Appl Clin Inform 2020; 11(04): 680-691
DOI: 10.1055/s-0040-1709707
Research Article

Graphical Presentations of Clinical Data in a Learning Electronic Medical Record

Luca Calzoni
1  Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
,
Gilles Clermont
2  Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
,
Gregory F. Cooper
1  Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
3  Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
,
Shyam Visweswaran
1  Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
3  Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
,
Harry Hochheiser
1  Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
3  Intelligent Systems Program, University of Pittsburgh, Pittsburgh, Pennsylvania, United States
› Author Affiliations
Funding This study received its funding from U.S. Department of Health and Human Services, National Institutes of Health, U.S. National Library of Medicine (grant numbers: T15LM007059 supporting L.C. and H.H., and R01LM012095 supporting G.F.C., G.C., S.V., and H.H.).

Abstract

Background Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient.

Objectives We used insights from literature and feedback from potential users to inform the design of an EMR display capable of highlighting relevant information.

Methods We used a review of relevant literature to guide the design of preliminary paper prototypes of the LEMR user interface. We observed five ICU physicians using their current EMR systems in preparation for morning rounds. Participants were interviewed and asked to explain their interactions and challenges with the EMR systems. Findings informed the revision of our prototypes. Finally, we conducted a focus group with five ICU physicians to elicit feedback on our designs and to generate ideas for our final prototypes using participatory design methods.

Results Participating physicians expressed support for the LEMR system. Identified design requirements included the display of data essential for every patient together with diagnosis-specific data and new or significantly changed information. Respondents expressed preferences for fishbones to organize labs, mouseovers to access additional details, and unobtrusive alerts minimizing color-coding. To address the concern about possible physician overreliance on highlighting, participants suggested that non-highlighted data should remain accessible. Study findings led to revised prototypes, which will inform the development of a functional user interface.

Conclusion In the feedback we received, physicians supported pursuing the concept of a LEMR system. By introducing novel ways to support physicians' cognitive abilities, such a system has the potential to enhance physician EMR use and lead to better patient outcomes. Future plans include laboratory studies of both the utility of the proposed designs on decision-making, and the possible impact of any automation bias.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by the University of Pittsburgh Institutional Review Board (protocols 17030258, 17040082).


Supplementary Material



Publication History

Received: 17 October 2019

Accepted: 09 March 2020

Publication Date:
14 October 2020 (online)

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