Using Machine Learning to Predict the Information Seeking Behavior of Clinicians Using an Electronic Medical Record System

Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determine the relevance of patient data in different conte...

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Bibliographic Details
Published in:AMIA ... Annual Symposium proceedings 2018, Vol.2018, p.673-682
Main Authors: King, Andrew J, Cooper, Gregory F, Hochheiser, Harry, Clermont, Gilles, Hauskrecht, Milos, Visweswaran, Shyam
Format: Article
Language:English
Subjects:
Acute Kidney Injury
Diagnostic Tests, Routine
Electronic Health Records
Humans
Information Seeking Behavior
Intensive Care Units
Machine Learning
Medical Records Systems, Computerized
Physicians
Respiratory Insufficiency
User-Computer Interface
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Summary:Poor electronic medical record (EMR) usability is detrimental to both clinicians and patients. A better EMR would provide concise, context sensitive patient data, but doing so entails the difficult task of knowing which data are relevant. To determine the relevance of patient data in different contexts, we collect and model the information seeking behavior of clinicians using a learning EMR (LEMR) system. Sufficient data were collected to train predictive models for 80 different targets (e.g., glucose level, heparin administration) and 27 of them had AUROC values of greater than 0.7. These results are encouraging considering the high variation in information seeking behavior (intraclass correlation 0.40). We plan to apply these models to a new set of patient cases and adapt the LEMR interface to highlight relevant patient data, and thus provide concise, context sensitive data.
ISSN:1559-4076