arXiv:2605.24261v1 Announce Type: new Abstract: A critical challenge facing clinicians managing chronic disease interventions is sustaining long-run patient health given limited information and resources. Digital therapeutics (DTs) provide a cost-effective way to manage interventions at scale through repeated interactions (e.g. daily treatment recommendations), but patient success is highly dependent on their adherence. Behavioral psychology suggests that both treatment recommendations and past adherence affect future adherence, yet existing decision support frameworks for DTs model only recom
The proliferation of digital health interventions and the increasing focus on personalized medicine make optimizing patient engagement and adherence a pressing challenge right now.
This research provides a framework for more effective digital therapeutic interventions by integrating behavioral psychology into online learning, potentially improving health outcomes and reducing healthcare costs.
Existing decision support frameworks for digital therapeutics will evolve to account for the dynamic interplay between treatment recommendations and patient adherence, moving beyond static models.
- · Digital therapeutics companies
- · Patients with chronic diseases
- · Healthcare providers
- · Behavioral psychologists
- · Ineffective digital health platforms
- · Static treatment recommendation systems
Improved patient adherence to digital health interventions will lead to better health outcomes for chronic disease management.
The success of these optimized DTs could accelerate the adoption of digital health solutions across a wider range of medical conditions.
Personalized, AI-driven digital therapeutics may become the standard of care, significantly reducing the burden on human clinical resources and traditional healthcare infrastructure.
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Read at arXiv cs.LG