AI-Powered Healthcare Diagnosis System
Clinical decision support to improve review speed and patient outcomes.
Key Details
| Challenge | Clinicians spent too long gathering prior notes before a decision. |
|---|---|
| Solution | A review copilot that summarizes history and flags guideline-relevant findings — with a human in the loop. |
| Technologies | HIPAA-ready cloud, LLM + RAG, FHIR APIs |
Technologies used
Azure
OpenAI
Python
PostgreSQL
Docker
Client background
A specialty clinic network asked clinicians to reconstruct patient history from fragmented EHR notes before each decision. Review time crowded out patient time, and guideline-relevant findings were easy to miss under load.
Key challenges
- Prior notes were long, duplicated and hard to scan under appointment pressure.
- No consistent way to surface guideline-relevant findings with citations.
- Privacy and audit requirements blocked naive cloud LLM experiments.
- Clinicians refused tools that tried to replace judgment instead of assisting it.
What we built
- Review copilot that summarizes history with citations into source notes.
- Guideline-aware flags presented as suggestions, never autonomous diagnoses.
- FHIR-oriented integrations and HIPAA-ready hosting with access logging.
- Decision log so every accepted or rejected suggestion is auditable.
Project team: 8 engineers across AI/ML, backend and domain specialists — delivery over 20 weeks.
How we delivered
01
Shadow
Observed clinic workflows and mapped where review time was lost.
02
Pilot
Deployed in one specialty with clear safety and success criteria.
03
Integrate
Connected EHR reads via FHIR with least-privilege access.
04
Govern
Locked human-in-the-loop policy and monitoring for drift.
Business impact
FasterChart review
CitedSuggestions
LoggedDecisions