title: Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System.
abstract: Electronic medical records (EMRs) are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient's clinical state, better ways are needed to determine when and how to display EMR data. We built a prototype system that records how physicians view EMR data, which we used to train models that predict which EMR data will be relevant in a given patient. We call this approach a Learning EMR (LEMR). A physician used the prototype to review 59 intensive care unit (ICU) patient cases. We used the data-access patterns from these cases to train logistic regression models that, when evaluated, had AUROC values as high as 0.92 and that averaged 0.73, supporting that the approach is promising. A preliminary usability study identified advantages of the system and a few concerns about implementation. Overall, 3 of 4 ICU physicians were enthusiastic about features of the prototype.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: King
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Harry
authors.initials: H
authors.lastname: Hochheiser
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
mesh_terms: D003422:Critical Care; D057286:Electronic Health Records; D006801:Humans; D007362:Intensive Care Units; D000069550:Machine Learning; D010820:Physicians