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

NCT06660979 | Completed DMC

• 2 other identifiers: 2024P0027002P30AG064199-06
• Study Type: interventional
• Target: 1,249
• Locations: 1 country
• Sites: 1

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

Keywords

Medication optimization; High-risk medications

Outcome Measures

Primary Outcomes (1)

• Discontinuation or taper for high-risk medication

  Deprescribing will be assessed using routinely collected data from the EHR system for eligible patients flagged as in need of deprescribing. The deprescribing outcome will be a "reduction" in inappropriate prescribing, defined as either discontinuation of one the the medication classes of interest or ordering a dose taper.

  Through trial completion, up to 7 months

Secondary Outcomes (1)

• Discontinuation of high-risk medication

  Through trial completion, up to 7 months

Study Arms (2)

Reinforcement learning intervention

EXPERIMENTAL

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.

Behavioral: Reinforcement learning

Usual care

NO INTERVENTION

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

Interventions

Reinforcement learning 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.

Reinforcement learning intervention

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

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

Sponsors & Collaborators

• National Institute on Aging (NIA): collaborator
• Atrius Health: collaborator
• Brigham and Women's Hospital: lead

Study Sites (1)

Atrius Health

Boston, Massachusetts, 02215, United States

Location

MeSH Terms

Interventions

Reinforcement Machine Learning

Intervention Hierarchy (Ancestors)

Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor

Study Record Dates

• First Submitted: October 24, 2024
• First Posted: October 28, 2024
• Study Start: August 11, 2025
• Primary Completion: March 27, 2026
• Study Completion: March 27, 2026
• Last Updated: April 2, 2026

Record last verified: 2025-11

Locations

US (1)