Cleaned up old reflex files/legacy code etc
This commit is contained in:
Andrew Charlwood
@@ -4,12 +4,13 @@ A web-based application for analyzing secondary care patient treatment pathways.
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## Features
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- **Desktop App**: Native window experience via pywebview (no browser needed)
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- **Interactive Visualization**: Plotly icicle charts showing patient treatment hierarchies with cost and frequency statistics
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- **Dual Chart Types**: Directory-based (Trust → Directorate → Drug → Pathway) and Indication-based (Trust → GP Diagnosis → Drug → Pathway) views
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- **Pre-computed Pathways**: Treatment pathways pre-processed and stored in SQLite for sub-50ms filter response times
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- **GP Diagnosis Matching**: Patient indications matched from GP records using SNOMED cluster codes (~93% match rate)
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- **Modern Web Interface**: Browser-based UI using Dash (Plotly) + Dash Mantine Components with NHS branding
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- **Drug Browser**: Drawer-based card browser organized by clinical directorate for drug/indication selection
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- **Trend Analysis**: Historical trend views showing how drug usage and costs change over time
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- **Modern Web Interface**: Dash (Plotly) + Dash Mantine Components with NHS branding
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- **Flexible Filtering**: Filter by date range, NHS trusts, drugs, and medical directories
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## Requirements
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@@ -29,20 +30,21 @@ cd patient-pathway-analysis
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# Install dependencies
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uv sync
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# One-time dev setup: adds src/ to Python path via .pth file
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uv run python setup_dev.py
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```
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## Quick Start
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### Run the Web Application
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### Run the Application
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```bash
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# Run as desktop app (recommended)
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python app_desktop.py
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# Run in browser (development)
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python run_dash.py
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```
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Open http://localhost:8050 in your browser.
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The desktop app opens automatically in a native window. For browser mode, open http://localhost:8050.
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The application loads pre-computed pathway data from SQLite on startup. No additional configuration is needed for viewing existing data.
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@@ -65,6 +67,19 @@ python -m cli.refresh_pathways --chart-type indication
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python -m cli.refresh_pathways --chart-type all --dry-run -v
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```
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**Compute Trends (for Trends view):**
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```bash
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# Compute historical trend snapshots
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python -m cli.compute_trends
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# Custom date range
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python -m cli.compute_trends --start 2022-01-01 --end 2025-06-30
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# Help
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python -m cli.compute_trends --help
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```
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## Usage
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### Interface Overview
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@@ -73,21 +88,28 @@ The application has a single-page layout with:
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| Component | Purpose |
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|-----------|---------|
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| **Header** | NHS branding, data freshness indicator (patient count + relative time) |
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| **Sidebar** | Navigation items with drawer triggers for Drug Selection, Trust Selection, Indications |
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| **KPI Row** | 4 cards: Unique Patients, Drug Types, Total Cost, Indication Match Rate |
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| **Filter Bar** | Chart type toggle (By Directory / By Indication) + date filter dropdowns |
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| **Chart Card** | Interactive Plotly icicle chart with loading spinner |
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| **Drawer** | Right-side panel with drug chips, trust chips, and directorate card browser |
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| **Header** | NHS branding, fraction KPIs (patients, drugs, cost), data freshness indicator |
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| **Sidebar** | Navigation: Patient Pathways, Trust Comparison, Trends |
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| **Sub-Header** | Chart type toggle (By Directory / By Indication) + date filter dropdowns |
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| **Filter Bar** | Patient Pathways drug/trust/directorate filter buttons with modals |
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| **Chart Card** | 9-tab chart area (Icicle, Sankey, Heatmap, Funnel, Depth, Scatter, Network, Timeline, Doses) |
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| **Trust Comparison** | Per-directorate 6-chart dashboard comparing drugs across trusts |
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| **Trends** | Historical trend analysis with directorate overview + drug drill-down |
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### Filtering Data
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1. **Chart Type**: Toggle between "By Directory" and "By Indication" views
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2. **Date Filters**: Select treatment initiation period and last-seen window
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3. **Drug Selection**: Open the drawer to select specific drugs via chips
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4. **Trust Selection**: Open the drawer to filter by NHS trusts
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5. **Directorate Browser**: Navigate directorates → indications → drug fragments in the drawer
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6. **Clear Filters**: Reset all selections to show full dataset
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The application has three analytical views:
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1. **Patient Pathways**: Icicle chart + 8 additional analytics tabs with drug/trust/directorate filtering
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2. **Trust Comparison**: Per-directorate analysis comparing drugs across trusts
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3. **Trends**: Historical trend analysis showing directorate and drug-level changes over time
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Common controls across all views:
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- **Chart Type**: Toggle between "By Directory" and "By Indication" views
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- **Date Filters**: Select treatment initiation period and last-seen window
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- **Drug/Trust/Directorate Selection**: Open modals to filter by specific drugs, trusts, or directorates (Patient Pathways)
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- **Clear Filters**: Reset all selections to show full dataset
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### Understanding the Pathway Chart
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@@ -121,22 +143,23 @@ Root (Regional Total)
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```
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.
