Healthcare AI: Unifying Clinical Data for Real-Time Insights
A healthcare provider had patient data spread across five clinical systems. We built a FHIR-compliant integration layer and added AI analytics on top.
The Challenge
What was getting in the way
- 01
Patient data lived in five separate systems. Clinicians had to check multiple screens for a single patient view
- 02
No predictive analytics. Clinical decisions relied entirely on manual chart review
- 03
Existing systems used different data formats, so reconciling records took hours of manual work each week
The Solution
How we solved it
We mapped data from all five source systems to FHIR R4, built an integration layer on Azure, and piped everything into a unified data store. On top of that, we trained TensorFlow models on anonymized clinical data to flag high-risk patients. Clinicians got a Power BI dashboard with real-time alerts and trend views.
Technologies
What We Built
A look inside the project
The Process
Step-by-step delivery
Data Integration
Connect five clinical data sources into one pipeline
FHIR Mapping
Convert legacy formats to FHIR R4 standard
AI Model Training
Train predictive models on anonymized patient data
Clinical Dashboard
Build Power BI dashboards for clinician use
Decision Support
Deploy real-time alerts for high-risk patients
The Results
The numbers
Clinical Insights Delivery
System Integration Achieved
Faster Clinical Decisions