CompletedNANCT06660979

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

Sponsor: Brigham and Women's Hospital

Trial Details

Condition	Aging
Intervention	Reinforcement learning
Phase	NA
Status	Completed
Participants	1,249
Est. Completion	27 Mar 2026
Last Updated	2 Apr 2026

Primary Outcome

Discontinuation or taper for high-risk medication

Eligibility Criteria

Inclusion 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

Trial Sites

Countries

United States

View Registry Entry

Related Analysis

Completed Trial: Reinforcement Learning Personalises EHR Tools for Deprescribing in Older Adults — Novel Approach to Medication Safety

A completed cluster randomised trial tested reinforcement learning algorithms to personalise EHR deprescribing tools for high-risk medications in older adults. The study involved 70 primary care providers and 1,249 patients, representing a novel approach to medication safety in geriatric care.

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