Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning | Zendy

Bob Chen | Zendy; Mhd Wael Alrifai | Zendy; Cheng Gao | Zendy; Barrett Jones | Zendy; Laurie L. Novak | Zendy; Nancy M. Lorenzi | Zendy; Daniel J. France | Zendy; Bradley Malin | Zendy; You Chen | Zendy

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Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning

Author(s) -

Bob Chen,

Mhd Wael Alrifai,

Cheng Gao,

Barrett Jones,

Laurie L. Novak,

Nancy M. Lorenzi,

Daniel J. France,

Bradley Malin,

You Chen

Publication year - 2020

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1093/jamia/ocaa338

Subject(s) - audit , workflow , computer science , task (project management) , workload , categorization , audit trail , sample (material) , event (particle physics) , electronic health record , process mining , machine learning , artificial intelligence , health care , database , chemistry , physics , business process , management , geochemistry , chromatography , quantum mechanics , business process modeling , compatibility (geochemistry) , geology , economics , economic growth , operating system

The characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics.

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