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

Jennifer A. Pacheco, Luke V. Rasmussen, Richard C. Kiefer, Thomas R. Campion, Peter Speltz, Robert J. Carroll, Sarah C. Stallings, Huan Mo, Monika Ahuja, Guoqian Jiang, Eric R. LaRose, Peggy L. Peissig, Ning Shang, Barbara Benoit, Vivian S. Gainer, Kenneth Borthwick, Kathryn L. Jackson, Ambrish Sharma, Andy Yizhou Wu, Abel N. KhoDan M. Roden, Jyotishman Pathak, Joshua C. Denny, William K. Thompson

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1540-1546
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume25
Issue number11
DOIs
StatePublished - Nov 1 2018

Keywords

  • Algorithms
  • Architecture
  • Electronic health records
  • Genomics
  • Phenotype
  • Prostatic hyperplasia

ASJC Scopus subject areas

  • Health Informatics

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    Pacheco, J. A., Rasmussen, L. V., Kiefer, R. C., Campion, T. R., Speltz, P., Carroll, R. J., Stallings, S. C., Mo, H., Ahuja, M., Jiang, G., LaRose, E. R., Peissig, P. L., Shang, N., Benoit, B., Gainer, V. S., Borthwick, K., Jackson, K. L., Sharma, A., Wu, A. Y., ... Thompson, W. K. (2018). A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments. Journal of the American Medical Informatics Association, 25(11), 1540-1546. https://doi.org/10.1093/jamia/ocy101