Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control

Victoria A. Shaffer, Pete Wegier, K. D. Valentine, Jeffery L. Belden, Shannon M. Canfield, Mihail Popescu, Linsey M. Steege, Akshay Jain and Richelle J. Koopman
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Victoria A. Shaffer: Department of Psychological Sciences, University of Missouri, Columbia, MO, USA
Pete Wegier: Temmy Latner Centre for Palliative Care, Sinai Health System and University of Toronto, Toronto, Ontario, Canada
K. D. Valentine: Health Decision Sciences Center, Massachusetts General Hospital and Harvard Medical School, Cambridge, MA, USA
Jeffery L. Belden: Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA
Shannon M. Canfield: Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA
Mihail Popescu: Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA
Linsey M. Steege: School of Nursing, University of Wisconsin–Madison, Madison, WI, USA
Akshay Jain: Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO, USA
Richelle J. Koopman: Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA

Medical Decision Making, 2020, vol. 40, issue 6, 785-796

Abstract: Objective . Uncontrolled hypertension is driven by clinical uncertainty around blood pressure data. This research sought to determine whether decision support—in the form of enhanced data visualization—could improve judgments about hypertension control. Methods . Participants (Internet sample of patients with hypertension) in 3 studies ( N = 209) viewed graphs depicting blood pressure data for fictitious patients. For each graph, participants rated hypertension control, need for medication change, and perceived risk of heart attack and stroke. In study 3, participants also recalled the percentage of blood pressure measurements outside of the goal range. The graphs varied by systolic blood pressure mean and standard deviation, change in blood pressure values over time, and data visualization type. Results . In all 3 studies, data visualization type significantly affected judgments of hypertension control. In studies 1 and 2, perceived hypertension control was lower while perceived need for medication change and subjective perceptions of stroke and heart attack risk were higher for raw data displays compared with enhanced visualization that employed a smoothing function generated by the locally weighted smoothing algorithm. In general, perceptions of hypertension control were more closely aligned with clinical guidelines when data visualization included a smoothing function. However, conclusions were mixed when comparing tabular presentations of data to graphical presentations of data in study 3. Hypertension was perceived to be less well controlled when data were presented in a graph rather than a table, but recall was more accurate. Conclusion . Enhancing data visualization with the use of a smoothing function to minimize the variability present in raw blood pressure data significantly improved judgments about hypertension control. More research is needed to determine the contexts in which graphs are superior to data tables.

Keywords: hypertension; data visualization; decision support; risk perception; chronic disease management (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:6:p:785-796

DOI: 10.1177/0272989X20940999

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