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Use of Enhanced Data Visualization to Improve Patient Judgments about Hypertension Control.
Shaffer, Victoria A; Wegier, Pete; Valentine, K D; Belden, Jeffery L; Canfield, Shannon M; Popescu, Mihail; Steege, Linsey M; Jain, Akshay; Koopman, Richelle J.
Afiliação
  • Shaffer VA; Department of Psychological Sciences, University of Missouri, Columbia, MO, USA.
  • Wegier P; Temmy Latner Centre for Palliative Care, Sinai Health System and University of Toronto, Toronto, Ontario, Canada.
  • Valentine KD; Health Decision Sciences Center, Massachusetts General Hospital and Harvard Medical School, Cambridge, MA, USA.
  • Belden JL; Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA.
  • Canfield SM; Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA.
  • Popescu M; Department of Health Management and Informatics, University of Missouri, Columbia, MO, USA.
  • Steege LM; School of Nursing, University of Wisconsin-Madison, Madison, WI, USA.
  • Jain A; Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO, USA.
  • Koopman RJ; Department of Family and Community Medicine, University of Missouri, Columbia, MO, USA.
Med Decis Making ; 40(6): 785-796, 2020 08.
Article em En | MEDLINE | ID: mdl-32696711
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pacientes / Visualização de Dados / Hipertensão Tipo de estudo: Guideline / Prognostic_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pacientes / Visualização de Dados / Hipertensão Tipo de estudo: Guideline / Prognostic_studies Limite: Adult / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2020 Tipo de documento: Article