LLM-Based Clinical Decision Support System
Case Study Summary
Client: Confidential
Industry: Healthcare
Role: AI Solution Architect
Impact Metrics:
- 90% of suggestions rated relevant by clinicians
- 60% reduction in time spent finding references
- High adoption across pilot clinicians
- Transparent reasoning with source-linked citations
I built a clinical decision support tool that lets providers submit patient context and receive evidence-backed diagnostic suggestions, summaries, and next-step recommendations grounded in the latest medical guidelines.
The Challenge
Clinicians were spending too much time searching disparate guidelines and past notes before making diagnostic decisions. Rapidly changing medical knowledge made it difficult to stay current, and opaque AI outputs undermined trust in automated recommendations.
The Solution
→ Implementation
I implemented a privacy-first workflow that ingests patient details, anonymizes sensitive data, and enriches it with a retrieval-augmented pipeline. The system executes an input → retrieval → reasoning → summarization flow on Vertex AI, pulling curated medical sources before generating step-by-step clinical reasoning and suggested actions. A web dashboard surfaces summaries, cited references, and recommended tests directly in the clinician workflow.
→ Solution Architecture
Architecture diagram placeholder per request.
→ Tech Stack
- Infrastructure: Google Cloud (Vertex AI)
- Data Platform: BigQuery + Cloud Storage
- CI/CD: GitHub Actions
- Containerization: Docker + Cloud Run
- Backend: Python + FastAPI
- Modeling: LLM with retrieval-augmented generation
- Vector Index: Vertex AI Matching Engine
Key Learnings
- Grounding: RAG dramatically improved suggestion relevance by anchoring responses to trusted sources.
- Transparency: Showing step-by-step reasoning built clinician confidence in model outputs.
- Freshness: Integrating curated, frequently updated guidelines kept recommendations current.
- Workflow fit: Surfacing citations and next steps inside the dashboard boosted adoption.
Measurable Impact
- 90% of clinician ratings marked suggestions as relevant and helpful
- 60% faster access to supporting literature for each case
- Higher adoption across pilot teams due to transparent reasoning
- Increased trust in AI recommendations through cited sources
-
Let's have a virtual coffee together!
Want to see if we're a match? Let's have a chat and find out. Schedule a free 30-minute strategy session to discuss your AI challenges and explore how we can work together.