School of Information, University of Michigan, Ann Arbor, MI 48109, United States.
Department of Medicine, Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305, United States.
J Am Med Inform Assoc. 2024 Nov 1;31(11):2455-2473. doi: 10.1093/jamia/ocae183.
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.
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.
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.
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.
Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians' experience in reviewing device data.
了解医疗保健提供者使用 GlucoGuide 的体验，GlucoGuide 是一款模拟工具，将可视化数据分析与算法洞察相结合，以支持临床医生使用来自 1 型糖尿病设备的患者生成数据。
这项定性研究分三个阶段进行。在第 1 阶段，11 名临床医生在数据走查活动中通过大声思考使用商业糖尿病平台审查数据，然后进行半结构化访谈。在第 2 阶段，开发了 GlucoGuide。在第 3 阶段，相同的临床医生在大声思考活动中使用 GlucoGuide 审查数据，然后进行半结构化访谈。使用归纳主题分析对第 1 阶段和第 3 阶段大声思考活动和访谈的转录本进行分析。
在第 1 阶段确定了 3 个高级任务、8 个子任务和 4 个挑战。在第 2 阶段，确定了 GlucoGuide 的 3 项要求。第 3 阶段的结果表明，与第 1 阶段使用的商业糖尿病数据报告相比，临床医生发现 GlucoGuide 更易于使用，认知负担更低。此外，GlucoGuide 解决了第 1 阶段遇到的挑战。
该研究表明，在实现数据接口功能时，了解分析任务和特定于任务的可视化策略的知识可以导致降低与数据交互的感知负担的工具。此外，通过对相关数据的可视化分析来支持临床医生理解算法洞察，可以积极影响临床医生利用算法支持的意愿。
结合多种数据驱动方法（如可视化策略和算法洞察）的任务对齐工具可以改善临床医生审查设备数据的体验。