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├── src/ # All application library code
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│ ├── core/ # Foundation: paths, models, logging
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│ ├── config/ # Snowflake connection settings
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│ ├── data_processing/ # Data layer (SQLite, Snowflake, transforms)
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│ ├── analysis/ # Analysis pipeline
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│ ├── visualization/ # Plotly chart generation
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│ └── cli/ # CLI tools (refresh_pathways)
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├── core/ # Foundation: paths, models, logging
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├── config/ # Snowflake connection settings
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├── data_processing/ # Data layer (SQLite, Snowflake, transforms)
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├── analysis/ # Analysis pipeline
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├── visualization/ # Plotly chart generation
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├── cli/ # CLI tools (refresh_pathways, compute_trends)
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├── dash_app/ # Dash web application
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│ ├── app.py # App entry point, layout, stores
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│ ├── assets/nhs.css # NHS design system CSS
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│ ├── data/ # Query wrappers + card browser data
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│ ├── components/ # UI components (header, sidebar, etc.)
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│ └── callbacks/ # Dash callbacks (filters, chart, KPI, drawer)
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├── run_dash.py # Entry point: python run_dash.py
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│ ├── components/ # UI components (header, sidebar, chart_card, trends, etc.)
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│ └── callbacks/ # Dash callbacks (filters, chart, KPI, trends, etc.)
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├── app_desktop.py # Desktop entry point (pywebview native window)
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├── run_dash.py # Browser entry point
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├── app.spec # PyInstaller packaging spec
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├── data/ # Reference data + SQLite DB (pathways.db)
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├── tests/ # Test suite (113 tests)
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├── tests/ # Test suite (114 tests)
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├── docs/ # Documentation
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└── archive/ # Historical/deprecated code
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```
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@@ -149,7 +172,7 @@ See `CLAUDE.md` for detailed architecture documentation.
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# Run all tests
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python -m pytest tests/ -v
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# Run with coverage
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# Run with coverage (114 tests)
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python -m pytest tests/ -v --cov=core --cov=data_processing --cov=analysis
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# Run only fast tests
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@@ -158,7 +181,16 @@ python -m pytest tests/ -v -m "not slow"
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## Configuration
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### Snowflake Connection (`src/config/snowflake.toml`)
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### Desktop Packaging
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```bash
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# Build standalone executable (Windows)
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pyinstaller app.spec
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# Output: dist/NHS_Pathway_Analysis/NHS_Pathway_Analysis.exe
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```
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### Snowflake Connection (`config/snowflake.toml`)
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```toml
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[snowflake]
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@@ -177,11 +209,11 @@ authenticator = "externalbrowser" # Required for NHS SSO
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# Ensure dependencies are installed
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uv sync
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# Ensure src/ is on Python path
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uv run python setup_dev.py
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# Try desktop mode
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python app_desktop.py
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# Try running with uv
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uv run python run_dash.py
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# Or browser mode
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python run_dash.py
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```
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### Database not found
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@@ -193,7 +225,7 @@ python -m data_processing.migrate
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### Snowflake connection issues
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1. Ensure `src/config/snowflake.toml` has the correct account identifier
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1. Ensure `config/snowflake.toml` has the correct account identifier
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2. A browser window will open for SSO authentication
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3. Verify your network allows Snowflake connections
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