Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms
<|reference_start|>Improving Health Information Access in the World's Largest Maternal Mobile Health Program via Bandit Algorithms: 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 mHealth program and used markovian restless multiarmed bandits to optimize a single limited intervention resource. However this paper demonstrates the challenges in adopting a markovian approach in Kilkari; therefore CHAHAK instead relies on non-markovian time-series restless bandits, and optimizes multiple interventions to improve listenership. We use real Kilkari data from the Odisha state in India to show CHAHAK's effectiveness in harnessing multiple interventions to boost listenership, benefiting marginalized communities. When deployed CHAHAK will assist the largest maternal mHealth program to date.<|reference_end|>
@article{lalan2024improving,
    title={Improving Health Information Access in the World's Largest Maternal
  Mobile Health Program via Bandit Algorithms},
    author={Arshika Lalan, Shresth Verma, Paula Rodriguez Diaz, Panayiotis
  Danassis, Amrita Mahale, Kumar Madhu Sudan, Aparna Hegde, Milind Tambe,
  Aparna Taneja},
    journal={arXiv preprint arXiv:2407.12131},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.12131},
    primaryClass={cs.CY cs.AI cs.LG cs.MA}
}