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!


Things that I work on.

Deploy healthcare machine learning pipelines on the cloud

Facilitate ML and AI research in healthcare

Chatbot & Conversational AI for clinical workflows

Design AI applications that fits enterprise architecture

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

Customize OSCAR, OpenMRS, DHIS2 and RedCap


Things that I'm good at.



My thoughts.


Locally hosted LLMs

TL; DR: From my personal experiments (on an 8-year-old, i5 laptop with 16 GB RAM), locally hosted LLMs are extremely useful for many tasks that do not require much model-captured knowledge.  Image credit: I, Luc Viatour, CC BY-SA 3.0, via Wikimedia Commons The era of relying solely on large language models (LLMs) for all-encompassing […]

CQL - Clinical Quality Language

LLM-in-the Loop CQL execution 

TL;DR: I have added experimental support for using LLMs to interpret DocumentReference-based definitions in CQL of the type: define HasVisualFootExamThisMonth:  exists(  [DocumentReference] D  where D.status.value = ‘current’ )   (Read Part I here) Image credit: Grufo, CC BY-SA 4.0, via Wikimedia Commons CQL is a domain-specific language that allows clinicians and researchers to express queries and […]

Bovee and Thill, CC BY 2.0 , via Wikimedia Commons

Come, join us to make generative AI in healthcare more accessible! 

ChatGPT captured the imagination of the healthcare world though it led to the rather misguided belief that all it needs is a chatbot application that can make API calls. A more realistic and practical way to leverage generative AI in healthcare is to focus on specific problems that can benefit from its ability to synthesize […]

Why is RAG not suitable for all Generative AI applications in healthcare?

Why is RAG not suitable for all Generative AI applications in healthcare?

Retrieval-augmented generation (RAG) is a method of generating natural language that leverages external knowledge sources, such as large-scale text corpora. RAG first retrieves a set of relevant documents for a given input query or context and then uses these documents as additional input for a neural language model that generates the output text. RAG aims to improve the factual accuracy, diversity, […]

Grounding vs RAG in Healthcare Applications 

Grounding vs RAG in Healthcare Applications 

Both Grounding and RAG (Retrieval-Augmented Generation) play significant roles in enhancing LLMs capabilities and effectiveness and reducing hallucinations. In this post, I delve into the subtle differences between RAG and grounding, exploring their use in generative AI applications in healthcare.  What is RAG?  RAG, short for Retrieval-Augmented Generation, represents a paradigm shift in the field […]

To or not to LangChain

To or not to LangChain

LangChain is a free and accessible coordination framework for building applications that rely on large language models (LLMs). Although it is widely used, it sometimes receives critiques such as being complex, insecure, unscalable, and hard to maintain. As a novel framework, some of these critiques might be valid, but they might also be a strategy […]