Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics.

Introduction: Non-attendance (NA) causes additional burden on the outpatient services due to clinician time and other resources being wasted, and it lengthens wait lists for patients. Telehealth, the delivery of health services remotely using digital technologies, is one promising approach to accommodate patient needs while offering more flexibility in outpatient services. However, there is limited evidence about whether offering telehealth consults as an option can change NA rates, or about the preferences of hospital outpatients for telehealth compared to in-person consults. We model patient preferences with a Maximum Entropy Inverse Reinforcement Learning (IRL) behaviour model, allowing for the calculation of general population- and demographic-specific relative preferences for consult modality. The aim of this research is to use real-world data to model patient preferences for consult modality using Maximum Entropy IRL behaviour model. Methods: Retrospective data were collected from an immunology outpatient clinic associated with a large metropolitan hospital in Brisbane, Australia. We used IRL with the Maximum Entropy behaviour model to learn outpatient preferences for appointment modality (telehealth or in-person) and to derive demographic predictors of attendance or NA. IRL models patients as decision making agents interacting sequentially over multiple time-steps, allowing for present actions to impact future outcomes, unlike previous models applied in this domain. Results: We found statistically significant ( Discussion: Using the Maximum Entropy IRL sequential behaviour model, our results agree with previous evidence that non-attendance can be reduced when telehealth is offered in outpatient clinics. Our results complement previous studies using non-sequential modelling methodologies. Our preference and NA prediction results may be useful to outpatient clinic administrators to tailor services to specific patient groups, such as scheduling text message consult reminders if a given patient is predicted to be more likely to NA.