Matching algorithms for blood donation

• Published in: Nature machine intelligence Vol. 5; no. 10; pp. 1108 - 1118
• Main Authors: McElfresh, Duncan C., Kroer, Christian, Pupyrev, Sergey, Sodomka, Eric, Sankararaman, Karthik, Chauvin, Zack, Dexter, Neil, Dickerson, John P.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01-10-2023; Nature Publishing Group
• Subjects: 4000/4011; 639/705/117; Algorithms; Blood; Blood & organ donations; Datasets; Engineering; Matching; Policies; Privacy; Social networks; Supply chains; Brazil; United States > US; India
• ISSN: 2522-5839; 2522-5839
• DOI: 10.1038/s42256-023-00722-5

Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries. Large-scale coordination is necessary to alleviate demand. Using the Facebook Blood Donations tool, we conduct a large-scale algorithmic matching of blood donors with donation opportunities. While measuring actual donation rates remains a challenge, we measure donor action (for example, making a donation appointment) as a proxy for actual donation. We develop automated policies for matching patients and donors, based on an online matching model. We provide theoretical guarantees for these policies, both regarding the number of expected donations and the equitable treatment of blood recipients. In simulations, a simple matching strategy increases the number of donations by 5–10%; a pilot experiment with real donors shows a 5% relative increase in donor action rate (from 3.7% to 3.9%). When scaled to the global Blood Donations tool user base, this corresponds to an increase of around 100,000 users taking action toward donation. Further, observing donor action on a social network can shed light on donor behaviour and response to incentives. Our initial findings align with several observations made in the medical and social science literature regarding donor behaviour.
Online matching platforms are increasingly used for applications with positive social impact such as matching blood donors with recipients, where matching algorithms need to balance fairness with an efficiency objective. The authors demonstrate, both in computational simulations and using real data from the Facebook Blood Donations tool, that introducing a simple online matching policy can substantially increase the likelihood of donor action.