Clinical data in context: Towards sensemaking tools for interpreting personal health data

被引：36

作者：

Raj, Shriti ^{[1
]}

Lee, Joyce M. ^{[2
]}

Garrity, Ashley ^{[2
]}

Newman, Mark W. ^{[3
]}

机构：

[1] School of Information, University of Michigan, 105 S. State St., Ann Arbor,MI,48109, United States

[2] Child Health Evaluation and Research Center, Pediatric Endocrinology, University of Michigan Medical School, 300 North Ingalls St., Ann Arbor,MI,48104, United States

[3] School of Information, Department of EECS, University of Michigan, 105 S. State St., Ann Arbor,MI,48109, United States

来源：

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies | 2019年 / 3卷 / 01期

关键词：

Diseases - Medical problems;

D O I：

10.1145/3314409

中图分类号：

学科分类号：

摘要：

Clinical data augmented with contextual data can help patients with chronic conditions make sense of their disease. However, existing tools do not support interpretation of multiple data streams. To beter understand how individuals make sense of clinical and contextual data, we interviewed patients with Type 1 diabetes and their caregivers using context-enhanced visualizations of patients' data as probes to facilitate interpretation activities. We observed that our participants performed four analytical activities when interpreting their data - finding context-based trends and explaining them, triangulating multiple factors, suggesting context-specific actions, and hypothesizing about alternate contextual factors affecting outcomes. We also observed two challenges encountered during analysis - the inability to identify clear trends challenged action planning and counterintuitive insights compromised trust in data. Situating our findings within the existing sensemaking frameworks, we demonstrate that sensemaking can not only inform action but can guide the discovery of information needs for exploration. We further argue that sensemaking is a valuable approach for exploring contextual data. Informed by our findings and our reflection on existing sensemaking frameworks, we provide design guidelines for sensemaking tools to improve awareness of contextual factors affecting patients and to support patients' agency in making sense of health data. © 2019 Association for Computing Machinery.

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