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

arXiv (Cornell University) Pub Date : 2023-11-13 DOI:10.48550/arxiv.2311.07139

Lalan, Arshika, Verma, Shresth, Sudan, Kumar Madhu, Mahale, Amrita, Hegde, Aparna, Tambe, Milind, Taneja, Aparna

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引用次数: 0

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 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.

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分析和预测大规模移动医疗计划的低听众趋势:初步调查

移动医疗方案正日益成为向贫困社区受益人传播卫生信息的一种流行媒介。Kilkari是世界上最大的移动医疗项目之一，它向孕妇和新妈妈提供时间敏感的音频信息。我们一直在与印度一家运营Kilkari项目的非营利组织ARMMAN合作，以确定瓶颈，提高项目效率。特别是，我们提供了受益者与移动医疗项目互动轨迹的初步分析，并检查了该项目中可以潜在增强以促进其成功的要素。我们根据听众人数将这群人分成不同的群组，以便分析每个群组的听众模式，从而有助于提高节目的成功率。我们还展示了在时间序列预测中使用历史数据来识别受益人辍学并使非政府组织能够制定及时的干预措施以加强受益人保留的初步结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv (Cornell University)

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