Beyond Listenership: AI-Predicted Interventions Drive Improvements in Maternal Health Behaviours

TL;DR

This study demonstrates that AI-driven interventions in automated voice call programs significantly improve maternal health knowledge and behaviors, addressing engagement issues and translating listenership gains into tangible health outcomes.

Contribution

It provides the first evidence linking AI-targeted voice call interventions to actual improvements in maternal health behaviors and knowledge.

Findings

AI interventions increase beneficiary listenership

Enhanced listenership correlates with better health behaviors

Statistically significant improvements in health knowledge and actions

Abstract

Automated voice calls with health information are a proven method for disseminating maternal and child health information among beneficiaries and are deployed in several programs around the world. However, these programs often suffer from beneficiary dropoffs and poor engagement. In previous work, through real-world trials, we showed that an AI model, specifically a restless bandit model, could identify beneficiaries who would benefit most from live service call interventions, preventing dropoffs and boosting engagement. However, one key question has remained open so far: does such improved listenership via AI-targeted interventions translate into beneficiaries' improved knowledge and health behaviors? We present a first study that shows not only listenership improvements due to AI interventions, but also simultaneously links these improvements to health behavior changes. Specifically, we demonstrate that AI-scheduled interventions, which enhance listenership, lead to statistically significant improvements in beneficiaries' health behaviors such as taking iron or calcium supplements in the postnatal period, as well as understanding of critical health topics during pregnancy and infancy. This underscores the potential of AI to drive meaningful improvements in maternal and child health.