NCT06660979

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

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 1,249 (actual)
• Sponsor: Brigham and Women's Hospital · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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

Type	Name	Description
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.

Timeline

• Start date: 2025-08-11
• Primary completion: 2026-03-27
• Completion: 2026-03-27
• First posted: 2024-10-28
• Last updated: 2026-04-02

Locations

1 site across 1 country: United States