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In many public health settings, it is important for patients to adhere tohealth programs, such as taking medications and periodic health checks.Unfortunately, beneficiaries may gradually disengage from such programs, whichis detrimental to their health. A concrete example of gradual disengagement hasbeen observed by an organization that carries out a free automated call-basedprogram for spreading preventive care information among pregnant women. Manywomen stop picking up calls after being enrolled for a few months. To avoidsuch disengagements, it is important to provide timely interventions. Suchinterventions are often expensive and can be provided to only a small fractionof the beneficiaries. We model this scenario as a restless multi-armed bandit(RMAB) problem, where each beneficiary is assumed to transition from one stateto another depending on the intervention. Moreover, since the transitionprobabilities are unknown a priori, we propose a Whittle index based Q-Learningmechanism and show that it converges to the optimal solution. Our methodimproves over existing learning-based methods for RMABs on multiple benchmarksfrom literature and also on the maternal healthcare dataset.

Learn to Intervene: An Adaptive Learning Policy for Restless Bandits in Application to Preventive Healthcare