Inpatient nurses' preferences and decisions with risk information visualization.

clinical clinical decision rules decision-support systems electronic health records medical informatics risk prediction display

Journal

Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
Titre abrégé: J Am Med Inform Assoc
Pays: England
ID NLM: 9430800

Informations de publication

Date de publication:
31 Oct 2023
Historique:
received: 05 06 2023
revised: 10 09 2023
accepted: 09 10 2023
medline: 30 10 2023
pubmed: 30 10 2023
entrez: 30 10 2023
Statut: aheadofprint

Résumé

We examined the influence of 4 different risk information formats on inpatient nurses' preferences and decisions with an acute clinical deterioration decision-support system. We conducted a comparative usability evaluation in which participants provided responses to multiple user interface options in a simulated setting. We collected qualitative data using think aloud methods. We collected quantitative data by asking participants which action they would perform after each time point in 3 different patient scenarios. More participants (n = 6) preferred the probability format over relative risk ratios (n = 2), absolute differences (n = 2), and number of persons out of 100 (n = 0). Participants liked average lines, having a trend graph to supplement the risk estimate, and consistent colors between trend graphs and possible actions. Participants did not like too much text information or the presence of confidence intervals. From a decision-making perspective, use of the probability format was associated with greater concordance in actions taken by participants compared to the other 3 risk information formats. By focusing on nurses' preferences and decisions with several risk information display formats and collecting both qualitative and quantitative data, we have provided meaningful insights for the design of clinical decision-support systems containing complex quantitative information. This study adds to our knowledge of presenting risk information to nurses within clinical decision-support systems. We encourage those developing risk-based systems for inpatient nurses to consider expressing risk in a probability format and include a graph (with average line) to display the patient's recent trends.

Identifiants

pubmed: 37903375
pii: 7333907
doi: 10.1093/jamia/ocad209
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Patient-Centered Outcomes Research Institute
ID : K12HS026395
Pays : United States

Informations de copyright

Published by Oxford University Press on behalf of the American Medical Informatics Association 2023.

Auteurs

Alvin D Jeffery (AD)

School of Nursing, Vanderbilt University, Nashville, TN 37240, United States.
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Tennessee Valley Healthcare System, United States Department of Veterans Affairs, Nashville, TN 37212, United States.

Carrie Reale (C)

Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.

Janelle Faiman (J)

Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.

Vera Borkowski (V)

School of Nursing, Vanderbilt University, Nashville, TN 37240, United States.

Russ Beebe (R)

Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.

Michael E Matheny (ME)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Tennessee Valley Healthcare System, United States Department of Veterans Affairs, Nashville, TN 37212, United States.
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, United States.

Shilo Anders (S)

Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
Center for Research and Innovation in Systems Safety, Vanderbilt University Medical Center, Nashville, TN 37232, United States.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN 37232, United States.

Classifications MeSH