Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics
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.
Authors
- Centaine L. Snoswell (ORCID: https://orcid.org/0000-0002-4298-9369)
- Aaron Snoswell (ORCID: https://orcid.org/0000-0003-0960-0144)
- Nan Ye (ORCID: https://orcid.org/0000-0002-4689-3559)
Institutions
- Australian Research Council (AU)
- Queensland University of Technology (AU)
- The University of Queensland (AU)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2024-10-31
- DOI
- https://doi.org/10.3389/fdgth.2024.1384248
- Primary Topic
- Telemedicine and Telehealth Implementation
- Type
- article
- Field-Weighted Citation Impact
- 0.00