Augmenting clinicians' analytical workflow through task-based integration of data visualizations and algorithmic insights: a user-centered design study.

Autor:	Scholich T; School of Information, University of Michigan, Ann Arbor, MI 48109, United States., Raj S; Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, United States.; Institute for Human-Centered AI, Stanford University, Stanford, CA 94305, United States., Lee J; Susan B. Meister Child Health Evaluation and Research Center (CHEAR), University of Michigan, Ann Arbor, MI 48109, United States.; Division of Pediatric Endocrinology, University of Michigan, Ann Arbor, MI 48109, United States., Newman MW; School of Information, University of Michigan, Ann Arbor, MI 48109, United States.; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, United States.
Jazyk:	angličtina
Zdroj:	Journal of the American Medical Informatics Association : JAMIA [J Am Med Inform Assoc] 2024 Nov 01; Vol. 31 (11), pp. 2455-2473.
DOI:	10.1093/jamia/ocae183
Abstrakt:	Objectives: To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices. Materials and Methods: This qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview. Results: 3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1. Discussion: The study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians' willingness to leverage algorithmic support. Conclusion: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data. (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)
Databáze:	MEDLINE
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