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J Am Med Inform Assoc. 2018 Nov 1;25(11):1540-1546. doi: 10.1093/jamia/ocy101.

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

Author information

1
Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
2
Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
3
Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
4
Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA.
5
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
6
Meharry-Vanderbilt Alliance, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
7
Department of Pathology, Loma Linda University Health, Loma Linda, California, USA.
8
Department of Biomedical Informatics, Marshfield Clinic Research Institute, Marshfield, Wisconsin, USA.
9
Department of Biomedical Informatics, Columbia University, New York, New York, USA.
10
Research IS and Computing, Partners HealthCare, Harvard University, Somerville, Massachusetts, USA.
11
Henry Hood Center for Health Research, Geisinger, Danville, Pennsylvania, USA.
12
Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA.
13
Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
14
Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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.

PMID:
30124903
PMCID:
PMC6213083
[Available on 2019-08-16]
DOI:
10.1093/jamia/ocy101

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