A web-based application for analyzing secondary care patient treatment pathways. It processes clinical activity data to visualize hierarchical treatment patterns (Trust → Directory/Specialty → Drug → Patient pathway) as interactive Plotly icicle charts.

Features

Desktop App: Native window experience via pywebview (no browser needed)
Interactive Visualization: Plotly icicle charts showing patient treatment hierarchies with cost and frequency statistics
Dual Chart Types: Directory-based (Trust → Directorate → Drug → Pathway) and Indication-based (Trust → GP Diagnosis → Drug → Pathway) views
Pre-computed Pathways: Treatment pathways pre-processed and stored in SQLite for sub-50ms filter response times
GP Diagnosis Matching: Patient indications matched from GP records using SNOMED cluster codes (~93% match rate)
Trend Analysis: Historical trend views showing how drug usage and costs change over time
Modern Web Interface: Dash (Plotly) + Dash Mantine Components with NHS branding
Flexible Filtering: Filter by date range, NHS trusts, drugs, and medical directories

Requirements

Python 3.10 or higher
uv package manager (recommended)

Optional (for data refresh)

Access to NHS Snowflake data warehouse with SSO authentication

Installation

# Clone the repository
git clone <repository-url>
cd patient-pathway-analysis

# Install dependencies
uv sync

Quick Start

Run the Application

# Run as desktop app (recommended)
python app_desktop.py

# Run in browser (development)
python run_dash.py

The desktop app opens automatically in a native window. For browser mode, open http://localhost:8050.

The application loads pre-computed pathway data from SQLite on startup. No additional configuration is needed for viewing existing data.

Refresh Pathway Data (requires Snowflake)

# Initialize/migrate the database
python -m data_processing.migrate

# Full refresh — both chart types, all date filters
python -m cli.refresh_pathways --chart-type all

# Directory charts only (faster, ~5 minutes)
python -m cli.refresh_pathways --chart-type directory

# Indication charts only (~12 minutes, includes GP lookup)
python -m cli.refresh_pathways --chart-type indication

# Dry run (test without database changes)
python -m cli.refresh_pathways --chart-type all --dry-run -v

Compute Trends (for Trends view):

# Compute historical trend snapshots
python -m cli.compute_trends

# Custom date range
python -m cli.compute_trends --start 2022-01-01 --end 2025-06-30

# Help
python -m cli.compute_trends --help

Usage

Interface Overview

The application has a single-page layout with:

Component	Purpose
Header	NHS branding, fraction KPIs (patients, drugs, cost), data freshness indicator
Sidebar	Navigation: Patient Pathways, Trust Comparison, Trends
Sub-Header	Chart type toggle (By Directory / By Indication) + date filter dropdowns
Filter Bar	Patient Pathways drug/trust/directorate filter buttons with modals
Chart Card	9-tab chart area (Icicle, Sankey, Heatmap, Funnel, Depth, Scatter, Network, Timeline, Doses)
Trust Comparison	Per-directorate 6-chart dashboard comparing drugs across trusts
Trends	Historical trend analysis with directorate overview + drug drill-down

Filtering Data

The application has three analytical views:

Patient Pathways: Icicle chart + 8 additional analytics tabs with drug/trust/directorate filtering
Trust Comparison: Per-directorate analysis comparing drugs across trusts
Trends: Historical trend analysis showing directorate and drug-level changes over time

Common controls across all views:

Chart Type: Toggle between "By Directory" and "By Indication" views
Date Filters: Select treatment initiation period and last-seen window
Drug/Trust/Directorate Selection: Open modals to filter by specific drugs, trusts, or directorates (Patient Pathways)
Clear Filters: Reset all selections to show full dataset

Understanding the Pathway Chart

The icicle chart displays hierarchical treatment pathways:

Root (Regional Total)
  └─ Trust Name (e.g., "Norfolk and Norwich University Hospitals")
      └─ Directory/Indication (e.g., "Rheumatology" or "rheumatoid arthritis")
          └─ Drug Name (e.g., "ADALIMUMAB")
              └─ Treatment Pathway (e.g., "ADALIMUMAB → INFLIXIMAB")

Width: Relative patient count
Color intensity: Proportion of parent group
Hover: Shows cost, dosing frequency, date range, and per-patient statistics
Click: Zoom into a specific branch

Date Filter Combinations

Initiated	Last Seen	Description
All years	Last 6 months	Default — all patients active recently
All years	Last 12 months	Broader activity window
Last 1 year	Last 6 months	Recently initiated, active
Last 1 year	Last 12 months	Recently initiated, any activity
Last 2 years	Last 6 months	Medium history, active
Last 2 years	Last 12 months	Medium history, any activity

Project Structure

.
├── core/                        # Foundation: paths, models, logging
├── config/                      # Snowflake connection settings
├── data_processing/             # Data layer (SQLite, Snowflake, transforms)
├── analysis/                    # Analysis pipeline
├── visualization/               # Plotly chart generation
├── cli/                         # CLI tools (refresh_pathways, compute_trends)
├── dash_app/                    # Dash web application
│   ├── app.py                   # App entry point, layout, stores
│   ├── assets/nhs.css           # NHS design system CSS
│   ├── data/                    # Query wrappers + card browser data
│   ├── components/              # UI components (header, sidebar, chart_card, trends, etc.)
│   └── callbacks/               # Dash callbacks (filters, chart, KPI, trends, etc.)
├── app_desktop.py               # Desktop entry point (pywebview native window)
├── run_dash.py                  # Browser entry point
├── app.spec                     # PyInstaller packaging spec
├── data/                        # Reference data + SQLite DB (pathways.db)
├── tests/                       # Test suite (114 tests)
├── docs/                        # Documentation
└── archive/                     # Historical/deprecated code

See CLAUDE.md for detailed architecture documentation.

Running Tests

# Run all tests
python -m pytest tests/ -v

# Run with coverage (114 tests)
python -m pytest tests/ -v --cov=core --cov=data_processing --cov=analysis

# Run only fast tests
python -m pytest tests/ -v -m "not slow"

Configuration

Desktop Packaging

# Build standalone executable (Windows)
pyinstaller app.spec

# Output: dist/NHS_Pathway_Analysis/NHS_Pathway_Analysis.exe

Snowflake Connection (`config/snowflake.toml`)

[snowflake]
account = "your-account"
database = "DATA_HUB"
schema = "CDM"
warehouse = "your-warehouse"
authenticator = "externalbrowser"  # Required for NHS SSO

Troubleshooting

App won't start

# Ensure dependencies are installed
uv sync

# Try desktop mode
python app_desktop.py

# Or browser mode
python run_dash.py

Database not found

# Check data/pathways.db exists
python -m data_processing.migrate

Snowflake connection issues

Ensure config/snowflake.toml has the correct account identifier
A browser window will open for SSO authentication
Verify your network allows Snowflake connections

Documentation

CLAUDE.md — Technical architecture documentation
docs/USER_GUIDE.md — End-user guide
docs/DEPLOYMENT.md — Deployment guide

License

Internal NHS use only. Not for distribution.

Support

For questions or issues, contact the Medicines Intelligence team.

README.md

NHS High-Cost Drug Patient Pathway Analysis Tool