Machine Learning to Reduce Hypertension Treatment Clinical Inertia

Temple University

Status: Not yet enrolling

Conditions: Hypertension

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

Study type: Interventional

Funder types: Other

Identifiers

NCT05406336

K01HL151974

Details and patient eligibility

About

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.

Full 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.

Enrollment: 50 estimated patients

Sex: All

Ages: 20+ years old

Volunteers: Accepts Healthy Volunteers

Inclusion and exclusion criteria

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:

Trial design

Primary purpose: Treatment

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

---

50 participants in 2 patient groups

No Information from Machine Learning Algorithm

No Intervention group

Description:

The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will not include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit.

Information from Machine Learning Algorithm

Experimental group

Description:

The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit about whether the algorithm predicts that the patient will have uncontrolled BP at the next visit.

Treatment:

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