Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing (REINFORCE-EHR)

Mass General Brigham

Status: Enrolling

Conditions: Aging

Treatments: Behavioral: Reinforcement learning

Study type: Interventional

Funder types: Other; NIH

Identifiers

NCT06660979

2P30AG064199-06 (U.S. NIH Grant/Contract)

2024P002700

Details and patient eligibility

About

The overall goal of the proposed research is to refine and adapt and perform efficacy testing of a novel reinforcement learning-based approach to personalizing EHR-based tools for PCPs on deprescribing of high-risk medications for older adults. The trial will be conducted at Atrius Health, an integrated delivery network in Massachusetts, and will intervene upon primary care providers. The investigators will conduct a cluster randomized trial using reinforcement learning to adapt electronic health record (EHR) tools for deprescribing high-risk medications versus usual care. 70 PCPs will be randomized (i.e., 35 each to the reinforcement learning intervention and usual care [no EHR tool] in each arm) to the trial and follow them for approximately 30 weeks. The primary outcome will be discontinuation or ordering a dose taper for the high-risk medications for eligible patients by included primary care providers, using EHR data at Atrius. The primary hypothesis is that the personalized intervention using reinforcement learning will improve deprescribing compared with usual care.

Enrollment: 70 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

The trial will intervene upon primary care providers (including physicians and PCP-designated nurse practitioners and physician assistants) at Atrius Health.

Patients of the PCPs will be included in the intervention and analysis if they are >/=65 years of age and have been prescribed >/= 90 pills of high-risk medications in the prior 180 days based on EHR data.

Exclusion criteria

• Not a primary care provider at Atrius Health

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

---

70 participants in 2 patient groups

Reinforcement learning intervention

Experimental group

Description:

The intervention is a reinforcement learning program that personalizes EHR-based tools for PCPs to promote deprescribing high-risk medications over follow-up. The reinforcement learning intervention selects a tool for each provider based on an algorithm from an inventory of EHR tools and chooses tools that are predicted to motivate action for the individual provider. The effectiveness of each tool will be assessed on a selected interval based on whether a deprescribing action is taken by PCPs for eligible patients. The algorithm is trained to maximize these actions over time.

Treatment:

Behavioral: Reinforcement learning

Usual care

No Intervention group

Description:

No EHR-based tools provided beyond those used in regular clinical practice.