A data-driven approach to patient blood management.

Area of Science:

• Transfusion Medicine
• Health Informatics
• Hospital Administration

Background:

• Implementing robust patient blood management (PBM) systems in large academic hospitals presents significant challenges.
• A collaborative effort between transfusion medicine and information technology departments was initiated to address these challenges.
• The development of an integrated, semiautomated system for monitoring red blood cell (RBC) usage was a key focus.

Purpose of the Study:

• To develop and implement a comprehensive, semiautomated system for monitoring red blood cell (RBC) usage in a large academic hospital.
• To leverage data analytics for targeted improvements in patient blood management (PBM) practices.
• To assess the impact of the developed system on RBC utilization and transfusion appropriateness.

Main Methods:

• Three overlapping databases were created: a daily transfusion report, a discharge database integrating transfusion and billing data, and a denominator database tracking hemoglobin tests.
• A set of algorithms was developed for the automated auditing of red blood cell (RBC) transfusions.
• Transfusions not meeting algorithmic criteria underwent manual review, with data compiled into reports for distribution and targeted education.

Main Results:

• The percentage of appropriate red blood cell (RBC) orders increased from 70%-80% to 90%-95% post-implementation.
• Overall RBC transfusions per 1000 patient-days decreased by 67% in targeted hospital areas.
• The patient blood management (PBM) program resulted in an approximate 3% reduction in the hospital's blood budget.

Conclusions:

• The semiautomated auditing system enables efficient and comprehensive analysis of blood usage across the hospital.
• Implementation of this technology has led to measurable improvements in patient blood management (PBM) practices.
• The system provides valuable data for identifying areas for PBM optimization and cost reduction.