Gargi Singh
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Deployed SAHELI: Field Optimization of Intelligent RMAB for Maternal and Child Care
Shresth Verma
Aditya S. Mate
Paritosh Verma
Sruthi Gorantala
Neha Madhiwalla
Aparna Hegde
Manish Jain
Innovative Applications of Artificial Intelligence (IAAI) (2023) (to appear)
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Underserved communities face critical health challenges due to lack of access to timely and reliable information. Non-governmental organizations are leveraging the widespread use of cellphones to combat these healthcare challenges and spread preventative awareness. The health workers at these organizations reach out individually to beneficiaries; however such programs still suffer from declining engagement. We have deployed SAHELI, a system to efficiently utilize the limited availability of health workers for improving maternal and child health in India. SAHELI uses the Restless Multi-armed Bandit (RMAB) framework to identify beneficiaries for outreach. It is the first deployed application for RMABs
in public health, and is already in continuous use by our partner NGO, ARMMAN. We have already reached ∼ 100K beneficiaries with SAHELI, and are on track to serve 1 million beneficiaries by the end of 2023. This scale and impact has been achieved through multiple innovations in the RMAB model and its development, in preparation of real world data, and in deployment practices; and through careful consideration of responsible AI practices. Specifically, in this paper, we describe our approach to learn from past data to improve the performance of SAHELI’s RMAB model, the real-world challenges faced during deployment and adoption of SAHELI, and the end-to-end pipeline
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Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health
Aditya Mate
Lovish Madaan
Neha Madhiwalla
Shresth Verma
Aparna Hegde
Pradeep Varakantham
AAAI Conference on Artificial Intelligence (2022) (to appear)
Preview abstract
The widespread availability of cell phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. This paper describes our work to assist non-profits that employ automated messaging programs to deliver timely preventive care information to beneficiaries (new and expecting mothers) during pregnancy and after delivery. Unfortunately, a key challenge in such information delivery programs is that a significant fraction of beneficiaries drop out of the program. Yet, non-profits often have limited health-worker resources (time) to place crucial service calls for live interaction with beneficiaries to prevent such engagement drops. To assist non-profits in optimizing this limited resource, we developed a Restless Multi-Armed Bandits (RMABs) system. One key technical contribution in this system is a novel clustering method of offline historical data to infer unknown RMAB parameters. Our second major contribution is evaluation of our RMAB system in collaboration with an NGO, via a real-world service quality improvement study. The study compared strategies for optimizing service calls to 23003 participants over a period of 7 weeks to reduce engagement drops. We show that the RMAB group provides statistically significant improvement over other comparison groups, reducing 30% engagement drops. To the best of our knowledge, this is the first study demonstrating the utility of RMABs in real world public health settings. We are transitioning our RMAB system to the NGO for real-world use.
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