The Role of Automatic Identification Methods and Techniques in Blood Transfusion

  • E. Brodheim
Part of the Developments in Hematology and Immunology book series (DIHI, volume 22)


The overriding avoidable risk in blood transfusion lies in clerical errors made in recording information, transcribing information, and associating information. There are 20 to 30 such operations involved from the time that a unit of whole blood is taken from a donor to the time that a component derived from that unit is transfused to a patient. Each one of these operations is associated with an error rate of 1 to 10 thousand depending upon the nature of the transaction and the degree of care exercised. The cumulative effect is one error associated with every 10 to 20 blood components transfused. Similar error rates have been reported for other sectors of patient care [1]. Most of these errors are either detected by extensive cross-checking or do not result in serious harm to the patient. However, a small fraction of these errors go undetected and some of these have serious consequences. In the United States, two independent analyses of transfusion-related fatalities concluded that almost 90% of the avoidable deaths were the results of clerical oversights [2,3]. Automation that is based upon the use of bar codes (or other machine-readable symbols) has the potential to dramatically reduce these types of errors. At the same time, this type of automation can virtually eliminate all manual data recording and data transcription operations. This reduces the high labor costs associated with clerical operations which typically account for about one third of the total cost of a blood transfusion. Consequently, automation has the prospect of reducing costs while improving the quality of transfusion services delivered to patients. (These same considerations apply not only to blood transfusion but to virtually every other segment of patient care.)


Blood Banking Blood Center Test List Transfusion Service Donor Card 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 1989

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  • E. Brodheim

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