An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures.

ABSTRACT:

Background

Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale.

Objective

This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors' time.

Methods

We used data representing 2439 weekly interactions between a digital health intervention ("Prescription Opioid Wellnes

SUBMITTER: Piette JD 

PROVIDER: S-EPMC10369305 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature