Abstract：
OBJECTIVE: 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.
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.
CONCLUSIONS: 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.
CONCLUSIONS: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians\' experience in reviewing device data.
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.
CONCLUSIONS: 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.
CONCLUSIONS: Task-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians\' experience in reviewing device data.
摘要：
目的：了解医疗保健提供者使用GlucoGuide的经验，一种模型工具，它将视觉数据分析与算法见解集成在一起，以支持临床医生使用患者从1型糖尿病设备生成的数据。
方法：这项定性研究分三个阶段进行。在第1阶段，11名临床医生在一次大声思考的数据演练活动中使用商业糖尿病平台审查数据，然后进行半结构化访谈。在阶段2中,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。归纳主题分析用于分析第1阶段和第3阶段的大声思考活动和访谈的笔录。
结果：3个高级任务，8个子任务，在第一阶段确定了4个挑战。在阶段2中,确定了对GlucoGuide的3个要求。第3阶段结果表明，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，并且认知负担更低。此外,GlucoGuide解决了第一阶段所面临的挑战。
结论：该研究表明，在实现数据接口的功能时，分析任务和特定任务的可视化策略的知识可以产生降低与数据互动的感知负担的工具。此外,通过对相关数据的视觉分析,支持临床医生将算法洞察情境化,可以积极影响临床医生利用算法支持的意愿.
结论：结合多种数据驱动方法的任务对齐工具，例如可视化策略和算法见解，可以提高临床医生审查设备数据的经验。
方法：这项定性研究分三个阶段进行。在第1阶段，11名临床医生在一次大声思考的数据演练活动中使用商业糖尿病平台审查数据，然后进行半结构化访谈。在阶段2中,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。归纳主题分析用于分析第1阶段和第3阶段的大声思考活动和访谈的笔录。
结果：3个高级任务，8个子任务，在第一阶段确定了4个挑战。在阶段2中,确定了对GlucoGuide的3个要求。第3阶段结果表明，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，并且认知负担更低。此外,GlucoGuide解决了第一阶段所面临的挑战。
结论：该研究表明，在实现数据接口的功能时，分析任务和特定任务的可视化策略的知识可以产生降低与数据互动的感知负担的工具。此外,通过对相关数据的视觉分析,支持临床医生将算法洞察情境化,可以积极影响临床医生利用算法支持的意愿.
结论：结合多种数据驱动方法的任务对齐工具，例如可视化策略和算法见解，可以提高临床医生审查设备数据的经验。
