用机器学习优化产妇电话提醒时间，提升接听率。
Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Improved Message Delivery in Mobile Maternal Health
- 通过协作强化学习算法，自动学习每位母亲的接电话偏好时间。
- 试点中6500名参与者接听率显著提高，统计上显著优于随机拨号。
- 适合大规模移动孕产妇健康干预，推动个性化精准触达。
移动健康（mHealth）项目通过自动化语音消息向弱势群体传递健康信息，已在改善健康意识和行为改变方面证明有效。印度的Kilkari项目每周向数百万孕妇发送重要孕产期信息。然而，当前随机拨号常导致错失通话，降低信息送达率。本研究开展了一项实地试验，采用协作强化学习算法优化呼叫时机，学习个体母亲的偏好接听时间。在约6500名Kilkari参与者的试点中，与基线随机呼叫相比，该算法显著提升了接听率。结果表明，个性化调度能有效增强信息传递效果，具备在印度大规模推广以改善孕产妇健康服务的潜力。
原文摘要 · Abstract (English)
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.
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