“Be Really Careful about That”: Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection

This study demonstrates clinicians' interest in and reservations about IA tools for clinical deterioration. The findings underscore the importance of understanding clinicians' cognitive needs and framing IA-generated tools as complementary to support them. A clinician focuses on high-level pattern matching information, and clinician's comments related to the power of consistency with typical views (e.g., this is "how I usually see things"), and questions regarding support of score interpretation (e.g., age of the data, questions about what the model "knows") suggest some of the challenges of IA implementation. The findings also identify design implications including the need for contextualizing the EWS for the patient's specific situation, incorporating trend information, and explaining the display purpose for clinical use.

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Publication Details

Journal
Applied Clinical Informatics
Published
2025-03-01
DOI
https://doi.org/10.1055/a-2505-7743
Citations
3
Primary Topic
Clinical Reasoning and Diagnostic Skills
Type
article
Field-Weighted Citation Impact
4.55
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article

“Be Really Careful about That”: Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection

Kensaku Kawamoto, Usman Sattar, Amanda Jeppesen, Melanie C. Wright et al.
3 citations
Applied Clinical Informatics
Clinical Reasoning and Diagnostic Skills
4.55
article

“Be Really Careful about That”: Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection

Kensaku Kawamoto, Usman Sattar, Amanda Jeppesen, Melanie C. Wright, Mary A. Nies, Thomas S. Reese, Guilherme Del Fiol, Jorie Butler, Karl Madaras‐Kelly, Andrea Doubleday
article en
3 citations

Abstract

This study demonstrates clinicians' interest in and reservations about IA tools for clinical deterioration. The findings underscore the importance of understanding clinicians' cognitive needs and framing IA-generated tools as complementary to support them. A clinician focuses on high-level pattern matching information, and clinician's comments related to the power of consistency with typical views (e.g., this is "how I usually see things"), and questions regarding support of score interpretation (e.g., age of the data, questions about what the model "knows") suggest some of the challenges of IA implementation. The findings also identify design implications including the need for contextualizing the EWS for the patient's specific situation, incorporating trend information, and explaining the display purpose for clinical use.

Applied Clinical InformaticsVol. 16(02)
Idaho State University (US), Vanderbilt University (US), University of Utah (US), Geriatric Research Education and Clinical Center (US), Center for Innovation (US)
Openalex Percentile: Top 5%
Clinical Reasoning and Diagnostic Skills
4.55
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“Be Really Careful about That”: Clinicians' Perceptions of an Intelligence Augmentation Tool for In-Hospital Deterioration Detection — Kensaku Kawamoto, Usman Sattar, et al. · Applied Clinical Informatics (2025) | TGRS Research Map | TGRS