A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments Academic Article uri icon

Overview

MeSH Major

  • Depressive Disorder, Major
  • Electronic Health Records
  • Primary Health Care

abstract

  • Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.

publication date

  • November 2018

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC6213083

Digital Object Identifier (DOI)

  • 10.1093/jamia/ocy101

PubMed ID

  • 30124903

Additional Document Info

start page

  • 1540

end page

  • 1546

volume

  • 25

number

  • 11