Justin J. Boutilier

Jackie Baek, Justin J. Boutilier, Vivek F. Farias et al.

Behavioral health interventions, delivered through digital platforms, have the potential to significantly improve health outcomes, through education, motivation, reminders, and outreach. We study the problem of optimizing personalized interventions for patients to maximize a long-term outcome, where interventions are costly and capacity-constrained. We assume we have access to a historical dataset collected from an initial pilot study. We present a new approach for this problem that we dub DecompPI, which decomposes the state space for a system of patients to the individual level and then approximates one step of policy iteration. Implementing DecompPI simply consists of a prediction task using the dataset, alleviating the need for online experimentation. DecompPI is a generic model-free algorithm that can be used irrespective of the underlying patient behavior model. We derive theoretical guarantees on a simple, special case of the model that is representative of our problem setting. When the initial policy used to collect the data is randomized, we establish an approximation guarantee for DecompPI with respect to the improvement beyond a null policy that does not allocate interventions. We show that this guarantee is robust to estimation errors. We then conduct a rigorous empirical case study using real-world data from a mobile health platform for improving treatment adherence for tuberculosis. Using a validated simulation model, we demonstrate that DecompPI can provide the same efficacy as the status quo approach with approximately half the capacity of interventions. DecompPI is simple and easy to implement for an organization aiming to improve long-term behavior through targeted interventions, and this paper demonstrates its strong performance both theoretically and empirically, particularly in resource-limited settings.

Katherine B. Adams, Justin J. Boutilier, Qinyang He et al.

We study a sequential resource allocation problem where a decision maker selects subsets of agents at each period to maximize overall outcomes without prior knowledge of individual-level effects. Our framework applies to settings such as community health interventions, targeted digital advertising, and workforce retention programs, where intervention effects evolve dynamically. Agents may exhibit habituation (diminished response from frequent selection) or recovery (enhanced response from infrequent selection). The technical challenge centers on nonstationary reward distributions that lead to changing intervention effects over time. The problem requires balancing two key competing objectives: heterogeneous individual rewards and the exploration-exploitation tradeoff in terms of learning for improved future decisions as opposed to maximizing immediate outcomes. Our contribution introduces the first framework incorporating this form of nonstationary rewards in the combinatorial multi-armed bandit literature. We develop algorithms with theoretical guarantees on dynamic regret and demonstrate practical efficacy through a diabetes intervention case study. Our personalized community intervention algorithm achieved up to three times as much improvement in program enrollment compared to baseline approaches, validating the framework's potential for real-world applications. This work bridges theoretical advances in adaptive learning with practical challenges in population-level behavioral change interventions.

Katherine B. Adams, Justin J. Boutilier, Sarang Deo et al.

Diabetes is a global health priority, especially in low- and-middle-income countries, where over 50% of premature deaths are attributed to high blood glucose. Several studies have demonstrated the feasibility of using Community Health Worker (CHW) programs to provide affordable and culturally tailored solutions for early detection and management of diabetes. Yet, scalable models to design and implement CHW programs while accounting for screening, management, and patient enrollment decisions have not been proposed. We introduce an optimization framework to determine personalized CHW visits that maximize glycemic control at a community-level. Our framework explicitly models the trade-off between screening new patients and providing management visits to individuals who are already enrolled in treatment. We account for patients' motivational states, which affect their decisions to enroll or drop out of treatment and, therefore, the effectiveness of the intervention. We incorporate these decisions by modeling patients as utility-maximizing agents within a bi-level provider problem that we solve using approximate dynamic programming. By estimating patients' health and motivational states, our model builds visit plans that account for patients' tradeoffs when deciding to enroll in treatment, leading to reduced dropout rates and improved resource allocation. We apply our approach to generate CHW visit plans using operational data from a social enterprise serving low-income neighborhoods in urban areas of India. Through extensive simulation experiments, we find that our framework requires up to 73.4% less capacity than the best naive policy to achieve the same performance in terms of glycemic control. Our experiments also show that our solution algorithm can improve upon naive policies by up to 124.5% using the same CHW capacity.