Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2022

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.

Keywords

AI for social impact, health communication

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Health Information Technology

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 36th AAAI Conference on Artificial Intelligence 2022, Virtual, February 22 - March 1

First Page

12017

Last Page

12025

ISBN

9781577358763

Identifier

10.1609/aaai.v36i11.21460

Publisher

AAAI

City or Country

Washington, DC

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1609/aaai.v36i11.21460

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