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Matching algorithms for blood donation

Nature Machine Intelligence volume 5, pages 1108–1118 (2023)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

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.

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Fig. 1: Stages of the blood supply chain.
Fig. 2: Overview of the Facebook Blood Donations tool.
Fig. 3: Distribution of predicted MA likelihood.
Fig. 4: Simulation results for 12 cities around the world.
Fig. 5: Aggregate MA rate for both Rand and Max, for each day in the experiment.

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Data availability

The datasets related to Facebook user information are not publicly available due to privacy concerns. All data used to create plots are available from the corresponding author on reasonable request. Datasets used for simulations (Supplementary Information Section 2) are publicly available from the Socioeconomic Data and Applications Center (SEDAC)41,42, and can be found online (http://sedac.ciesin.columbia.edu/data/collection/gpw-v4).

Code availability

All code used for our simulations is available online40, and is included in Supplementary Software.

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Acknowledgements

All authors were employed by Facebook while contributing to this project.

Author information

Authors and Affiliations

  1. Department of Medicine, Stanford University, Stanford, CA, USA

    Duncan C. McElfresh

  2. Ci2i, Department of Veterans Affairs VA Palo Alto Health Care System, Palo Alto, CA, USA

    Duncan C. McElfresh

  3. Columbia University, New York, NY, USA

    Christian Kroer

  4. Meta, Menlo Park, CA, USA

    Sergey Pupyrev, Karthik Sankararaman & Neil Dexter

  5. Virtual Chair, Chicago, IL, USA

    Eric Sodomka

  6. Middesk, New York, NY, USA

    Zack Chauvin

  7. University of Maryland, College Park, MD, USA

    John P. Dickerson

  8. Arthur, New York, NY, USA

    John P. Dickerson

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Contributions

D.C.M. led manuscript preparation, coding, data analysis and theoretical analysis. S.P. provided mentorship and contributed to coding and data analysis. K.S. contributed to ML experiments. N.D. and Z.C. contributed to data analysis and software engineering. C.K. and J.P.D. provided mentorship and contributed to theoretical analysis. All authors contributed to shaping discussions and manuscript preparation. E.S., J.P.D., C.K. and D.C.M. conceived of the idea.

Corresponding author

Correspondence to Duncan C. McElfresh.

Ethics declarations

Competing interests

All authors were employed by Facebook (now Meta) during this project. No authors will benefit financially from the Facebook Blood Donations tool, or from blood donations facilitated by this platform.

Peer review

Peer review information

Nature Machine Intelligence thanks Arpita Biswas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Liesbeth Venema, in collaboration with the Nature Machine Intelligence team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Discussions A–C and Figs. 1 and 2.

Supplementary Software

Code and instructions for running simulations on synthetic data.

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McElfresh, D.C., Kroer, C., Pupyrev, S. et al. Matching algorithms for blood donation. Nat Mach Intell 5, 1108–1118 (2023). https://doi.org/10.1038/s42256-023-00722-5

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