Impact of a problem-oriented view on clinical data retrieval Article Swipe
Michael G. Semanik
,
Peter Kleinschmidt
,
Adam Wright
,
DuWayne L. Willett
,
Shannon M. Dean
,
Sameh N. Saleh
,
Zoe Co
,
Emmanuel Sampene
,
Joel R Buchanan
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa332
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa332
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.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocaa332
- https://academic.oup.com/jamia/article-pdf/28/5/899/37334934/ocaa332.pdf
- OA Status
- bronze
- Cited By
- 23
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3128566614
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3128566614Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocaa332Digital Object Identifier
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Impact of a problem-oriented view on clinical data retrievalWork title
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articleOpenAlex work type
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enPrimary language
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2021Year of publication
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2021-01-06Full publication date if available
- Authors
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Michael G. Semanik, Peter Kleinschmidt, Adam Wright, DuWayne L. Willett, Shannon M. Dean, Sameh N. Saleh, Zoe Co, Emmanuel Sampene, Joel R BuchananList of authors in order
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https://doi.org/10.1093/jamia/ocaa332Publisher landing page
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https://academic.oup.com/jamia/article-pdf/28/5/899/37334934/ocaa332.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/jamia/article-pdf/28/5/899/37334934/ocaa332.pdfDirect OA link when available
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Computer science, Information retrievalTop concepts (fields/topics) attached by OpenAlex
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23Total citation count in OpenAlex
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2025: 3, 2024: 3, 2023: 5, 2022: 8, 2021: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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