Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health

TL;DR

This paper presents a field trial of a collaborative bandit algorithm that personalizes call timing in a mobile health program, significantly improving message delivery rates among millions of mothers in India.

Contribution

It introduces a novel machine learning algorithm for optimizing call scheduling in mHealth programs, demonstrating its effectiveness in a large-scale real-world deployment.

Findings

Significant increase in call pick-up rates with the bandit algorithm

Personalized scheduling improves message delivery in mHealth

Potential to enhance health outcomes through better message reach

Abstract

Mobile health (mHealth) programs utilize automated voice messages to deliver health information, particularly targeting underserved communities, demonstrating the effectiveness of using mobile technology to disseminate crucial health information to these populations, improving health outcomes through increased awareness and behavioral change. India's Kilkari program delivers vital maternal health information via weekly voice calls to millions of mothers. However, the current random call scheduling often results in missed calls and reduced message delivery. This study presents a field trial of a collaborative bandit algorithm designed to optimize call timing by learning individual mothers' preferred call times. We deployed the algorithm with around $6500$ Kilkari participants as a pilot study, comparing its performance to the baseline random calling approach. Our results demonstrate a statistically significant improvement in call pick-up rates with the bandit algorithm, indicating its potential to enhance message delivery and impact millions of mothers across India. This research highlights the efficacy of personalized scheduling in mobile health interventions and underscores the potential of machine learning to improve maternal health outreach at scale.