Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing
Completed · Not applicable
Conditions studied: Aging
In brief
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.
Key facts
- Study ID
- NCT06660979
- Run by
- Brigham and Women's Hospital
- People needed
- 1249
- Starts
- 2025-08-11
- Expected to finish
- 2026-03-27
- Last updated by the study team
- 2026-04-02
Who can join
Age: 18 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- 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.
You may not qualify if…
- Not a primary care provider at Atrius Health
Where it is running
- Atrius Health — Boston, Massachusetts, United States
Full record on ClinicalTrials.gov
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