Techno-functional leader bridging EHR systems, healthcare standards (FHIR · openEHR · TEFCA),
and applied AI (RAG · Knowledge Graphs · MCP · Medical Imaging) —
from architecture and prototypes to product delivery.
I lead and build at the intersection of healthcare standards, applied AI, and product delivery — combining hands-on engineering with program ownership:
- 🎯 Program & Product Leadership — driving applied-AI initiatives end-to-end: discovery → architecture → prototype → delivery
- 🔌 Healthcare Interoperability — FHIR, openEHR, HL7, SMART-on-FHIR, Epic & Cerner integrations, TEFCA
- 🤖 Applied AI / Clinical AI — RAG, knowledge graphs, LLM agents, MCP servers for healthcare data
- 🏗️ Engineering Depth — .NET 8, Python, microservices, clean architecture, GraphQL APIs
- 🩻 Medical Imaging AI — DICOM, MONAI, radiology pipelines
- 🤝 Techno-functional bridge — translating between clinicians, product, and engineering
- 🧮 AI-augmented practitioner — daily driver of GitHub Copilot, Claude Code & ChatGPT (mostly plan-mode / agentic workflows) for architecture, prototyping, code review, and documentation
Sorted by relevance, not date. See pinned repos below or my full repository list.
| Project | Stack | What it does |
|---|---|---|
| fhir-mcp-suite ⭐ | Python | A suite of Model Context Protocol (MCP) servers for FHIR — letting LLMs query clinical data safely |
| healthcare-graphql-api ⭐ | .NET 8, HotChocolate | Production-ready Healthcare GraphQL API with JWT auth, caching, rate limiting, Docker |
| openEHR-trialcapture ⭐ | TypeScript | Clinical trial data capture using openEHR archetypes |
| python-healthcare-api-microservices ⭐ | Python | Healthcare API in a microservices pattern |
| TEFCA-Knowledge | HTML / Docs | A practitioner's hub for TEFCA + FHIR + Clinical AI |
| EpicSmartBackendApp-4-1-Github | C# | SMART-on-FHIR backend application using Epic |
| CleanArchitectureHealthcareAPI | C# | Clean-architecture .NET healthcare API reference |
| Project | Stack | What it does |
|---|---|---|
| bodhi_app ⭐ | FastAPI · React · Neo4j | ClinIQ · BODHI — full-stack clinical knowledge-graph app on the Bharat Ontology for Disease & Healthcare Informatics (Eka Care) |
| fhir-mapping--agent ⭐ | Python | LLM agent for mapping data into FHIR resources |
| KnowledgeRAG | Python | Retrieval-augmented generation over knowledge corpora |
| GraphRAG | Python | Graph-based RAG implementation |
| pnumoniaApp-Monai 🚧 | Python / MONAI | Pneumonia detection on chest images using MONAI (WIP) |
| RAdImageProcessing 🚧 | Python | Radiology image processing pipeline (WIP) |
| Project | Stack | What it does |
|---|---|---|
| nodejs-healthcare-api | Node.js | Healthcare API on Node.js |
| Azure-Functions-HandsOn | C# / Azure | Serverless on Azure Functions |
| devops-pipeline-github | GitHub Actions | CI/CD pipelines with GitHub Actions |
Browse all repos by topic:
#fhir·#healthcare·#clinical-ai·#dotnet
Languages
Backend & Cloud
Healthcare Standards
AI / ML
AI-Augmented Workflow (daily drivers)
I work with AI agents — not just to autocomplete code, but as collaborative thinking partners. A typical loop on any non-trivial work:
- Plan-mode first — talk through the problem, constraints and trade-offs with Claude / Copilot agent before writing a single line
- Architecture artefact — produce the design doc / sequence diagram / FHIR resource map with the AI, then review it critically
- Iterate in small slices — each prototype tested with the AI as a reviewer (security, edge cases, OWASP)
- Documentation as a first-class output — every repo gets a real README, not an afterthought
This is how nearly every repo here was built — it's also how I run program work: structured AI-augmented thinking compresses discovery and design cycles dramatically.
- 🛠️
fhir-mcp-suite— extending MCP server coverage for more FHIR resources - 📚
TEFCA-Knowledge— building out v1.0 of the practitioner hub - 🧬 openEHR data capture apps — ADHD/autism screening + clinical trial capture
- Radiology & Medical Imaging AI — chest X-ray classification with MONAI, DICOM pipelines, PACS integration via MCP. A dedicated showcase project is planned — watch this space.
- 💼 LinkedIn: https://linkedin.com/in/paragmedsinge
- 📧 Email: paragmedsinge@yahoo.com
- 🌍 Based in: Pune, Maharashtra, India
- 💬 Open to: leadership roles, advisory engagements & collaborations in healthcare interoperability, applied AI, and clinical-AI product development
⚡ Note: Repos here are my hands-on lab — most solve a real interoperability or clinical-data problem from program work, not tutorials. I work in the open: expect a mix of WIP prototypes and stable showcases, each clearly labelled.