Enhancing donor acquisition and retention in blood banks via AI-powered decision support framework

Abstract

Blood banks play a critical role in ensuring a steady supply of safe blood for medical procedures. However, donor recruitment and retention pose significant challenges to the sustainability of blood banks. This study proposes an AIenabled decision-support system to optimize donor recruitment and retention strategies in blood banks. The system leverages machine learning algorithms to analyze historical donor data, demographic information, and external factors to predict donor behavior and identify potential strategies for improving recruitment and retention. By incorporating AI into decision-making processes, blood banks can make data-driven decisions, enhance the efficiency of donor management, and allocate resources effectively. This paper presents the methodology used to develop the AIenabled system and discusses its potential benefits and implications for blood bank operations. Experimental results demonstrate the effectiveness of the system in identifying successful recruitment and retention strategies. Overall, the research offers valuable insights into the application of AI in blood bank management, ultimately leading to more sustainable and efficient donor recruitment and retention practices.