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

NCT06660979 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1249

Last updated 2026-04-02

No results posted yet for this study

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

Interventions

BEHAVIORAL

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.

Sponsors & Collaborators

  • National Institute on Aging (NIA)

    collaborator NIH

  • Atrius Health

    collaborator OTHER

  • Brigham and Women's Hospital

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-08-11
Primary Completion
2026-03-27
Completion
2026-03-27

Countries

  • United States

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06660979 on ClinicalTrials.gov