A fair incentive scheme for community health workers

• Main Authors: BOSE, Avinandan, LI, Tracey, SINHA, Arunesh, MAI, Tien
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Community health workers; mechanism design; Markov Decision Process; Artificial Intelligence and Robotics; Health Information Technology
• Online Access: https://ink.library.smu.edu.sg/sis_research/7603; https://ink.library.smu.edu.sg/context/sis_research/article/8606/viewcontent/D_Tree_AAAI__1_.pdf
• Institution: Singapore Management University

Community health workers (CHWs) play a crucial role in the last mile delivery of essential health services to under-served populations in low-income countries. Many non-governmental organizations (NGOs) provide training and support to enable CHWs to deliver health services to their communities, with no charge to the recipients of the services. This includes monetary compensation for the work that CHWs perform, which is broken down into a series of well-defined tasks. In this work, we partner with a NGO D-Tree International to design a fair monetary compensation scheme for tasks performed by CHWs in the semi-autonomous region of Zanzibar in Tanzania, Africa. In consultation with stakeholders, we interpret fairness as the equal opportunity to earn, which means that each CHW has the opportunity to earn roughly the same total payment over a given T month period, if the CHW reacts to the incentive scheme almost rationally. We model this problem as a reward design problem for a Markov Decision Process (MDP) formulation for the CHWs' earning. There is a need for the mechanism to be simple so that it is understood by the CHWs, thus, we explore linear and piecewise linear rewards in the CHWs' measured units of work. We solve this design problem via a novel policy-reward gradient result. Our experiments using two real world parameters from the ground provide evidence of reasonable incentive output by our scheme.