Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions

• Published in: BMC medical informatics and decision making 2022-01, Vol.22 (1), p.23-23, Article 23
• Main Authors: Yu, Jingzhi, Pacheco, Jennifer A, Ghosh, Anika S, Luo, Yuan, Weng, Chunhua, Shang, Ning, Benoit, Barbara, Carrell, David S, Carroll, Robert J, Dikilitas, Ozan, Freimuth, Robert R, Gainer, Vivian S, Hakonarson, Hakon, Hripcsak, George, Kullo, Iftikhar J, Mentch, Frank, Murphy, Shawn N, Peissig, Peggy L, Ramirez, Andrea H, Walton, Nephi, Wei, Wei-Qi, Rasmussen, Luke V
• Format: Article
• Language: English
• Subjects: Algorithm: Natural Language; Algorithms; Ambiguity; Analysis; Codes; Communication; Data warehouses; Electronic health records; Electronic Health Records (EHR); Electronic medical records; Electronic records; Genetic aspects; Genotype & phenotype; Identification and classification; Informatics; Knowledge bases (artificial intelligence); Language; Medical records; Medical research; Natural language; Patients; Phenotype; Phenotypes; Phenotyping; Psychological aspects; Specifications; Taxonomy; Under-Specification; Vagueness
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-022-01759-z

Currently, 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. This 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. We 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. Our 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.