Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms

TL;DR

This paper evaluates openEHR as a formal standard for creating machine-readable, shareable representations of phenotyping algorithms in electronic health records to improve reproducibility and implementation.

Contribution

It provides an assessment of openEHR's suitability for encoding phenotyping algorithms, addressing the lack of a common standard.

Findings

openEHR can represent phenotyping algorithms formally

Potential for improved sharing and reproducibility of algorithms

Identifies challenges in implementing openEHR for this purpose

Abstract

Electronic Health Records (EHR) are data generated during routine clinical care. EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the pace of precision medicine at scale. A main EHR use-case is creating phenotyping algorithms to define disease status, onset and severity. Currently, no common machine-readable standard exists for defining phenotyping algorithms which often are stored in human-readable formats. As a result, the translation of algorithms to implementation code is challenging and sharing across the scientific community is problematic. In this paper, we evaluate openEHR, a formal EHR data specification, for computable representations of EHR phenotyping algorithms.