Ensuring Accuracy in Pre-analytical Testing through Lab Automation

Laboratories have a prominent role in helping to reduce avertible medical error. Find out how software can help identify errors prior to analysis.
Ensuring Accuracy in Pre-analytical Testing through Lab Automation

In the fast-paced, complex world of healthcare, clinicians must make accurate patient care decisions quickly. While a number of factors influence a clinical diagnosis or course of treatment, the majority of care decisions—up to 70%—are made using clinical laboratory test results.1 Because of this, accuracy remains of utmost importance for laboratories, which are often tasked with producing quality results amid time, cost and resource pressures.

The impact of medical errors

Medical errors in general are the third leading cause of death in the U.S.2 Beyond their impact on patient care, they are responsible for $17.1 billion in avoidable healthcare costs.3 Because of laboratories’ heavy influence on patient care, they have a prominent role in helping to reduce avertible medical error.

Opportunities for error exist in all phases of laboratory testing, and many have been addressed through the widespread adoption of lab automation. Historically, most automated solutions have focused on sample analysis, the phase of testing sandwiched between pre- and post-analytical processes. Overwhelmingly, however, it is the pre-analytical testing phase that presents the greatest challenge for laboratories.

Up to 75% of testing errors take place during pre-analytical testing.4 Research shows that the average cost for each pre-analytical error is an estimated $208, and that pre-analytical specimen errors can account for up to 1.2% of overall hospital operating costs.5 Added to this is the strain these errors place on hospital resources due to redraws, follow-up and delays in treatment.5

Challenges within the pre-analytical testing stage

For the laboratory, the pre-analytical testing stage consists of numerous steps. Many pre-analytical activities, however, take place before a sample even reaches the laboratory. Thus, a majority of errors result from factors occurring outside the laboratory’s control and are likely, at least in part, due to the fact that many healthcare professionals are involved in sample collection. The most common types of pre-analytical errors include low sample volume, clotting, hemolysis, and incorrect tube type or patient misidentification,6,7 all of which can lead to reporting delays and errors.

Because of this, laboratories intent on reducing errors need greater quality control in laboratory automation for the entire testing process—from blood draw to results delivery. To this end, there has been a demand for lab automation software that can help identify errors prior to analysis.

The most comprehensive early sample-condition specimen check available in current systems

The DxA 5000 total laboratory automation solution provides the most comprehensive early sample-condition specimen check available in current systems. The DxA 5000 identifies pre-analytical issues and segregates samples with such issues before they enter the analytical workflow and can cause errors and/or delays. This intelligent automation not only helps to ensure integrity in test results reporting, but it also is intended to prevent disruptions that can delay the transfer of vital information needed for patient care.

In three seconds, the DxA 5000 performs eight different measurements, automatically verifying tube type, cap color, orders pending, test volume, draw volume to determine the blood-to-anticoagulant ratio, sample identification to ensure the right barcode is on the right tube, sample spin status and tube weight. Additionally, the DxA 5000 checks sample volume at three separate points—pre-centrifugation, post-centrifugation and prior to sample storage—to ensure sufficient volume is available for present test orders, as well as any that may be needed in the future.

Curious about the DxA 5000’s pre-analytical quality checks?

1Da Rin G. Pre-analytical workstations: a tool for reducing laboratory errors. Clin Chim Acta. 2009;404:68–74.

2Daniel M, Makary M. Medical error—the third leading cause of death in the US. BMJ. 2016;353:i2139.

3Van Den Bos J, Rustagi K, Gray T et al. The $17.1 billion problem: the annual cost of measurable medical errors. Health Affairs. 2011;30(4):596–603.

4Bonini P, Plebani M, Ceriotti F et al. Errors in laboratory medicine. Clin Chem. 2002;48:691–698.

5Green SF. The cost of poor blood specimen quality and errors in preanalytical processes. Clin Biochem. 2013;46:1175–1179.

6Salvagno GL, Lippi G, Bassi A et al. Prevalence and type of pre-analytical problems for inpatients samples in coagulation laboratory. J Eval Clin Pract. 2008;14:351–353.

7Upreti S, Upreti S, Bansal R et al. Types and frequency of preanalytical errors in haematology lab. J Clin Diagn Res. 2013;7(11):2491–2493.

This product may not be available in your country or region at this time. Please contact your Beckman Coulter sales representative or distributor for more information.

Peter Soltani
Peter Soltani
Peter Soltani is Senior Vice President & General Manager for Hematology & Urinalysis, and recently expanded his role to lead the Workflow & IT Solutions business.

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Ensuring Accuracy in Pre-analytical Testing through Lab Automation

Ensuring Accuracy in Pre-analytical Testing through Lab Automation

Laboratories have a prominent role in helping to reduce avertible medical error. Find out how software can help identify errors prior to analysis.