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

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.