Bell Eapen

Physician | HealthIT Developer | Digital Health Consultant

About Me

I deliver health IT artifacts!

Bell Eapen MD, PhD.

Digital Health Consultant
Bellraj (Bell) Eapen is a techie physician with an MSc in eHealth and PhD in Information Systems from McMaster University. He is a proponent of Machine Learning and Artificial Intelligence applications in dermatology. He is proficient in open-source software development and maintains several software libraries on GitHub. He actively supports OSCAR and OpenMRS EMR platforms and is an advocate of FHIR, big data and blockchain adoption in healthcare. His research interests include Clinical Decision Support Systems (CDSS), Bioinformatics, Computational Biology, Cutaneous Imaging, mHealth and Public Health Informatics. He has several peer-reviewed publications.

I help healthcare organizations solve problems related to interoperability and analytics through the adoption of FHIR and OMOP data models, mainly for OSCAR EMR and OpenMRS platforms. I facilitate the adoption of machine learning and artificial intelligence in healthcare organizations leveraging NLP on clinical records. As a consultant, I deliver prototypes & models instead of reports and recommendations! My GitHub repo at has many sample projects.


Things that I work on.

Machine Learning Dermatology CDSS from text and images.

Data warehousing and health data analytics for healthcare organizations.

Designing HIS that fit into regional frameworks.

Customize OSCAR, OpenMRS, DHIS2 and RedCap

Support ML and AI research in healthcare

Support Knowledge Management in healthcare using FHIR


Things that I'm good at.



My thoughts.

Six things data scientists in healthcare should know

Healthcare, like most other fields, is eager to get on the data science bandwagon. Data scientists can make a huge difference in the way big data is utilized for clinical decision-making. However, there are paradigmatic differences in the way data scientists from quantitative fields view the world, compared to their clinical counterparts. This is especially […]

eHealth Programmer Girl

Open-source for healthcare

This post is meant to be an instruction guide for healthcare professionals who would like to join my projects on GitHub. What is a contribution? Contribution is not always coding. You can clean up stuff, add documentation, instructions for others to follow etc. Issues and feature requests should be posted under the ‘issues’ tab and […]

artificial intelligence

Clinical knowledge representation for reuse

The need for computerized clinical decision support is becoming increasingly obvious with the COVID-19 pandemic. The initial emphasis has been on ‘replacing’ the clinician which for a variety of reasons is impossible or impractical. Pragmatically, clinical decision support systems could provide clinical knowledge support for clinicians to make time-sensitive decisions with whatever information they have […]

SPQR10, CC BY-SA 4.0 , via Wikimedia Commons

COVID vaccination tracking with blockchain

COVID vaccine rollout has the potential to bring relief to billions of people around the world. But as encouraging as these programs may be, it is extremely important to note that a vaccine cannot be as effective if it is not effectively distributed and trusted by the public. IBM Blockchain has a vaccine distribution network […]

mohamed_hassan @

Chatting with FHIR endpoint

FHIR is an emerging standard for exchanging healthcare information electronically. Searching for resources is fundamental to the mechanics of FHIR. Search operations traverse through an existing set of resources filtering by parameters supplied to the search operation. health information systems convert the clinicians’ interactions into the search string. With the growing importance (and intelligence) of […]

Siobhán Grayson, CC BY-SA 4.0, via Wikimedia Commons

Embeddings in healthcare: TypingDNA and Skinmesh

Neural networks (NN) are everywhere, from image analytics to NLP to clinical decision support systems. Embeddings are a popular class of techniques that emerged out of NN with diverse applications. embedding is a low-dimensional translation of high dimensional space. For clinicians, embedding is nothing but a simple representation of complex data. Embeddings are typically used […]