Citations 2
Under-specification as the Source of Ambiguity and Vagueness in Narrative Phenotype Algorithm Definitions
Yu, Jingzhi, Ghosh, Anika, Shang, Ning , Benoit, Barbara, Carrell, David, Carroll, Robert L., Gainer, Vivian S., Håkonarson, Håkon , Mentch, Frank , Murphy, Shawn N. , Peissig, Peggy , Ramirez, Andrea, Walton, Nephi , Wei, Wei‐Qi, Carroll, Robert J. , Hakonarson, Hakon , Yu, Jingzhi, Pacheco, Jennifer A. , Luo, Yuan , Weng, Chunhua , Carroll, Robert, Dikilitas, Ozan , Freimuth, Robert R. , Hripcsak, George , Kullo, Iftikhar J. , Rasmussen, Luke V.
No container title available | August 12, 2021 | Volume: No volume available | Issue: No issue available | Pages: No page available
Abstract IntroductionCurrently, one of the commonly used methods for disseminating electronic health record (EHR)-based phenotype algorithms is providing a narrative description of the algorithm logic, often accompanied by flowcharts. A challenge with this mode of dissemination is the potential for under-specification in the algorithm definition, which leads to ambiguity and vagueness. MethodsThis study examines incidents of under-specification that occurred during the implementation of 34 narrative phenotyping algorithms in the electronic Medical Record and Genomics (eMERGE) network. We reviewed the online communication history between algorithm developers and implementers within the Phenotype Knowledge Base (PheKB) platform, where questions could be raised and answered regarding the intended implementation of a phenotype algorithm. ResultsWe developed a taxonomy of under-specification categories via an iterative review process between two groups of annotators. Under-specifications that lead to ambiguity and vagueness were consistently found across narrative phenotype algorithms developed by all involved eMERGE sites. Discussion & ConclusionOur findings highlight that under-specification is an impediment to the accuracy and efficiency of the implementation of current narrative phenotyping algorithms, and we propose approaches for mitigating these issues and improved methods for disseminating EHR phenotyping algorithms.
A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments
Pacheco, Jennifer A. , Rasmussen, Luke V. , Kiefer, Richard C. , Speltz, Peter, Carroll, Robert J. , Stallings, Sarah , Mo, Huan , Ahuja, Monika, Jiang, Guoqian , LaRose, Eric , Peissig, Peggy , Shang, Ning , Benoit, Barbara, Gainer, Vivian S., Borthwick, Kenneth M., Jackson, Kathryn , Sharma, Ambrish Kumar, Wu, Andy , Roden, Dan M. , Pathak, Jyotishman , Denny, Joshua C. , Thompson, William K. , Denny, Joshua C , Rasmussen, Luke V , Pacheco, Jennifer A , Campion, Thomas R , Wu, Andy Yizhou, Kho, Abel , Roden, Dan M.
Journal of the American Medical Informatics Association | July 14, 2018 | Volume: 25 | Issue: 11 | Pages: 1540-1546
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.