We analyze not‐for‐profit Medical Surplus Recovery Organizations (MSROs) that manage the recovery of surplus (unused or donated) medical products to fulfill the needs of underserved healthcare facilities in the developing world. Our work is inspired by an award‐winning North American non‐governmental organization (NGO) that matches the uncertain supply of medical surplus with the receiving parties’ needs. In particular, this NGO adopts a recipient‐driven resource allocation model, which grants recipients access to an inventory database, and each recipient selects products of limited availability to fill a container based on its preferences. We first develop a game theoretic model to investigate the effectiveness of this approach. This analysis suggests that the recipient‐driven model may induce competition among recipients and lead to a loss in value provision through premature orders. Further, contrary to the common wisdom from traditional supply chains, full inventory visibility in our setting may accelerate premature orders and lead to loss of effectiveness. Accordingly, we identify operational mechanisms to help MSROs deal with this problem. These are: (i) appropriately selecting container capacities while limiting the inventory availability visible to recipients and increasing the acquisition volumes of supplies, (ii) eliminating recipient competition through exclusive single‐recipient access to MSRO inventory, and (iii) focusing on learning recipient needs as opposed to providing them with supply information, and switching to a provider‐driven resource allocation model. We use real data from the NGO by which the study was inspired and show that the proposed improvements can substantially increase the value provided to recipients.
Hepatitis C virus (HCV) prevalence in prison systems is about 10 times higher than in the community. As such, prison systems offer a unique opportunity to control the HCV epidemic. New HCV-treatment drugs are very effective, but providing treatment to all inmates is prohibitively expensive unless prices fall. Current practice is to prioritize treatment based on disease severity and puts less emphasis on other factors such as the remaining sentence length and injection drug use behavior. In “Prioritizing Hepatitis C Treatment in U.S. Prisons,” T. Ayer, C. Zhang, A. Bonifonte, A. Spaulding, and J. Chhatwal analyze optimal approaches for treatment prioritization under resource constraints by developing a restless bandit modeling framework. They present an easy-to-implement closed-form index policy to support hepatitis C treatment prioritization decisions in U.S. prisons. They also test their proposed policy using a detailed, realistic agent-based simulation model and shed light on several controversial health policy decisions related to hepatitis C treatment prioritization.
Because of the importance, limited supply, and perishable nature of blood products, effective management of blood collection is critical for high-quality healthcare delivery. In this paper, working closely with the American Red Cross (ARC), we study a blood collection problem focusing on whole blood that is to be processed into cryoprecipitate (cryo), a critical blood product for controlling massive hemorrhaging. In particular, we aim to determine when and from which mobile collection sites to collect blood for cryo production, such that the weekly collection target is met while the collection costs are minimized. The cryo collection problem imposes a unique challenge: if blood collected is to be processed into cryo units, it has to be processed within eight hours after collection, while this time limit is 24 hours for most other blood products. To analyze the cryo collection problem, we first develop a mathematical program to represent and compare two different blood collection business models, namely, the status quo nonsplit model and an alternative model we propose, which splits each collection window into two intervals and allows different types of collections in the two intervals. Then, we establish several structural properties of the proposed mathematical program and develop a near-optimal solution algorithm to determine the cryo collection schedules under each collection model. Our extensive computational analyses based on real data indicated that, compared with the status quo, our proposed collection model can significantly reduce total collection costs. Based on this significant potential impact, our proposed collection model has been implemented by the ARC Douglasville manufacturing facility, the largest ARC blood manufacturing facility supplying blood to about 120 hospitals in the southern United States. Field data from postimplementation indicated that our proposed solution has resulted in (i) reducing inconsistencies in supply of cryo collections, and (ii) an approximately 40% reduction in the per-unit collection cost for cryo. Because of this success, the ARC is now at the stage of rolling out our proposed solution approach to other regions in the nation.
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