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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program:

Jackson A Killian1,2,3, Manish Jain3, Yugang Jia2

  • 1Harvard University, Cambridge, MA, United States.

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This study introduces a new model for digital health programs, optimizing patient engagement and clinical outcomes while ensuring equitable resource allocation for chronic disease management. The approach improves patient results and reduces disparities.

Keywords:
T2Dchronic diseasedigital healthequitymachine learningmulti-armed banditmulti-armed banditsresource allocationrestless multiarmed banditstype-2 diabetes

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Area of Science:

  • Digital Health Interventions
  • Chronic Disease Management
  • Health Informatics

Background:

  • Digital health programs offer personalized support for chronic diseases, aiming for target clinical outcomes and sustained patient engagement.
  • Challenges include patient dropout and inequitable intervention delivery, potentially disadvantaging certain patient subgroups.
  • Optimizing the balance between clinical outcomes and engagement, with equity and resource constraints in mind, is crucial.

Purpose of the Study:

  • To propose a resource management model for digital health programs.
  • To jointly account for individual clinical outcomes and patient engagement.
  • To ensure equitable allocation, enable capacity planning, and simulate using type 2 diabetes data.

Main Methods:

  • A restless multiarmed bandit (RMAB) model was developed to optimize long-term engagement and clinical outcomes (healthy glucose levels).
  • New equitable objectives for RMAB were proposed to mitigate performance disparities between groups.
  • Bilevel optimization algorithms were applied, and a model for the joint evolution of engagement and outcomes was formulated.

Main Results:

  • Optimized policies increased the number of patients reaching healthy glucose levels by up to 10% and reduced dropout by 10% over 12 months.
  • Equitable policies reduced mean absolute differences in engagement and health outcomes across demographic groups by up to 85%.
  • Simulations demonstrated the feasibility and effectiveness of the proposed approach.

Conclusions:

  • Digital health interventions can be effectively planned by considering clinical outcomes and engagement dynamics.
  • The proposed RMAB framework supports sequential decision-making and capacity planning.
  • Integrating equitable RMAB algorithms enhances the potential for equitable solutions, offering flexibility in balancing objectives.