Survey on blood supply chain management: Models and methods

Production and Quality Requirements of Human Platelet Lysate: A Position Statement from the Working Party on Cellular Therapies of the International Society of Blood Transfusion

Hospitals are imposed with the challenge of developing efficient blood inventory management tools due to its limited supply and uncertain demand. Besides, the short shelf-life of blood further comp...

Hybrid ordering policies for platelet inventory management under demand uncertainty

Blood transfusion is one of the most crucial and commonly administered therapeutics worldwide. The need for more accurate and efficient ways to manage blood demand and supply is an increasing concern. Building a technology-based, robust blood demand and supply chain that can achieve the goals of reducing ordering frequency, inventory level, wastage and shortage, while maintaining the safety of blood usage, is essential in modern healthcare systems. In this study, we summarize the key challenges in current demand and supply management for red blood cells (RBCs). We combine ideas from statistical time series modeling, machine learning, and operations research in developing an ordering decision strategy for RBCs, through integrating a hybrid demand forecasting model using clinical predictors and a data-driven multi-period inventory problem considering inventory and reorder constraints. We have applied the integrated ordering strategy to the blood inventory management system in Hamilton, Ontario using a large clinical database from 2008 to 2018. The proposed hybrid demand forecasting model provides robust and accurate predictions, and identifies important clinical predictors for short-term RBC demand forecasting. Compared with the actual historical data, our integrated ordering strategy reduces the inventory level by 40% and decreases the ordering frequency by 60%, with low incidence of shortages and wastage due to expiration. If implemented successfully, our proposed strategy can achieve significant cost savings for healthcare systems and blood suppliers. The proposed ordering strategy is generalizable to other blood products or even other perishable products.

A decision integration strategy for short-term demand forecasting and ordering for red blood cell components

Many studies have shown that repetition of study material with temporal gaps between the repetitions (i.e., spaced in time) is more effective for long-term retention than are repetitions in immediate succession (i.e., massed; Greene, 1989). Although this spacing effect has proven to be robust in the laboratory (Cepeda, Pashler, Vul, Wixted, & Rohrer, 2006), its status in the real world is relatively understudied. Other research has demonstrated the benefit of memory retrieval on subsequent retrieval of the same information (Bjork, 1975, 1988; Roediger & Karpicke, 2006), referred to as the testing effect. However, it is not clear how spacing and retrieval can be optimally combined in order to enhance knowledge retention in a real-world setting. To investigate this question, we analyzed longitudinal data from 10,514 individuals, collected in the context of naturally occurring workplace training. To determine the impact of spaced retrieval on knowledge retention, these data were analyzed using a generalized linear mixed model with the following fixed factors: (1) spacing interval between repetitions of content training (retrieval practice), (2) retention interval, and (3) question format. Random factors included the specific content on which employees were trained, which was clustered by employee and, in turn, by company, resulting in a three-level hierarchy. The results showed a significant interaction between spacing interval and retention interval: the optimal amount of spacing between repeated retrieval events increased as the retention interval increased. These findings are in line with the results of laboratory studies, demonstrating the relevance and transferability of laboratory-based research to real-world contexts.

https://link.springer.com/content/pdf/10.3758/s13428-018-1184-7.pdf

The spacing effect stands up to big data

Maintaining a robust blood product supply is an essential requirement to guarantee optimal patient care in modern health care systems. However, daily blood product use is difficult to anticipate. Platelet products are the most variable in daily usage, have short shelf lives, and are also the most expensive to produce, test, and store. Due to the combination of absolute need, uncertain daily demand, and short shelf life, platelet products are frequently wasted due to expiration. Our aim is to build and validate a statistical model to forecast future platelet demand and thereby reduce wastage. We have investigated platelet usage patterns at our institution, and specifically interrogated the relationship between platelet usage and aggregated hospital-wide patient data over a recent consecutive 29-mo period. Using a convex statistical formulation, we have found that platelet usage is highly dependent on weekday/weekend pattern, number of patients with various abnormal complete blood count measurements, and location-specific hospital census data. We incorporated these relationships in a mathematical model to guide collection and ordering strategy. This model minimizes waste due to expiration while avoiding shortages; the number of remaining platelet units at the end of any day stays above 10 in our model during the same period. Compared with historical expiration rates during the same period, our model reduces the expiration rate from 10.5 to 3.2%. Extrapolating our results to the ∼2 million units of platelets transfused annually within the United States, if implemented successfully, our model can potentially save ∼80 million dollars in health care costs.

https://www.pnas.org/content/pnas/114/43/11368.full.pdf

Xiaoying Tian

Papers

Big data modeling to predict platelet usage and minimize wastage in a tertiary care system