Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing (REINFORCE-EHR)
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Julie Lauffenburger, PharmD, PhD
- Phone Number: 617-525-8865
- Email: jlauffenburger@bwh.harvard.edu
Study Locations
-
-
Massachusetts
-
Boston, Massachusetts, United States, 02215
- Atrius Health
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
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
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Health Services Research
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Double
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: Reinforcement learning intervention
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.
|
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.
|
|
No Intervention: Usual care
No EHR-based tools provided beyond those used in regular clinical practice.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Discontinuation or taper for high-risk medication
Time Frame: Through trial completion, up to 7 months
|
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 Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Discontinuation of high-risk medication
Time Frame: Through trial completion, up to 7 months
|
Discontinuation of medication assessed using routinely-collected data from the EHR system for eligible patients flagged as in need of deprescribing.
|
Through trial completion, up to 7 months
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2024P002700
- 2P30AG064199-06 (U.S. NIH Grant/Contract)
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
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