Improving Blood Donor Care in a Collection Center Through Advanced Data Exploitation

Abstract

Background: Blood collection centers can take advantage of the huge amount of data collected on donors over the years to predict and detect early the onset of several diseases, However, dedicated tools are needed to carry out these analyses.

Objectives: This work develops a tool that combines available data with predictive tools to provide alerts to physicians and enable them to effectively visualize the history of critical donors in terms of the parameters that led to the alert.

Methods: The developed tool consists of data exchanging functions, interfaces to raise alerts and visualize donor history, and predictive algorithms. It was designed to be simple, modular and flexible.

Results: A prototype was applied to the Milan department of the Associazione Volontari Italiani Sangue, and was deemed suitable for prevention and early diagnosis objectives by the physicians of the center. The included Machine Learning predictive algorithms provided good estimates for the variables considered in the prototype.

Conclusion: Prevention and early diagnosis activities in blood collection centers can be effectively supported by properly using and displaying donor clinical data through a dedicated software tool.

Keywords: Blood Donation; Data Management; Early Diagnosis; Machine Learning.