A data‐informed system to manage scarce blood product allocation in a randomized controlled trial of convalescent plasma

Abstract

Background Equitable allocation of scarce blood products needed for a randomized controlled trial (RCT) is a complex decision‐making process within the blood supply chain. Strategies to improve resource allocation in this setting are lacking. Methods We designed a custom‐made, computerized system to manage the inventory and allocation of COVID‐19 convalescent plasma (CCP) in a multi‐site RCT, CONCOR‐1. A hub‐and‐spoke distribution model enabled real‐time inventory monitoring and assignment for randomization. A live CCP inventory system using REDCap was programmed for spoke sites to reserve, assign, and order CCP from hospital hubs. A data‐driven mixed‐integer programming model with supply and demand forecasting was developed to guide the equitable allocation of CCP at hubs across Canada (excluding Québec). Results 18/38 hospital study sites were hubs with a median of 2 spoke sites per hub. A total of 394.5 500‐ml doses of CCP were distributed; 349.5 (88.6%) doses were transfused; 9.5 (2.4%) were wasted due to mechanical damage sustained to the blood bags; 35.5 (9.0%) were unused at the end of the trial. Due to supply shortages, 53/394.5 (13.4%) doses were imported from Héma‐Québec to Canadian Blood Services (CBS), and 125 (31.7%) were transferred between CBS regional distribution centers to meet demand. 137/349.5 (39.2%) and 212.5 (60.8%) doses were transfused at hubs and spoke sites, respectively. The mean percentages of total unmet demand were similar across the hubs, indicating equitable allocation, using our model. Conclusion Computerized tools can provide efficient and immediate solutions for equitable allocation decisions of scarce blood products in RCTs.

A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy

“…For the same decision problem as Asadpour et al [138], Baghbani [139] employs a fuzzy programming method, while Ghasemi et al [140] utilize a novel mixed possibilistic-robust-fuzzy programming approach. Li et al [141] propose an allocation model for COVID-19 convalescent plasma. On the other hand, Hosseini-Motlagh et al [142] focus on the production method of plasma and propose a robust programming model to determine both operational and tactical decisions.…”