Bell Eapen MD, PhD.

Bringing Digital health & Gen AI research to life!

About Me

Translational research in health IT is my passion.

Bell Eapen MD, PhD.

Digital Health & Gen AI Consultant
I am Bellraj (Bell) Eapen, a techie MD (dermatologist) with a PhD in Information Systems from McMaster University. As an R&D lead engineer at Mayo clinic, I specialize in cloud architecture for Gen AI and multimodal machine learning using FHIR.

I maintain several software libraries on GitHub. I facilitate the adoption of Generative AI and FHIR in healthcare organizations. As a consultant, I deliver prototypes & models instead of reports and recommendations!

Checkout my GitHub repo and Contact Me for your next project!

Services

Things that I work on.

Cloud Architecture for ML

Deploy healthcare machine learning pipelines on the cloud

ML & AI Research in Healthcare

Facilitate ML and AI research in healthcare

Generative AI

Chatbot & Conversational AI for clinical workflows

AI Application Design

Design AI applications that fits enterprise architecture

FHIR & Analytics

Data warehousing and health data analytics for healthcare (FHIR & OHDSI OMOP)

Customize Health Information Systems

Customize OSCAR, OpenMRS, DHIS2 and RedCap

Skills

Things that I'm good at.

FHIR
90%
GCP
80%
AI
90%
JAVA
80%
Python
70%
OSCAR EMR
100%
DERMATOLOGY
100%
TensorFlow
85%

Blog

My thoughts.

Vibe coding in health informatics

Four takeaways from vibe coding

- October 29, 2025

A successful asynchronous session and synchronous session output.

CRISP-T: AI assisted qualitative research!

CRISP-T: Bridging Text, Numbers, and AI for Smarter Qualitative Research

- October 14, 2025

CRISP-T is a tool for researchers navigating the complexities of qualitative analysis on mixed data types. In fields like healthcare, education, and social sciences, qualitative data—interviews, open-ended surveys, field notes—often hold the richest insights. Yet, integrating this with structured numeric data has traditionally been cumbersome. CRISP-T addresses this gap by offering a unified framework that […]

Pyomop: Python package for managing OHDSI clinical data models. Includes support for LLM based plain text queries!

Vibe Coding FHIR to OMOP

- August 22, 2025

TL;DR: A clinician‑researcher can download vocabularies, point at a folder of FHIR Bulk Export files, and be querying in OMOP CDM in an afternoon. This function, generated by vibe coding using these prompts, would help you do just that! 🎵 What is Vibe Coding? Vibe coding is an AI‑assisted development approach popularized by Andrej Karpathy. […]

FHIRy: FHIR to pandas dataframe for data analytics, AI and ML!

🔍 Why FHIRy Matters

- August 6, 2025

In the evolving landscape of health information systems, interoperability is no longer a luxury—it’s a necessity. The Fast Healthcare Interoperability Resources (FHIR) standard, developed by HL7, has emerged as a cornerstone for structuring and exchanging electronic health data. But while FHIR excels at standardization and data sharing, it stumbles when faced with the demands of […]

AI in healthcare: Image credit: RMHare, CC0, via Wikimedia Commons

Building a Modular Framework for Generative AI in Healthcare

- July 18, 2025

A reference architecture designed to accelerate experimentation, deployment, and collaboration in healthcare AI.

LLM-in-the-Loop CQL execution

V. LLM-in-the-Loop CQL execution with unstructured data and FHIR terminology support

- June 4, 2025

Here is how you can run the CQL execution with unstructured data and FHIR terminology support using our LLM-in-the-Loop stack as presented at AMIA #CIC25.