Bell Eapen

Physician | HealthIT Developer | Digital Health Consultant

About Me

I deliver health IT artifacts!

Bell Eapen

Digital Health Consultant
Bellraj (Bell) Eapen is a techie physician with expertise in JAVA, Python, .NET, Web, and Mobile development platforms. He is currently pursuing a Ph.D. in information systems at McMaster University. He has expertise in Big Data, Machine Learning and Artificial Intelligence applications in healthcare. He is a proponent of open-source software and has extensive knowledge of the OSCAR and OpenMRS EMR platforms. He has experience with frameworks such as Keras and Apache Spark. His research interests include Clinical Decision Support Systems, Cutaneous Imaging, mHealth and Public Health Informatics. He has several peer-reviewed publications, and he is a reviewer and editorial board member for few journals.

As a consultant, I deliver prototypes and models instead of reports and recommendations!

Services

Things that I work on.

Clinical Decision Support Systems

Machine Learning Dermatology CDSS from text and images.

Data Warehousing

Data warehousing and health data analytics for healthcare organizations.

Health Information System Design

Designing HIS that fit into regional frameworks.

Customize Health Information Systems

Customize OSCAR, OpenMRS, DHIS2 and RedCap

ML & AI Research in Healthcare

Support ML and AI research in healthcare

Knowledge Management

Support Knowledge Management in healthcare using FHIR

Skills

Things that I'm good at.

FHIR
90%
ML
80%
AI
90%
JAVA
80%
Python
70%
OSCAR
100%
OPENMRS
100%
TensorFlow
85%

Blog

My thoughts.

Clinical Query Language – Part 1

Clinical Query Language – Part 1

- August 5, 2020

Clinical Query Language (CQL) is a high-level query language to represent and generate unambiguous quality measures or clinical decision rules. I am not a CQL expert. These are my notes from a system development perspective (not a clinical author perspective). I am trying to make sense of this emerging concept and add my notes here […]

OHDSI OMOP to FHIR mapper

OHDSI OMOP to FHIR mapper

- July 22, 2020

TL;DR Below is an open-source common-line tool for converting an OHDSI OMOP cohort (defined in ATLAS) to a FHIR bundle and vice versa. OHDSI OMOP CDM is one of the most popular clinical data models for health data warehouses. The simple, but clinically motivated data structure is intuitively appealing to clinicians leading to its good […]

eHealth Programmer Girl

OHDSI OMOP CDM ETL Tools in Python, .Net and Go

- June 11, 2020

TL;DR Here are few OHDSI OMOP CDM tools that may save you time if you are developing ETL tools! Python: pyomop | pypi.NET: omopcdmlib | NuGetGolang: gocdm The COVID-19 pandemic brought to light many of the vulnerabilities in our data collection and analytics workflows. Lack of uniform data models limits the analytical capabilities of public […]

FHIR and public health data warehouses

- April 28, 2020

First posted on CanEHealth.com The provincial government is building a connected health care system centred around patients, families and caregivers through the newly established OHTs. As disparate healthcare and public health teams move towards a unified structure, there is a growing need to reconsider our information system strategy. Most off the shelf solutions are pricey, […]

natural language processing

NLP for Clinical Notes – Tools and Techniques

- April 21, 2020

Clinicians add clinical notes to the EMR on each visit. The clinical notes are unstructured in most cases and can benefit from NLP (natural language processing) tools and techniques. Some are created by dictation software or by medical scribes. Family physicians and family practice-centric EMRs like OSCAR EMR rely on unstructured clinical notes. Clinical notes, […]

natural language processing

Kickstart NLP with UMLS

- January 15, 2020

The UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. Natural Language Processing (NLP) on the vast amount of data captured by electronic medical records (EMR) is gaining popularity. The recent advances in machine learning (ML) algorithms […]