Machine Learning to Reduce Hypertension Treatment Clinical Inertia

Hypertension Clinical Trial

Official title: Machine Learning to Reduce Hypertension Treatment Clinical Inertia

Status Not yet recruiting

Clinical Trial 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.

Clinical Trial Description

Among adults with uncontrolled blood pressure (BP) at a clinic visit, clinical inertia is common. Clinical inertia is defined as a failure of providers to initiate or intensify treatment (i.e., adding medication or increasing dosage) when guidelines indicate doing so. Prior studies report that clinicians intensify antihypertensive medication treatment in less than 20% of visits where intensification would have been clinically recommended. Thus, patients who have uncontrolled BP may not receive timely therapy to control their BP. To address this issue, the investigators will use a randomized design to test the hypothesis that clinicians will be more likely to intensify the hypertensive regimen and/or assess nonadherence for patients with uncontrolled BP at the current visit when presented with information that a patient is predicted to have uncontrolled BP at the next visit by a machine learning algorithm. ;

Related Conditions & MeSH terms

• Hypertension

• NCT number: NCT05406336
• Study type: Interventional
• Source: Temple University
• Contact: Gabriel Tajeu, DrPH
• Phone: 2055312258
• Email: gabriel.tajeu@temple.edu
• Status: Not yet recruiting
• Phase: N/A
• Start date: October 1, 2024
• Completion date: July 31, 2025