Build an end-to-end analytics solution on Microsoft Fabric that helps a hospital system improve patient outcomes, reduce costs, and meet regulatory requirements.
Healthcare providers — hospital administrators, clinical informaticists, data analysts, IT leaders, and quality improvement teams — who want to understand how Microsoft Fabric can unify their data platform.
| Time | Module | Duration | Fabric Capability |
|---|---|---|---|
| 9:00 – 9:20 | Module 0 — Introduction & Healthcare Context | 20 min | Overview |
| 9:20 – 9:50 | Module 1 — Lakehouse & Data Ingestion | 30 min | Lakehouse, Upload, Spark Notebook |
| 9:50 – 10:35 | Module 2 — Data Engineering | 45 min | Spark Notebooks, Delta Tables |
| 10:35 – 10:45 | Break | 10 min | |
| 10:45 – 11:30 | Module 3 — Semantic Model, Dashboard & Power BI Copilot | 45 min | Semantic Model, Power BI, Copilot |
| 11:30 – 12:30 | Module 4 — Real-Time Analytics | 60 min | Eventhouse, KQL, Real-Time Dashboard |
| 12:30 – 1:15 | Lunch Break | 45 min | |
| 1:15 – 1:55 | Module 5 — Data Agent | 40 min | Prep Data for AI, Fabric Data Agent |
| 1:55 – 2:25 | Module 6 — Gen AI: Clinical Intelligence | 30 min | Notebooks, Fabric AI Services |
| 2:25 – 3:25 | Module 7 — Predicting Readmissions with AutoML | 45–60 min | Azure OpenAI, FLAML AutoML, MLflow |
| 3:25 – 3:40 | Wrap-Up & Q&A | 15 min | |
| (Optional) | Module 8 — Building with VS Code Agent Mode | 60–90 min | GitHub Copilot Agent Mode, VS Code |
| (Optional) | Module 9 — Data Agent Evaluation (LLM-as-Judge) | 30 min | Data Agent SDK, LLM-as-Judge, Semantic Link |
| (Optional) | Module 10 — Fabric IQ: Ontology & Plan | 45–60 min | Fabric IQ, Ontology, Plan, Data Agent |
| (Optional) | Module 11 — AI Search + Data Agent (Structured + Unstructured) | 45–60 min | Azure AI Search, Fabric Data Agent |
Total core day: 5.5 hours (9:00 AM – 3:30 PM including breaks and lunch). Optional modules can be done independently after the lab.
FabricHackathon/
├── README.md ← You are here
├── lab-guide/
│ ├── Module00_Introduction.md ← Healthcare challenges & lab overview
│ ├── Module01_Setup_and_Data_Ingestion.md ← Lakehouse + Data Pipeline alt path
│ ├── Module02_Data_Engineering.md
│ ├── Module03_Semantic_Model_and_Dashboard.md ← + Power BI Copilot
│ ├── Module04_RealTime_Analytics.md
│ ├── Module05_Data_Agent.md ← Prep Data for AI + Data Agent
│ ├── Module06_GenAI_Clinical_Intelligence.md ← AI-powered clinical note analysis
│ ├── Module07_Readmission_Prediction.md ← GenAI-powered readmission prediction with AutoML
│ ├── Module08_VSCode_Agent_Mode.md ← (Optional) Build entire lab with AI agent
│ ├── Module09_Data_Agent_Evaluation.md ← (Optional) LLM-as-Judge evaluation pipeline
│ ├── Module10_Fabric_IQ.md ← (Optional) Ontology + Plan with LLM-assisted design
│ └── Module11_AI_Search_and_Data_Agent_Integration.md ← (Optional) Combine unstructured + structured responses
├── data/
│ ├── generate_healthcare_data.py ← Python script to regenerate data
│ ├── ai_search_sample_docs/ ← Sample unstructured docs for Azure AI Search (Module 11)
│ ├── patients.csv ← 200 synthetic patients
│ ├── encounters.csv ← ~1,000 hospital encounters
│ ├── conditions.csv ← 428 diagnoses (ICD-10 coded)
│ ├── medications.csv ← 640 medication records
│ ├── vitals.csv ← 3,800+ vital sign readings
│ ├── clinical_notes.csv ← 150 clinical notes (for Gen AI)
│ └── claims.csv ← ~1,000 billing/claims records
├── notebooks/
│ ├── 01_Bronze_Data_Ingestion.py ← With FHIR mapping & data exploration
│ ├── 02_Silver_Transformations.py ← With ICD-10 & SIRS clinical context
│ ├── 03_Gold_Analytics.py ← With KPI business rationale
│ ├── 04_GenAI_Clinical_Notes.py ← With prompt engineering explanations
│ ├── 05_RealTime_Vitals_Simulator.py ← With patient archetype & SIRS docs
│ ├── 06_Predictive_Readmission_Model.py ← GenAI feature engineering + XGBoost
│ └── 07_Data_Agent_Evaluation.py ← LLM-as-Judge evaluation pipeline
└── resources/
├── kql_queries.kql ← KQL queries for real-time analytics
└── dax_measures.md ← DAX measures for semantic model
- A Microsoft Fabric workspace (capacity F64 or higher recommended)
- A web browser (Microsoft Edge or Google Chrome)
- The synthetic CSV data files from the
data/folder (pre-generated) - For Module 6 (Gen AI): An Azure OpenAI Service endpoint with a GPT-4o deployment
By the end of this lab, you will have:
- A Lakehouse with Bronze → Silver → Gold data layers containing clinical, operational, and financial healthcare data
- Spark Notebooks that compute hospital quality measures including 30-day readmission rates, average length of stay, and ED utilization
- A Semantic Model (star schema) with measures for readmission rate, bed occupancy, and revenue analysis
- A Power BI Dashboard with pages for Patient Volume & Flow, Quality & Readmissions, and Population Health — plus Power BI Copilot testing
- A Real-Time Dashboard monitoring simulated patient vitals with sepsis early-warning detection
- A Data Agent that lets you ask questions about your healthcare data in natural language, with Prep Data for AI configuration
- A Gen AI Notebook that summarizes clinical notes and suggests ICD-10 codes
- A Readmission Prediction Model using Gen AI-assisted feature engineering and AutoML (FLAML)
- (Optional) VS Code Agent Mode — experience building the entire lab through conversational AI with GitHub Copilot
- (Optional) Readmission Prediction Model — Gen AI-assisted feature engineering with XGBoost to predict 30-day hospital readmissions and generate patient risk scores
- (Optional) AI Search + Data Agent Integration — combine Azure AI Search document context with Fabric metrics in one grounded response
- All data in this lab is 100% synthetic. No real patient information (PHI) is used.
- The data was generated using a Python script (
data/generate_healthcare_data.py) with realistic distributions but fictional names and records. - This lab is for educational purposes and demonstrates Fabric's capabilities. Production healthcare solutions require additional security, compliance (HIPAA), and governance controls.