American Red Cross Uses Analytics-Based Methods to Improve Blood-Collection Operations

Turgay Ayer, Can Zhang, Chenxi Zeng, Chelsea C. White III, V. Roshan Joseph, Mary Deck, Kevin Lee, Diana Moroney, Zeynep Ozkaynak, American Red Cross Uses Analytics-Based Methods to Improve Blood-Collection Operations, Interfaces, Volume 48, Issue 1, January-February 2018, pp. 24–34.

In this study, we describe a regional-level cryoprecipitate (cryo)-collection project at the American Red Cross Southern Region, one of the 36 Red Cross regions in the United States, which serves more than 120 hospitals in the Southern part of the country. Managing collections for cryo units is particularly challenging because producing cryo requires the collected whole blood to be processed within 8 hours after collection; for all other blood products, this time constraint is at least 24 hours. This project focuses on dynamically determining when and from which mobile collection sites the American Red Cross Southern Region should collect whole blood for cryo production, such that it meets its weekly collection targets and minimizes its collection costs. To solve this problem, we developed a new collection model, which allows different types of collections at the same collection site and developed a dynamic programming approach to solve the problem to near optimality. Analyzing the dynamic programming results led us to create a greedy-algorithm heuristic, which we implemented in a decision support tool (DST) to systematize the selection of the collection sites. The implementation of the DST in the Red Cross Southern Region resulted in an increase in the number of whole blood units that can be shipped back to the production facility and processed within eight hours after collection. During the fourth quarter of 2016, this facility processed about 1,000 more units of cryo per month (an increase of 20 percent) at a slightly lower collection cost, resulting in an approximately 40 percent reduction in the per-unit collection cost for cryo. Based on the successful implementation in the Southern Region, the American Red Cross also implemented our DST in its St. Louis facility and plans to implement it at its 10 other cryo production facilities.

Model:

To solve this complex, dynamic scheduling problem under uncertainty, we modeled the problem as a finite-horizon Markov decision process (MDP). 

Difficulty:

Given the large state and action spaces, approximations are needed to determine computationally efficient near-optimal policies for our problem.