Language: English | Deutsch
Cross-country Health Technology Assessment (HTA) analysis powered by AI.
Compare drug approval decisions between G-BA (Germany) and NICE (UK) through interactive analytics, side-by-side drug comparisons, and an AI-powered knowledge base chat.
Market Access teams in pharma spend weeks manually comparing HTA decisions across countries. G-BA and NICE evaluate the same drugs but use fundamentally different frameworks (clinical benefit vs. cost-effectiveness), making cross-country analysis complex and time-consuming.
Studies show only 54-72% concordance between agencies for the same drugs - understanding why decisions diverge is critical for market access strategy.
Market Access Intelligence automates the entire workflow:
- 2,300+ assessments from G-BA (998) and NICE (1,312) in a unified database
- Cross-country matching linking the same drug across agencies by INN
- RAG-powered chat that answers questions using assessment data + PDF decision documents
- PDF analysis of G-BA "Tragende Gruende" (decision rationale) for clinical detail beyond metadata
- KPI overview: assessment counts, matched drugs, concordance rate
- Outcome distribution (positive/restricted/negative) by agency
- Concordance matrix (G-BA vs NICE)
- Assessment timeline by year
- Side-by-side comparison for any matched drug
- All assessments per agency with indication, rating, outcome, date
- Direct links to source documents
- Ask questions in German or English
- Hybrid search: vector similarity + keyword matching + semantic reranking
- SQL routing for aggregate queries ("all orphan drugs", "how many")
- PDF-backed answers citing clinical endpoints and decision rationale
- Source citations with links to original documents
Example queries:
- "Compare G-BA and NICE decisions for pembrolizumab"
- "Welche Orphan Drugs wurden 2024 negativ bewertet?"
- "What mortality endpoints were evaluated for nivolumab in NSCLC?"
Data Sources Processing Frontend
+-----------------+
| G-BA XML |---+
| (POST -> S3) | | +-------------+
+-----------------+ +----->| Importer |---+
+-----------------+ | | (UPSERT) | |
| NICE Excel |---+ +-------------+ | +------------+
| (HTTP GET) | +--->| Azure SQL |
+-----------------+ | | Database |
+-----------------+ +-------------+ | +-----+------+
| G-BA PDFs |--------->| PDF Parser |----+ |
| (Tragende | | + Chunker | +----+----+
| Gruende) | +-------------+ | |
+-----------------+ +---+---+ +---+---+
|Embedder| |SQL |
|256-dim | |Routing|
+---+---+ +---+---+
| |
+---+---+ |
| Azure | |
| AI | |
| Search | |
+---+---+ |
| |
+---+---------+--+
| RAG Chat |
| (GPT-4o) |
+-------+-------+
|
+-------+-------+
| Streamlit |
| Dashboard |
+---------------+
| Layer | Technology |
|---|---|
| Database | Azure SQL (Serverless) |
| Search | Azure AI Search (hybrid: vector + BM25 + semantic) |
| Embeddings | Azure OpenAI text-embedding-3-small (256-dim) |
| Chat | Azure OpenAI GPT-4o |
| PDF Parsing | pdfplumber |
| Frontend | Streamlit |
| Language | Python 3.11+ |
| Source | Agency | Records | Content |
|---|---|---|---|
| G-BA XML | G-BA (Germany) | 998 assessments | Benefit ratings, subgroups, endpoints, comparators |
| NICE Excel | NICE (UK) | 1,312 appraisals | Recommendations, categorisations, comments |
| G-BA PDFs | G-BA (Germany) | 2,376 available | "Tragende Gruende" decision rationale documents |
G-BA rates clinical added benefit (Zusatznutzen). NICE evaluates cost-effectiveness (ICER threshold). These are fundamentally different measures.
We preserve original ratings and only harmonize to a ternary outcome for cross-country comparison:
| positive | restricted | negative | |
|---|---|---|---|
| G-BA | erheblich, betraechtlich, gering, nicht quantifizierbar, gilt als belegt | nicht quantifizierbar (insufficient evidence) | ist nicht belegt, geringerer Nutzen |
| NICE | Recommended (incl. CDF, IMF) | Optimised, Only in research | Not recommended |
- Python 3.11+
- Azure SQL Database
- Azure AI Search service
- Azure OpenAI deployment (text-embedding-3-small + GPT-4o)
git clone https://github.com/leelesemann-sys/market-access-intelligence.git
cd market-access-intelligence
pip install -r requirements.txtCopy the environment template and fill in your Azure credentials:
cp backend/.env.example backend/.envRequired variables:
HTA_SQL_SERVER=your-server.database.windows.net
HTA_SQL_DATABASE=your_db
HTA_SQL_USER=your_user
HTA_SQL_PASSWORD=your_password
HTA_SEARCH_ENDPOINT=https://your-search.search.windows.net
HTA_SEARCH_KEY=your_search_admin_key
HTA_OPENAI_ENDPOINT=https://your-region.api.cognitive.microsoft.com/
HTA_OPENAI_KEY=your_openai_key
Deploy the schema to your Azure SQL database:
# Apply schema.sql and schema_phase1d.sql to your database# Import G-BA + NICE data
python backend/run_pipeline.py --source all
# Build search index (embed + index)
python backend/run_pipeline.py --index
# Process G-BA PDF documents (Tragende Gruende)
python backend/run_pipeline.py --pdfs --pdf-limit 10 # test with 10
python backend/run_pipeline.py --pdfs --skip-scrape # full run (use cached URLs)streamlit run backend/app.pymarket-access-intelligence/
├── backend/
│ ├── sources/ # Data source adapters (G-BA XML, NICE Excel)
│ ├── pipeline/ # ETL: import, embed, index, PDF processing
│ ├── chat/ # RAG: hybrid retrieval + GPT-4o response
│ ├── app.py # Streamlit dashboard (3 tabs)
│ ├── run_pipeline.py # CLI orchestration
│ ├── config.py # Central configuration
│ └── db.py # Database connection
├── sql/ # Database schema
├── docs/ # Architecture documentation
└── requirements.txt
This project is licensed under the MIT License.
G-BA data is subject to G-BA usage terms. NICE data is publicly available under NICE terms.