Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing

NCT ID: NCT06660979

Last Updated: 2025-11-19

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 70 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-08-11
• Study Completion Date: 2026-05-31

Brief Summary

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.

Conditions

Aging

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: HEALTH_SERVICES_RESEARCH
• Blinding Strategy: DOUBLE

Study Groups

Reinforcement learning intervention

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.

Group Type: EXPERIMENTAL

Reinforcement learning

Intervention Type: BEHAVIORAL

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 inventory of EHR tools from which the algorithm will choose include the following potential factors: open encounter, order entry, cold-state outreach, simplification, and risk framing.

Usual care

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

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Reinforcement learning

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 inventory of EHR tools from which the algorithm will choose include the following potential factors: open encounter, order entry, cold-state outreach, simplification, and risk framing.

Intervention Type: BEHAVIORAL

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute on Aging (NIA)

NIH

Sponsor Role: collaborator

Atrius Health

OTHER

Sponsor Role: collaborator

Brigham and Women's Hospital

OTHER

Sponsor Role: lead

Responsible Party

Julie Lauffenburger

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Atrius Health

Boston, Massachusetts, United States

Site Status: RECRUITING

Countries

United States

Central Contacts

Julie Lauffenburger, PharmD, PhD

Role: CONTACT

jlauffenburger@bwh.harvard.edu

617-525-8865

Facility Contacts

John Zambrano, MD, MHS

Role: primary

Other Identifiers

2P30AG064199-06

Identifier Type: NIH

Identifier Source: secondary_id

View Link

2024P002700

Identifier Type: -

Identifier Source: org_study_id