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Machine Learning to Reduce Hypertension Treatment Clinical Inertia
Sponsor: Temple University
Summary
Among individuals with an uncontrolled BP at the current visit, the objective of this study is to compare clinical management of hypertension with and without information from a machine learning algorithm on whether a patient will have uncontrolled blood pressure at their next follow up visit through a case-vignette study.
Key Details
Gender
All
Age Range
20 Years - Any
Study Type
INTERVENTIONAL
Enrollment
50
Start Date
2025-04-25
Completion Date
2025-07-31
Last Updated
2025-04-10
Healthy Volunteers
Yes
Conditions
Interventions
Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm
The investigators have created a machine learning algorithm to predict uncontrolled blood pressure (BP) status (yes/no) at a follow up visit among adults with uncontrolled BP at their current visit. The investigators will determine whether adding this information to a vignette describing a patient will increase the likelihood that a clinician will intensify antihypertensive medication treatment.