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The Development and Evaluation of a Learning Electronic Medical Record System

King, Andrew (2018) The Development and Evaluation of a Learning Electronic Medical Record System. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Electronic medical record (EMR) systems 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 patient data. The American Medical Association envisions EMR systems that manage information flow and adjust for context, environment, and user preferences. We developed, implemented, and evaluated a prototype Learning EMR (LEMR) system with the aim of helping make this vision a reality.
A LEMR system, as we employ the term, observes clinician information seeking behavior and applies it to direct the future display of patient data.
The development of this system was divided into five phases. First, we developed a prototype LEMR interface that served as a testing bed for LEMR experimentation. The LEMR interface was evaluated in two studies: a think aloud study and a usability study. The results from these studies were used to iteratively improve the interface. Second, we tested the accuracy of an inexpensive eye-tracking device and developed an automatic method for mapping eye gaze to patient data displayed in the LEMR interface. In the two studies we showed that an inexpensive eye-tracking device can perform as well as a costlier device intended for research and that the automatic mapping method accurately captures the patient information a user is viewing. Third, we collected observations of clinician information seeking behavior in the LEMR system. In three studies we evaluated different observation methods and applied those methods to collect training data. Fourth, we used machine learning on the training data to model clinician information seeking behavior. The models predict information that clinicians will seek in a given clinical context. Fifth, we applied the models to direct the display of patient data in a prospective evaluation of the LEMR system. The evaluation found that the system reduced the amount of time it takes for clinicians to prepare for morning rounds and highlighted about half of the patient data that clinicians seek.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
King, Andrewajk77@pitt.eduajk770000-0002-9809-0563
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCooper, Gregorygfc@pitt.edugfc
Committee MemberVisweswaran, Shyamshv3@pitt.edushv3
Committee MemberHochheiser, Harryharryh@pitt.eduharryh
Committee MemberClermont, Gillescler@pitt.educler
Date: 25 August 2018
Date Type: Publication
Defense Date: 13 July 2018
Approval Date: 25 August 2018
Submission Date: 17 August 2018
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 202
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Biomedical Informatics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Electronic Health Records; Learning Health System; Human Computer Interaction; Context Aware Systems; Machine Learning; Eye tracking;
Date Deposited: 26 Aug 2018 01:16
Last Modified: 26 Aug 2018 01:16
URI: http://d-scholarship.pitt.edu/id/eprint/35223

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