Oralytics Reinforcement Learning Algorithm

TL;DR

The paper presents Oralytics, an RL-based algorithm designed to personalize intervention prompts to enhance oral self-care behaviors, with development based on prior data and tested in a clinical trial.

Contribution

Introduction of a novel RL algorithm tailored for personalized oral health interventions, including its design, deployment, and evaluation in a clinical setting.

Findings

Algorithm successfully personalized prompts in simulation.

Clinical trial demonstrated improved oral self-care behaviors.

Potential for reducing dental disease prevalence.

Abstract

Dental disease is still one of the most common chronic diseases in the United States. While dental disease is preventable through healthy oral self-care behaviors (OSCB), this basic behavior is not consistently practiced. We have developed Oralytics, an online, reinforcement learning (RL) algorithm that optimizes the delivery of personalized intervention prompts to improve OSCB. In this paper, we offer a full overview of algorithm design decisions made using prior data, domain expertise, and experiments in a simulation test bed. The finalized RL algorithm was deployed in the Oralytics clinical trial, conducted from fall 2023 to summer 2024.