Improving Mobile Maternal and Child Health Care Programs: Collaborative Bandits for Time Slot Selection

Maternal and child health is a global priority, reflected in the UN Sustainable Development Goal 3.1. Mobile health (mHealth) programs, using automated voice messages, are a vital tool for NGOs to disseminate health information in underserved communities. However, these programs face challenges: limited beneficiary phone access and unknown time preferences hinder timely outreach, leading to poor engagement. We address this by formulating the time preference inference problem as a multi-agent multi-armed bandit optimization problem, where beneficiaries are modeled as agents, and time slots as arms. We introduce a novel online collaborative filtering framework that infers preferred time slots by collaborating across beneficiaries to quickly identify their preferred time slots. To highlight the scope and impact of this problem, we are working with Kilkari, the world's largest maternal and child mHealth program serving millions in India every week. Kilkari faces substantial reattempt costs to improve call answer rates. Through extensive experiments on real-world data obtained from Kilkari, we demonstrate that our collaborative bandit framework significantly outperforms both existing policies used by the NGO, and popular non-collaborative bandit algorithms (e.g., Upper Confidence Bound), both in terms of number of call retries, saving critical bandwidth that enables wider outreach, and by rapidly learning optimal time slots, improving beneficiary engagement and retention.