title: A data-driven approach to patient blood management.
abstract: BACKGROUND
  Patient blood management (PBM) has become a topic of intense interest; however, implementing a robust PBM system in a large academic hospital can be a challenge. In a joint effort between transfusion medicine and information technology, we have developed three overlapping databases that allow for a comprehensive, semiautomated approach to monitoring up-to-date red blood cell (RBC) usage in our hospital. Data derived from this work have allowed us to target our PBM efforts.
  
  
  METHODS
  Information on transfusions is collected using three databases: daily report, discharge database, and denominator database. The daily report collects data on all transfusions in the past 24 hours. The discharge database integrates transfusion data and diagnostic billing codes. The denominator database allows for rate calculations by tracking all patients with a hemoglobin test ordered. A set of algorithms is applied to automatically audit RBC transfusions. The transfusions that do not fit the algorithms' rules are manually reviewed. Data from audits are compiled into reports and distributed to medical directors. Data are also used to target education efforts.
  
  
  RESULTS
  Since our PBM program began, the percentage of appropriate RBC orders increased from an initial 70%-80% to 90%-95%, and the overall RBC transfusions/1000 patient-days has decreased by 67% in targeted areas of the hospital. Our PBM program has shaved approximately 3% from our hospital's blood budget.
  
  
  CONCLUSIONS
  Our semiautomated auditing system allows us to quickly and comprehensively analyze and track blood usage throughout our hospital. Using this technology, we have seen improvements in our hospital's PBM.
authors.affiliation: Department of Laboratory Medicine and Pathology, University of Minnesota Medical School; Fairview Health Services, University of Minnesota Medical Center, Minneapolis, Minnesota; Fairview Health Services, Southdale Hospital, Edina, Minnesota.
authors.forename: Claudia S
authors.initials: CS
authors.lastname: Cohn
authors.forename: Julie
authors.initials: J
authors.lastname: Welbig
authors.forename: Robert
authors.initials: R
authors.lastname: Bowman
authors.forename: Susan
authors.initials: S
authors.lastname: Kammann
authors.forename: Katherine
authors.initials: K
authors.lastname: Frey
authors.forename: Nicole
authors.initials: N
authors.lastname: Zantek
mesh_terms: D000046:Academic Medical Centers / Q000379:methods*; D000465:Algorithms; D001771:Blood Banks / Q000706:statistics & numerical data*; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000592:standards*; D057286:Electronic Health Records / Q000706:statistics & numerical data; D055317:Evidence-Based Practice / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D008485:Medical Audit / Q000379:methods / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data*; D000094902:Blood Banking / Q000379:methods*