ArticleBMC medical informatics and decision making2022
Under-specification as the source of ambiguity and vagueness in narrative phenotype algorithm definitions.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Application of an Externally Developed Algorithm to Identify Research Cases and Controls from EHR Data: Trials and Triumphs.Applied clinical informatics · 2025Article
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
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Authors and funding
22 authors.
Funding
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 AND
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.
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Registered trials
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