Computerized bar code-based blood identification systems and near-miss transfusion episodes and transfusion errors

Mayo Clin Proc. 2013 Apr;88(4):354-9. doi: 10.1016/j.mayocp.2012.12.010.

Abstract

Objective: To determine whether the use of a computerized bar code-based blood identification system resulted in a reduction in transfusion errors or near-miss transfusion episodes.

Patients and methods: Our institution instituted a computerized bar code-based blood identification system in October 2006. After institutional review board approval, we performed a retrospective study of transfusion errors from January 1, 2002, through December 31, 2005, and from January 1, 2007, through December 31, 2010.

Results: A total of 388,837 U were transfused during the 2002-2005 period. There were 6 misidentification episodes of a blood product being transfused to the wrong patient during that period (incidence of 1 in 64,806 U or 1.5 per 100,000 transfusions; 95% CI, 0.6-3.3 per 100,000 transfusions). There was 1 reported near-miss transfusion episode (incidence of 0.3 per 100,000 transfusions; 95% CI, <0.1-1.4 per 100,000 transfusions). A total of 304,136 U were transfused during the 2007-2010 period. There was 1 misidentification episode of a blood product transfused to the wrong patient during that period when the blood bag and patient's armband were scanned after starting to transfuse the unit (incidence of 1 in 304,136 U or 0.3 per 100,000 transfusions; 95% CI, <0.1-1.8 per 100,000 transfusions; P=.14). There were 34 reported near-miss transfusion errors (incidence of 11.2 per 100,000 transfusions; 95% CI, 7.7-15.6 per 100,000 transfusions; P<.001).

Conclusion: Institution of a computerized bar code-based blood identification system was associated with a large increase in discovered near-miss events.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood Safety / methods*
  • Blood Transfusion / statistics & numerical data*
  • Electronic Data Processing*
  • Humans
  • Medical Errors / prevention & control*
  • Medical Errors / statistics & numerical data
  • Patient Identification Systems*
  • Product Labeling*
  • Program Evaluation
  • Retrospective Studies