Machine Learning to Reduce Hypertension Treatment Clinical Inertia

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.

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Key information

Conditions

Hypertension Cardiovascular Diseases Vascular Diseases

Age range

20 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

practicing primary care clinicians who see patients (i.e., internal medicine, family medicine, attending physicians, nurse practitioners) will be eligible to participate -

Exclusion criteria

Treatment and study plan

Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm

Other

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.

Primary outcomes

Vignette #1 - antihypertensive medication treatment intensification

Time frame: Immediately after clinical vignette

A clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Vignette #2 - antihypertensive medication treatment intensification

Time frame: Immediately after clinical vignette

A second clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Vignette #3 - antihypertensive medication treatment intensification

Time frame: Immediately after clinical vignette

A third clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.

Study contacts

Contact information is provided by the study sponsor or research team.

Gabriel Tajeu, DrPH

CONTACT

[email protected]

2055312258

Sponsors and collaborators

Lead sponsor

Temple University

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: Jun 6, 2022
Registry last updated: Apr 10, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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