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AMIA Annu Symp Proc. 2015 Nov 5;2015:1967-75. eCollection 2015.

Development and Preliminary Evaluation of a Prototype of a Learning Electronic Medical Record System.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
2
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA; Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA.
3
Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.

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.

PMID:
26958296
PMCID:
PMC4765593
[Indexed for MEDLINE]
Free PMC Article

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