  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.
