Using Julia for Observational Health Research

Observational health research is a domain of health informatics centered around the use of what is known as "Real World Data". This data comes in several different modalities, standards, and levels of quality. Through efforts done in JuliaHealth, JuliaInterop, and associated communities, the ability to work with this data is now fully realized. Through this talk, viewers will see how an observational health study can be conducted with Julia and how similar tools can be adapted to their research.

One patient encounter to a health care provider can produce an enormous amount of Real World Data (RWD). Per the United States Food and Drug Administration, RWD, "relates to patient health status and/or the delivery of health care routinely collected from a variety of sources." Some examples of RWD are electronic health records, medical claims, or mobile device data. Julia is primed to handle the computation required to generate clinical significance from RWD in the domain of observational health research.

Historically however, Julia's ecosystem has not been mature enough to participate directly in observational health research concerning large amounts of RWD. In the past, to effectively utilize this data the open science community, OHDSI (Observational Health Data Sciences and Informatics), was formed. The core standard that OHDSI has developed and is being rapidly adopted worldwide for handling RWD is the Observational Medical Outcomes Partnership Common Data Model - commonly referred to the OMOP CDM. Traditionally the tools built by OHDSI to interact with the OMOP CDM to extract and analyze patient information have been built in the R programming language. As a result, this has precluded other research communities from participating directly in this space.

I am pleased to announce in this talk that the Julia ecosystem has now reached a level of maturity to bridge to observational health research communities such as OHDSI to enable future observational health researchers to leverage the benefits of Julia. In this talk, I will provide a gentle introduction to observational health research and popular Common Data Models such as OMOP. This will lead into a discussion on lessons learned from an observational health study I performed called "Assessing Health Equity in Mental Healthcare Delivery Using a Federated Network Research Model" which used Julia as its main driving engine. Finally, tools available in the Julia ecosystem from JuliaHealth, JuliaInterop, and others that enable bridging between these two communities will be highlighted.

By the end of this talk, it should be made clear to potential researchers from the Julia community that the Julia ecosystem is matured enough to participate in observational health research endeavors. Furthermore, through the lessons I share through this talk, potential researchers can take inspirations on methods I used for their own work. My end goal for this talk is that by showing how these communities can be bridged, novel collaborations can be made and the benefits of using Julia can be easily accessed in observational health research.