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To better 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.<\/jats:p>","DOI":"10.1145\/3314409","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T11:57:40Z","timestamp":1554206260000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":66,"title":["Clinical Data in Context"],"prefix":"10.1145","volume":"3","author":[{"given":"Shriti","family":"Raj","sequence":"first","affiliation":[{"name":"School of Information, University of Michigan, Ann Arbor, Michigan, Ann Arbor, MI, USA"}]},{"given":"Joyce M.","family":"Lee","sequence":"additional","affiliation":[{"name":"Child Health Evaluation and Research Center, Pediatric Endocrinology, University of Michigan Medical School, Ann Arbor, Michigan, MI, USA"}]},{"given":"Ashley","family":"Garrity","sequence":"additional","affiliation":[{"name":"Child Health Evaluation and Research Center, Pediatric Endocrinology, University of Michigan Medical School, Ann Arbor, Michigan, MI, USA"}]},{"given":"Mark W.","family":"Newman","sequence":"additional","affiliation":[{"name":"School of Information, University of Michigan, Ann Arbor, Michigan, Ann Arbor, MI, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,3,29]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2598510.2598598"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/07370024.2011.556552"},{"key":"e_1_2_1_3_1","volume-title":"Characterizing visualization insights from quantified selfers' personal data presentations","author":"Choe Eun Kyoung"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3154862.3154881"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556288.2557372"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2015.08.003"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/07370024.2017.1324787"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2984511.2984534"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2598510.2598558"},{"key":"e_1_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Sarah Faisal Ann Blandford and Henry WW Potts. 2013. 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