Impact of a problem-oriented view on clinical data retrieval

Author:

Semanik Michael G1,Kleinschmidt Peter C2,Wright Adam3,Willett Duwayne L4ORCID,Dean Shannon M1,Saleh Sameh N4ORCID,Co Zoe5,Sampene Emmanuel6,Buchanan Joel R2

Affiliation:

1. Department of Pediatrics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA

2. Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA

3. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA

4. Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas, USA

5. Department of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA

6. Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA

Abstract

Abstract 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.

Funder

University of Wisconsin Institute for Clinical Translational Research Novel Methods Pilot Award

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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