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

arXiv: https://arxiv.org/abs/2507.16356 · HTML: https://mlsanity.com/paper/2507.16356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 22 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Optimal Best Arm Identification with Fixed Confidence](https://mlsanity.com/paper/1602.04589/) (2016)
- [Boltzmann Exploration Done Right](https://mlsanity.com/paper/1705.10257/) (2017)
- [Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health](https://mlsanity.com/paper/2109.08075/) (2021)
- [Optimal Algorithms for Latent Bandits with Cluster Structure](https://mlsanity.com/paper/2301.07040/) (2023)
- [Regret Guarantees for Item-Item Collaborative Filtering](https://mlsanity.com/paper/1507.05371/) (2015)
- [Online Low Rank Matrix Completion](https://mlsanity.com/paper/2209.03997/) (2022)
- [Alternating Linear Bandits for Online Matrix-Factorization Recommendation](https://mlsanity.com/paper/1810.09401/) (2018)
- [ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria](https://mlsanity.com/paper/2204.13663/) (2022)
- [Analyzing and Predicting Low-Listenership Trends in a Large-Scale Mobile Health Program: A Preliminary Investigation](https://mlsanity.com/paper/2311.07139/) (2023)
- [Preliminary Study of the Impact of AI-Based Interventions on Health and Behavioral Outcomes in Maternal Health Programs](https://mlsanity.com/paper/2407.11973/) (2024)

## Built on by (most cited first, 2 of 2)

- [Deployment of AI-Assisted Interventions: Capacity Constraints and Noisy Compliance](https://mlsanity.com/paper/2604.14370/) (2026)
- [Bandit Simulation for Average Reward Inference](https://mlsanity.com/paper/2606.00913/) (2026)

## Abstract (arXiv)

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.