title: Restrictive blood transfusion practices are associated with improved patient outcomes.
abstract: BACKGROUND
  Blood transfusion has been cited as one of the five most overutilized therapeutic procedures in the United States. We assessed the impact of clinical decision support at computerized physician order entry and education on red blood cell (RBC) transfusions and clinical patient outcomes at our institution.
  
  
  METHODS
  Clinical patient outcomes and RBC transfusions were assessed before and after implementation of a best practice alert triggered for transfusions when the hemoglobin level was higher than 7 g/dL for all inpatient discharges from January 2008 through December 2013. Retrospective clinical and laboratory data related to RBC transfusions were extracted: case-mix complexity, patient discharges and selected surgical volumes, and patient outcomes (mortality, 30-day readmissions, length of stay).
  
  
  RESULTS
  There was a significant improvement in RBC utilization as assessed by RBC units transfused per 100 patient-days-at-risk. Concurrently, hospital-wide clinical patient outcomes showed improvement (mortality, p = 0.034; length of stay, p = 0.003) or remained stable (30-day readmission rates, p = 0.909). Outcome improvements were even more pronounced in patients who received blood transfusions, with decreased mortality rate (55.2 to 33.0, p < 0.001), length of stay (mean, 10.1 to 6.2 days, p < 0.001), and 30-day readmission rate (136.9 to 85.0, p < 0.001). The mean number of units transfused per patient also declined (3.6 to 2.7, p < 0.001). Acquisition costs of RBC units per 1000 patient discharges decreased from $283,130 in 2009 to $205,050 in 2013 with total estimated savings of $6.4 million and likely far greater impact on total transfusion-related costs.
  
  
  CONCLUSIONS
  Improved blood utilization is associated with improved clinical patient outcomes.
authors.affiliation: Department of Pathology, Stanford University, Stanford, California; Department of Medicine, Stanford University, Stanford, California.
authors.forename: Lawrence T
authors.initials: LT
authors.lastname: Goodnough
authors.forename: Paul
authors.initials: P
authors.lastname: Maggio
authors.forename: Eric
authors.initials: E
authors.lastname: Hadhazy
authors.forename: Lisa
authors.initials: L
authors.lastname: Shieh
authors.forename: Tina
authors.initials: T
authors.lastname: Hernandez-Boussard
authors.forename: Paul
authors.initials: P
authors.lastname: Khari
authors.forename: Neil
authors.initials: N
authors.lastname: Shah
mesh_terms: D000328:Adult; D000368:Aged; D020000:Decision Support Systems, Clinical / Q000706:statistics & numerical data*; D003953:Diagnosis-Related Groups; D057286:Electronic Health Records; D017707:Erythrocyte Transfusion / Q000592:standards* / Q000706:statistics & numerical data*; D005260:Female; D006454:Hemoglobins; D017052:Hospital Mortality; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data; D017410:Practice Guidelines as Topic; D012189:Retrospective Studies
chemical_list: D006454:Hemoglobins