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Appl Clin Inform 2019; 10(05): 849-858
DOI: 10.1055/s-0039-1698465
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

Benjamin R. Kummer
1   Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
,
Joshua Z. Willey
2   Department of Neurology, Columbia University, New York, New York, United States
,
Michael J. Zelenetz
3   Department of Analytics, New York Presbyterian Hospital, New York, New York, United States
,
Yiping Hu
4   Department of Biomedical Informatics, Columbia University, New York, New York, United States
,
Soumitra Sengupta
4   Department of Biomedical Informatics, Columbia University, New York, New York, United States
,
Mitchell S. V. Elkind
2   Department of Neurology, Columbia University, New York, New York, United States
5   Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York, United States
,
George Hripcsak
4   Department of Biomedical Informatics, Columbia University, New York, New York, United States
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Publikationsverlauf

19. Mai 2019

28. August 2019

Publikationsdatum:
06. November 2019 (online)

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Abstract

Background Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR.

Objectives This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard.

Methods We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR.

Results By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5).

Conclusion At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

Keywords

clinical decision support systems - data aggregation - neurology - organizational efficiency - referral and consultation - inpatients

Protection of Human and Animal Subjects

The institutional review board of CUIMC approved the use of consultation data for this analysis, and waived the requirement for informed consent.


 

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