Matching Algorithms for Blood Donation
Matching Algorithms for Blood Donation
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献血匹配算法
DOI：
10.1038/s42256-023-00722-5
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
John P. Dickerson
中科院分区：
文献类型：
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作者：
Duncan C. McElfresh;Christian Kroer;S. Pupyrev;Eric Sodomka;Karthik Abinav Sankararaman;Zack Chauvin;Neil Dexter;John P. Dickerson
管理易腐库存，例如等待有需要的患者使用的血液库存，几十年来一直是研究的主题。这已经在多个学科中进行了研究：医学和社会科学家研究了谁献血，频率如何以及为什么;管理科学研究人员长期以来一直从物流角度研究血液供应链。然而，全球对血液的需求仍然远远超过供应，低收入和中等收入国家的需求最大。学者和政策专家都认为，大规模的协调是必要的，以减轻对献血的需求。利用最近部署的Facebook献血工具，我们进行了第一次大规模的献血者与献血机会的算法匹配。在模拟和真实的实验中，我们将潜在的捐赠者与机会相匹配，并在捐赠者行为的先验观察基础上训练机器学习模型。虽然衡量实际捐赠率仍然是一个挑战，但我们衡量捐赠者的行动（即，打电话给血库或预约）作为实际献血的代理。模拟表明，即使是一个简单的匹配策略也可以将供体的行动率提高10-15%;对真实的供体进行的试点实验发现，大约5%的增幅略小。虽然总体行动率仍然很低，但即使是全球网络中捐助者的这种适度增加，也相当于成千上万的潜在捐助者采取行动进行捐助。此外，观察捐赠者在社交网络上的行为可以揭示捐赠者的行为和对激励措施的反应。我们的初步研究结果与医学和社会科学文献中关于供体行为的几项观察结果一致。
Managing perishable inventory, such as blood stock awaiting use by patients in need, has been a topic of research for decades. This has been investigated across several disciplines: medical and social scientists have investigated who donates blood, how frequently, and why; management science researchers have long studied the blood supply chain from a logistical perspective. Yet global demand for blood still far exceeds supply, and unmet need is greatest in low- and middle-income countries. Both academics and policy experts suggest that large-scale coordination is necessary to alleviate demand for donor blood. Using the recently-deployed Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities. In both simulations and real experiments we match potential donors with opportunities, guided by a machine learning model trained on prior observations of donor behavior. While measuring actual donation rates remains a challenge, we measure donor action (i.e., calling a blood bank or making an appointment) as a proxy for actual donation. Simulations suggest that even a simple matching strategy can increase donor action rate by 10-15%; a pilot experiment with real donors finds a slightly smaller increase of roughly 5%. While overall action rates remain low, even this modest increase among donors in a global network corresponds to many thousands of more potential donors 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.