Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation
Abstract
Mobile health programs are becoming an increasingly popular medium
for dissemination of health information among beneficiaries in less
privileged communities. Kilkari is one of the world’s largest mobile
health programs which delivers time sensitive audio-messages to
pregnant women and new mothers. We have been collaborating with
ARMMAN, a non-profit in India which operates the Kilkari program,
to identify bottlenecks to improve the efficiency of the program. In
particular, we provide an initial analysis of the trajectories of benefi-
ciaries’ interaction with the mHealth program and examine elements
of the program that can be potentially enhanced to boost its success.
We cluster the cohort into different buckets based on listenership
so as to analyze listenership patterns for each group that could help
boost program success . We also demonstrate preliminary results on
using historical data in a time-series prediction to identify benefi-
ciary dropouts and enable NGOs in devising timely interventions to
strengthen beneficiary retention.
Research Areas
