Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation

Autor:	Lalan, Arshika, Verma, Shresth, Sudan, Kumar Madhu, Mahale, Amrita, Hegde, Aparna, Tambe, Milind, Taneja, Aparna
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Multiagent Systems
Druh dokumentu:	Working Paper
Popis:	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 beneficiaries' 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 beneficiary dropouts and enable NGOs in devising timely interventions to strengthen beneficiary retention. Comment: Accepted to Data Science for Social Good Workshop, KDD 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.07139 Zobrazit plný text záznamu View this record from Arxiv