  India has a maternal mortality ratio of 113 and child mortality ratio of 2830
per 100,000 live births. Lack of access to preventive care information is a
major contributing factor for these deaths, especially in low resource
households. We partner with ARMMAN, a non-profit based in India employing a
call-based information program to disseminate health-related information to
pregnant women and women with recent child deliveries. We analyze call records
of over 300,000 women registered in the program created by ARMMAN and try to
identify women who might not engage with these call programs that are proven to
result in positive health outcomes. We built machine learning based models to
predict the long term engagement pattern from call logs and beneficiaries'
demographic information, and discuss the applicability of this method in the
real world through a pilot validation. Through a pilot service quality
improvement study, we show that using our model's predictions to make
interventions boosts engagement metrics by 61.37%. We then formulate the
intervention planning problem as restless multi-armed bandits (RMABs), and
present preliminary results using this approach.
