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. 2022 Jan 28;22(1):23.
doi: 10.1186/s12911-022-01759-z.

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

Jingzhi Yu  1 , Jennifer A Pacheco  2 , Anika S Ghosh  2 , Yuan Luo  2 , Chunhua Weng  3 , Ning Shang  3 , Barbara Benoit  4 , David S Carrell  5 , Robert J Carroll  6 , Ozan Dikilitas  7 , Robert R Freimuth  8 , Vivian S Gainer  4 , Hakon Hakonarson  9 , George Hripcsak  3 , Iftikhar J Kullo  7 , Frank Mentch  9 , Shawn N Murphy  4 , Peggy L Peissig  10 , Andrea H Ramirez  6 , Nephi Walton  11 , Wei-Qi Wei  6 , Luke V Rasmussen  12
Affiliations

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

Jingzhi Yu et al. BMC Med Inform Decis Mak. .

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Discussion and conclusion: 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.

Keywords: Algorithm: Natural Language; Ambiguity; Electronic Health Records (EHR); Phenotyping; Under-Specification; Vagueness.

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Conflict of interest statement

Not applicable.

Figures

Fig. 1
Fig. 1
Example of raising issues of vagueness and under-specification in the PheKB database, from the Chronic Kidney Disease phenotype. https://phekb.org/phenotype/chronic-kidney-disease
Fig. 2
Fig. 2
Categories of under-specification and other common issues identified in narrative phenotype algorithms
See this image and copyright information in PMC

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