter_id
• Add pathway_refresh_log table for tracking refresh status
• Create indexes for efficient filtering
• Verify schema with: python -c "from data_processing.schema import *"

1.2 Create Pathway Pipeline Module

• Create data_processing/pathway_pipeline.py with:
  • fetch_and_transform_data() - Snowflake fetch + UPID/drug/directory transformations
  • process_pathway_for_date_filter(df, date_filter_config) - Single filter processing
  • extract_denormalized_fields(ice_df) - Extract trust, directory, drug_sequence from ids
  • convert_to_records(ice_df, date_filter_id) - Convert ice_df to list of dicts for SQLite
• Integrate with existing analysis/pathway_analyzer.py functions
• Verify: python -c "from data_processing.pathway_pipeline import *"

1.3 Create Migration Script

• Create script to set up new tables in existing data/pathways.db
• Pre-populate pathway_date_filters with 6 combinations
• Verify migration runs cleanly on fresh database

Phase 2: CLI Refresh Command

2.1 Create Refresh Command

• Create cli/refresh_pathways.py with:
  • DATE_FILTER_CONFIGS constant (6 combinations)
  • compute_date_ranges(config, max_date) - Calculate actual dates from config
  • refresh_pathways(minimum_patients, provider_codes, ...) main function
• Implement refresh flow:
  1. Fetch ALL data from Snowflake (full date range)
  2. Apply transformations (UPID, drug names, directory)
  3. Clear existing pathway_nodes
  4. For each of 6 date filter configs: filter → process → insert
  5. Update pathway_refresh_log
• Add CLI argument parsing (--minimum-patients, --provider-codes, etc.)
• Verify: python -m cli.refresh_pathways --help

2.2 Test Refresh Pipeline

• Run refresh with Snowflake data
• Verify all 6 date_filter_ids populated in pathway_nodes
• Verify pathway structure matches original generate_icicle_chart() output
• Verify patient counts are correct (compare with original app)
• Document estimated processing time (expect 6-12 minutes for 440K records)

Phase 3: Reflex Integration

3.1 Update AppState

• Replace date picker state with dropdown state:
  • selected_initiated: str = "all" ("all", "1yr", "2yr")
  • selected_last_seen: str = "6mo" ("6mo", "12mo")
• Add date_filter_id computed property: f"{selected_initiated}_{selected_last_seen}"
• Rewrite load_pathway_data() to query pathway_nodes table:
  • Base filter: WHERE date_filter_id = ?
  • Trust/directory/drug filters on denormalized columns
• Add recalculate_parent_totals() for filtered hierarchies
• Update KPI calculations from root node data

3.2 Update Icicle Figure

• Update icicle_figure computed property to use all pathway_nodes columns
• Match original 10-field customdata structure:
  • values, colours, costs, costpp
  • first_seen, last_seen, first_seen_parent, last_seen_parent
  • average_spacing, cost_pp_pa
• Restore full hover/text templates from visualization/plotly_generator.py
• Verify chart renders correctly with treatment statistics

3.3 Update UI Components

• Replace date pickers with select dropdowns:
  • Initiated: "All years", "Last 2 years", "Last 1 year"
  • Last Seen: "Last 6 months", "Last 12 months"
• Add "Data refreshed: X ago" indicator from pathway_refresh_log
• Update filter section layout
• Verify UI compiles and renders correctly

Phase 4: Testing & Validation

4.1 End-to-End Validation

• Pathway hierarchy matches original: Compare specific pathway ids structure
• Patient counts match: Compare root patient count for same date range
• Treatment statistics display correctly: Verify "Average treatment duration" hover data
• Drug filtering works: Filter to FARICIMAB, verify correct pathways shown
• Chart renders with all tooltip data: Verify 10-field customdata structure

4.2 Performance Testing

• Measure filter change response time (target: <500ms)
• Measure initial page load (target: <2s including data load)
• Verify chart interaction (zoom, hover) is smooth with no lag
• Test with full dataset

4.3 Documentation

• Update CLAUDE.md with new architecture
• Document CLI usage for refresh_pathways
• Update README with new run instructions
• Document any breaking changes from original app

Completion Criteria

All tasks marked [x] AND:

• App compiles without errors (reflex run succeeds)
• All 6 date filter combinations work correctly
• Drug/directory/trust filters work with instant updates
• KPIs display correct numbers matching filter state
• Icicle chart renders with full pathway data and statistics
• Treatment duration and dosing information displays in tooltips
• No console errors during normal operation
• Verified with real patient data from Snowflake

Reference

Date Filter Combinations

ID	Initiated	Last Seen	Default
all_6mo	All years	Last 6 months	Yes
all_12mo	All years	Last 12 months	No
1yr_6mo	Last 1 year	Last 6 months	No
1yr_12mo	Last 1 year	Last 12 months	No
2yr_6mo	Last 2 years	Last 6 months	No
2yr_12mo	Last 2 years	Last 12 months	No

Key Files

File	Purpose
data_processing/schema.py	Database schema definitions
data_processing/pathway_pipeline.py	New pathway processing pipeline
cli/refresh_pathways.py	CLI refresh command
analysis/pathway_analyzer.py	Existing pathway analysis logic
visualization/plotly_generator.py	Existing chart generation
pathways_app/app_v2.py	Reflex application