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.

Learning Objective 1: Learn how machine learning models can be used to predict information seeking behavior of clinicians when using an electronic medical record system

Learning Objective 2: Understand how machine learning models are trained using data from electronic medical records


Andrew King (Presenter)
University of Pittsburgh

Gregory Cooper, University of Pittsburgh
Harry Hochheiser, University of Pittsburgh
Gilles Clermont, University of Pittsburgh
Milos Hauskrecht, University of Pittsburgh
Shyam Visweswaran, University of Pittsburgh

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