Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure

Stopped early · Not applicable

Conditions studied: Heart Failure

In brief

Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an individual patient-doctor relationship to population health management strategies. As an example, at our institution, medication therapy management (MTM) pharmacists are being deployed to patients with heart failure with the intent of improving patient outcomes (through proper medication management and adherence) while reducing cost (e.g., keeping these patients out of the hospital). The success of such strategies will be dependent on the ability to effectively direct scarce resources to deliver appropriate/needed care to patients. In this prospective, pragmatic randomized and matched controlled study, the investigators hypothesize that the combination of accurate, data-driven benefit models and MTM pharmacist intervention in patients with heart failure will result in reduced 1-year mortality and hospital admissions. Using our extensive historical electronic health record data, the investigators have developed a machine learning model that, for individual patients with heart failure, predicts risk and benefit (that is, reduction in risk) associated with closing specific "care gaps". These care gaps represent standard evidence-based treatments that may be missing for an individual patient, such as beta blockers or flu shots. The investigators will use this model to define three cohorts to be studied: 1) a high risk/high benefit group to be referred for MTM pharmacist intervention, 2) a high risk/high benefit group to continue with existing standard of care (not necessarily involving MTM pharmacy), and 3) a high risk/low benefit group to be referred for MTM pharmacist intervention. Comparison of groups 1 and 2 (for which assignment is randomized) will evaluate the effectiveness of the MTM pharmacy intervention, while comparison of groups 1 and 3 will evaluate the accuracy of the benefit model prediction and importance of appropriate patient selection for treatment. The primary study outcomes will be mortality and number of hospital admissions during 1-year follow-up following study enrollment.

Key facts

Study ID

NCT03804606

Run by

Geisinger Clinic

People needed

100

Starts

2019-02-28

Expected to finish

2023-09-01

Last updated by the study team

2025-03-28

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• All adult Geisinger patients with heart failure, as identified by a validated EHR (Electonic Health Record)-based phenotype algorithm,
• Patients with a Geisinger primary care provider (PCP)
• Patients who follow with Geisinger Cardiology (at least 1 visit in past two years).
• Fulfills the specifications for arm assignment based on the results of the care gap benefit model.

You may not qualify if…

• Patients with a Geisinger PCP or Cardiologist in the South Central Region (part of the Geisinger Holy Spirit footprint) as MTM availability is limited in this service area.
• Patients who have indicated they do not wish to participate in research studies

Where it is running

• Geisinger Health System — Danville, Pennsylvania, United States

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.