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Treffer: Impact of a problem-oriented view on clinical data retrieval.

Title:
Impact of a problem-oriented view on clinical data retrieval.
Authors:
Semanik MG; Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA., Kleinschmidt PC; Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA., Wright A; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA., Willett DL; Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA., Dean SM; Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA., Saleh SN; Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA., Co Z; Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA., Sampene E; Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA., Buchanan JR; Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA.
Source:
Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2021 Apr 23; Vol. 28 (5), pp. 899-906.
Publication Type:
Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9430800 Publication Model: Print Cited Medium: Internet ISSN: 1527-974X (Electronic) Linking ISSN: 10675027 NLM ISO Abbreviation: J Am Med Inform Assoc Subsets: MEDLINE
Imprint Name(s):
Publication: 2015- : Oxford : Oxford University Press
Original Publication: Philadelphia, PA : Hanley & Belfus, c1993-
MeSH Terms:
Data Display* , Electronic Health Records* , Medical Records, Problem-Oriented*, Humans ; Information Storage and Retrieval ; User-Computer Interface ; Workflow
References:
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Grant Information:
UL1 TR002373 United States TR NCATS NIH HHS
Contributed Indexing:
Keywords: clinical decision support systems; data display; electronic health records; medical records; problem-oriented; user-computer interface
Entry Date(s):
Date Created: 20210210 Date Completed: 20210826 Latest Revision: 20240401
Update Code:
20250114
PubMed Central ID:
PMC8068438
DOI:
10.1093/jamia/ocaa332
PMID:
33566093
Database:
MEDLINE

Weitere Informationen

Objective: The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).
Methods: Simple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.
Results: With POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001).
Discussion: The study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.
Conclusion: EHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.
(© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)