Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms
Arshika Lalan, Shresth Verma, Paula Rodriguez Diaz, Panayiotis, Danassis, Amrita Mahale, Kumar Madhu Sudan, Aparna Hegde, Milind Tambe,, Aparna Taneja

TL;DR
This paper introduces CHAHAK, a non-markovian restless bandit system that strategically allocates multiple interventions to improve engagement and reduce dropouts in Kilkari, the world's largest maternal mHealth program, using real-world data.
Contribution
It presents a novel non-markovian bandit approach for optimizing multiple interventions in large-scale mHealth programs, addressing challenges faced by traditional markovian models.
Findings
CHAHAK effectively increased listenership in Kilkari.
The non-markovian approach outperformed markovian models.
Deployment can significantly enhance engagement in underserved communities.
Abstract
Harnessing the wide-spread availability of cell phones, many nonprofits have launched mobile health (mHealth) programs to deliver information via voice or text to beneficiaries in underserved communities, with maternal and infant health being a key area of such mHealth programs. Unfortunately, dwindling listenership is a major challenge, requiring targeted interventions using limited resources. This paper focuses on Kilkari, the world's largest mHealth program for maternal and child care - with over 3 million active subscribers at a time - launched by India's Ministry of Health and Family Welfare (MoHFW) and run by the non-profit ARRMAN. We present a system called CHAHAK that aims to reduce automated dropouts as well as boost engagement with the program through the strategic allocation of interventions to beneficiaries. Past work in a similar domain has focused on a much smaller scale…
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Taxonomy
TopicsMobile Health and mHealth Applications
