In a journal article written by Kavulavu et al. (2022), entitled “Challenges facing Blood Transfusion Services at a Regional Blood Transfusion Center in Western Kenya”, one of the key challenges faced at the Eldoret Regional Blood Transfusion Center (ERBTC) is unsuitable blood wastages and discards. While majority of these blood discards were due to transfusion transmissible infections, statistics have shown that about 33.3% of the discards were due to insufficient volumes. Aligned with this, this question is raised in hopes to know what strategies can be implemented to reduce the incidence of unsuitable blood discards. Moreover, this will also look into the potential of using technology and automated machines in addressing these problems.
According to the study entitled "Blood Donation Prediction Using Artificial Neural Network" by Barhoom et al., the research focuses on utilizing an Artificial Neural Network (ANN) model to predict blood donation patterns. By employing this advanced technology, the aim of the study is to enhance the accuracy of forecasting blood donation needs. The ANN model developed using the JustNN tool achieved an impressive test set performance accuracy of 99.31%, surpassing the results of other similar studies. This high level of accuracy is crucial in ensuring a sufficient and timely blood supply, especially in critical medical situations where blood donation plays a life-saving role. By leveraging AI tools like artificial neural network and AI-driven predictive models, transfusion centers can optimize their blood collection strategies, improve inventory management, and ultimately reduce the rate of unsuitable blood discards, ensuring a more efficient and sustainable blood supply chain.
Barhoom, Alaa, et al. “(PDF) Blood Donation Prediction Using Artificial Neural Network.” ResearchGate, 10 Oct. 2019, www.researchgate.net/publication/336990435_Blood_Donation_Prediction_using_Artificial_Neural_Network
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