title: Hospital blood bank information systems accurately reflect patient transfusion: results of a validation study.
abstract: BACKGROUND
  Hospital transfusion laboratories collect information regarding blood transfusion and some registries gather clinical outcomes data without transfusion information, providing an opportunity to integrate these two sources to explore effects of transfusion on clinical outcomes. However, the use of laboratory information system (LIS) data for this purpose has not been validated previously.
  
  
  METHODS
  Validation of LIS data against individual patient records was undertaken at two major centers. Data regarding all transfusion episodes were analyzed over seven 24-hour periods.
  
  
  RESULTS
  Data regarding 596 units were captured including 399 red blood cell (RBC), 95 platelet (PLT), 72 plasma, and 30 cryoprecipitate units. They were issued to: inpatient 221 (37.1%), intensive care 109 (18.3%), outpatient 95 (15.9%), operating theater 45 (7.6%), emergency department 27 (4.5%), and unrecorded 99 (16.6%). All products recorded by LIS as issued were documented as transfused to intended patients. Median time from issue to transfusion initiation could be calculated for 535 (89.8%) components: RBCs 16 minutes (95% confidence interval [CI], 15-18 min; interquartile range [IQR], 7-30 min), PLTs 20 minutes (95% CI, 15-22 min; IQR, 10-37 min), fresh-frozen plasma 33 minutes (95% CI, 14-83 min; IQR, 11-134 min), and cryoprecipitate 3 minutes (95% CI, -10 to 42 min; IQR, -15 to 116 min).
  
  
  CONCLUSIONS
  Across a range of blood component types and destinations comparison of LIS data with clinical records demonstrated concordance. The difference between LIS timing data and patient clinical records reflects expected time to transport, check, and prepare transfusion but does not affect the validity of linkage for most research purposes. Linkage of clinical registries with LIS data can therefore provide robust information regarding individual patient transfusion. This enables analysis of joint data sets to determine the impact of transfusion on clinical outcomes.
authors.affiliation: Transfusion Medicine Services, Australian Red Cross Blood Service, Melbourne, Victoria, Australia. zmcquilten@arcbs.redcross.org.au
authors.forename: Zoe K
authors.initials: ZK
authors.lastname: McQuilten
authors.forename: Nikita
authors.initials: N
authors.lastname: Schembri
authors.forename: Mark N
authors.initials: MN
authors.lastname: Polizzotto
authors.forename: Christine
authors.initials: C
authors.lastname: Akers
authors.forename: Melissa
authors.initials: M
authors.lastname: Wills
authors.forename: Merrole F
authors.initials: MF
authors.lastname: Cole-Sinclair
authors.forename: Susan
authors.initials: S
authors.lastname: Whitehead
authors.forename: Erica M
authors.initials: EM
authors.lastname: Wood
authors.forename: Louise E
authors.initials: LE
authors.lastname: Phillips
mesh_terms: D001315:Australia; D001771:Blood Banks / Q000592:standards* / Q000706:statistics & numerical data; D016913:Blood Component Transfusion / Q000706:statistics & numerical data*; D006751:Hospital Information Systems / Q000592:standards* / Q000706:statistics & numerical data; D006784:Hospitals, Teaching / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D008499:Medical Records / Q000592:standards* / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D012042:Registries / Q000706:statistics & numerical data; D015203:Reproducibility of Results