Research + Innovation Showcase: Reducing Blood Products Discards with AI

April 30, 2020 – An AI-Driven Predictive Modelling Framework to Analyze and Visualize Blood Product Transactional Data for Reducing Blood Products Discards Jaber Rad, NICHE Research Group, Faculty of Computer Science, NICHE Research Group, Dalhousie University

Blood products and their derivatives are essential commodities in any healthcare system. Keeping an equilibrium between shortage and wastage in blood inventories has always been a challenge for health authorities due to perishable nature of blood products. One blood inventory management approach is to reduce the wastage of blood product due to unsafe handling, storage and inefficient transfer processes.

Research in blood product inventory management has predominantly been focused on reducing wastage due to outdates (i.e. expiry of the blood product), whereas wastage due to discards, related to the lifecycle of a blood product, is not well investigated and solved.

In this study, we take a data analytics approach to investigate blood product transition sequences in order to develop blood product transition models to both investigate the efficiency of the underlying processes and also to predict and flag a potential blood product discard.


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