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.
Highlights
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
The field of EHR-based phenotyping has expanded in the past decade, with progress led by multiple groups such as the electronic Medical Record and Genomics Network [3, 4], The Patient-Centered Outcomes Research Network (PCORnet) [5], the Informatics for Integrating Biology & the Bedside (i2b2) community [6], and the Observational Health Data Sciences and Informatics (OHDSI) consortium [7]
Extending the biomedical query mediation (BQM) process analysis work [10] to further our understanding of under-specification, ambiguity, and vagueness in narrative phenotype algorithms, we investigated the communication involving the implementation of a collection of narrative phenotype algorithms developed by the electronic Medical Record and Genomics (eMERGE) Network
Summary
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. The field of EHR-based phenotyping has expanded in the past decade, with progress led by multiple groups such as the electronic Medical Record and Genomics (eMERGE) Network [3, 4], The Patient-Centered Outcomes Research Network (PCORnet) [5], the Informatics for Integrating Biology & the Bedside (i2b2) community [6], and the Observational Health Data Sciences and Informatics (OHDSI) consortium [7]. Within eMERGE, a phenotype algorithm is typically developed by one institution and implemented and validated by at least one other institution for evaluation and tuning to enhance portability before wider release. These phenotype algorithms have been represented as narrative descriptions of the logic, which each institution would translate into executable code to query a local data warehouse
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