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Patient Judgments About Hypertension Control: The Role of Variability, Trends, and Outliers in Visualized Blood
Victoria Anne Shaffer1, Pete Wegier2,3, K D Valentine1
1University of Missouri, Department of Psychological Sciences, Columbia, MO, United States.
Insights
Patient perceptions of hypertension control are swayed by how blood pressure (BP) data is visualized, including averages, trends, and outliers. Effective data visualization is crucial for accurate hypertension management decisions.
Area of Science:
- Health Informatics
- Medical Visualization
- Patient Decision Making
Background:
- Uncontrolled hypertension remains a major US health issue despite available treatments.
- Effective management requires accurate patient understanding of their condition.
Purpose of the Study:
- To investigate how data visualizations of blood pressure (BP) impact patient perceptions of hypertension control.
- To inform the development of visualizations supporting shared decision-making in hypertension treatment.
Main Methods:
- Three online studies were conducted with patients having hypertension.
- Participants reviewed vignettes with varying BP data visualizations (mean, standard deviation, trends, outliers).
- Judgments on hypertension control and need for medication change were assessed.
Main Results:
- Hypertension control judgments were significantly influenced by BP mean, standard deviation, data trends, and extreme values (outliers).
- Both clinically relevant factors (BP mean) and non-relevant factors (variability, outliers) affected patient perceptions.
Conclusions:
- Patient perception of hypertension control is susceptible to how BP data is presented visually.
- Data visualizations can inadvertently mislead patients by emphasizing non-clinical factors.
- Developing visualizations that highlight clinically meaningful information is essential for improving hypertension management.
Background:
Uncontrolled hypertension is a significant health problem in the United States, even though multiple drugs exist to effectively treat this chronic disease.
Objective:
As part of a larger project developing data visualizations to support shared decision making about hypertension treatment, we conducted a series of studies to understand how perceptions of hypertension control were impacted by data variations inherent in the visualization of blood pressure (BP) data.
Methods:
In 3 Web studies, participants (internet sample of patients with hypertension) reviewed a series of vignettes depicting patients with hypertension; each vignette included a graph of a patient's BP. We examined how data visualizations that varied by BP mean and SD (Study 1), the pattern of change over time (Study 2), and the presence of extreme values (Study 3) affected patients' judgments about hypertension control and the need for a medication change.
Results:
Participants' judgments about hypertension control were significantly influenced by BP mean and SD (Study 1), data trends (whether BP was increasing or decreasing over time-Study 2), and extreme values (ie, outliers-Study 3).
Conclusions:
Patients' judgment about hypertension control is influenced both by factors that are important predictors of hypertension related-health outcomes (eg, BP mean) and factors that are not (eg, variability and outliers). This study highlights the importance of developing data visualizations that direct attention toward clinically meaningful information.
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