Matching Algorithms for Blood Donation

TL;DR

This paper introduces a large-scale algorithmic matching system for blood donation using Facebook data, demonstrating a modest but impactful increase in donor actions and providing theoretical guarantees for fairness and efficiency.

Contribution

It develops and tests automated matching policies for blood donors, offering the first large-scale algorithmic approach with proven theoretical guarantees and real-world pilot results.

Findings

A simple matching strategy increases donations by 5-10%.

A pilot shows a 5% increase in donor action rate.

Scaling to global users could lead to around 100,000 additional donor actions.

Abstract

Global demand for donated blood far exceeds supply, and unmet need is greatest in low- and middle-income countries; experts suggest that large-scale coordination is necessary to alleviate demand. Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. While measuring actual donation rates remains a challenge, we measure donor action (e.g., making a donation appointment) as a proxy for actual donation. We develop automated policies for matching donors with donation opportunities, 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 Donation tool user base, this corresponds to an increase of around one hundred thousand users taking action toward donation. Further, observing donor action on a social network can shed light onto donor behavior and response to incentives. Our initial findings align with several observations made in the medical and social science literature regarding donor behavior.