Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911283766362112 |
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| author | Dasgupta, Arpan Maniyar, Mizhaan Srivastava, Awadhesh Kumar, Sanat Mahale, Amrita Hegde, Aparna Suggala, Arun Shanmugam, Karthikeyan Taneja, Aparna Tambe, Milind |
| author_facet | Dasgupta, Arpan Maniyar, Mizhaan Srivastava, Awadhesh Kumar, Sanat Mahale, Amrita Hegde, Aparna Suggala, Arun Shanmugam, Karthikeyan Taneja, Aparna Tambe, Milind |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_16356 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health Dasgupta, Arpan Maniyar, Mizhaan Srivastava, Awadhesh Kumar, Sanat Mahale, Amrita Hegde, Aparna Suggala, Arun Shanmugam, Karthikeyan Taneja, Aparna Tambe, Milind Artificial Intelligence 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. |
| title | Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2507.16356 |