A Deployed Online Reinforcement Learning Algorithm In An Oral Health Clinical Trial
This addresses oral health issues for marginalized individuals at risk for dental disease, but it is incremental as it applies existing RL methods to a new domain.
The paper tackled the problem of low adherence to oral self-care behaviors by developing Oralytics, a mHealth system using online reinforcement learning to deliver prompts, and deployed it in a clinical trial with a planned randomized control trial in 2025.
Dental disease is a prevalent chronic condition associated with substantial financial burden, personal suffering, and increased risk of systemic diseases. Despite widespread recommendations for twice-daily tooth brushing, adherence to recommended oral self-care behaviors remains sub-optimal due to factors such as forgetfulness and disengagement. To address this, we developed Oralytics, a mHealth intervention system designed to complement clinician-delivered preventative care for marginalized individuals at risk for dental disease. Oralytics incorporates an online reinforcement learning algorithm to determine optimal times to deliver intervention prompts that encourage oral self-care behaviors. We have deployed Oralytics in a registered clinical trial. The deployment required careful design to manage challenges specific to the clinical trials setting in the U.S. In this paper, we (1) highlight key design decisions of the RL algorithm that address these challenges and (2) conduct a re-sampling analysis to evaluate algorithm design decisions. A second phase (randomized control trial) of Oralytics is planned to start in spring 2025.