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

Study 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.

Primary Outcome

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.

Conditions Studied

Interventions

• BEHAVIORAL Reinforcement learning

Study Locations (1)