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The Lab and Risk Reduction, Part 3

Data indicate that 55-70% of the errors in the laboratory stem from the pre-analytical phase

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(Editor's note: This is a multi-part series on risk management for the laboratory. Click here to read Part 1. Click here to read Part 2.)

In this installment, we will discuss pre-analytical risks and errors.  Current data indicate that 55-70% of the errors in the laboratory stem from the pre-analytical phase.  Many of these are due to mis-identifying the patient.  It has been established that barcode and radio frequency (RF) bracelets reduce ID errors. 

For example, Morrison et al. reported a before-after design to evaluate the impact of the identification system on the frequency of mislabeled and unlabeled samples reported in our laboratory. Labeling errors fell from 5.45 per 10,000 labels before implementation to 3.2 per 10,000 afterward (a reduction of more than 40%).

Even with the use of bar codes and RF bracelets, ID errors still occur. Some of the reasons for the remaining ID errors include some 15 types of workarounds, including affixing patient identification bar codes to computer carts, scanners, doorjambs or even phlebotomist's belts. 

Another study further identified 31 causes of ID errors such as unreadable bar codes (crinkled, smudged, torn, missing, covered by another label); malfunctioning scanners; missing patient identification wristbands (chewed, soaked, missing); failing batteries; uncertain wireless connectivity; and emergencies.

Hemolysis
Another source of pre-analytical errors is the hemolyzed sample.  It has been reported as high as 3.3% of all of the routine samples, accounting for up to 40%-70% of all unsuitable specimens identified, nearly five times higher than other causes, such as insufficient, incorrect and clotted samples. In order to mitigate these errors the following approaches may help:

  1. continuous education of the personnel,
  2. systematic detection and quantification of hemolysis in any sample,
  3. immediate clinicians warning on the probability of in vivo hemolysis,
  4. completing of tests unaffected by hemolysis and
  5. a request of a second specimen for those potentially affected.  Many chemistry and immunochemistry instruments check all samples for hemoglobin in serum samples. It is wise to determine what the lowest amount of hemoglobin detected is. 

Grecu counted test request forms, samples and the types of pre-analytical errors that occurred in a Stat laboratory during a calendar year. During the 1-year period, a total of 168,728 samples and 88,655 requests forms were received in their Stat laboratory. The total number of pre-analytical errors was 1,457, accounting for 0.8% of the total number of samples received.

Of the total pre-analytical errors, 46.4% were hemolyzed samples for chemistry testing, 43.2% were clotted samples (in hematology), 6.4% were samples "lost-not received in the laboratory,"  2.9% samples showed an inadequate sample-anticoagulant ratio, 0.7% were requests with errors in patient identification, 0.3% were samples collected in blood collection tubes with inappropriate anticoagulant and 0.1% were missing test requests.

Incorrect Forms
In another study, out of total 61,983 samples received from patients, pre-analytical errors were found in 829 samples (1.3%). The most common mistakes were incorrect filling of forms (wrong names or IDs) or mislabeling of vials (289 cases, 0.47%). The second most common cause was the use of incorrect vials (149 cases, 0.24 %).

In the outpatient sample collection, the situation was slightly better, with total errors being found in 510 cases (0.69% or approximately one-half as many in the inpatient group). Here, too, the most common cause was a mismatch between form and sample (193 cases, 0.26 %).

In an effort to reduce these errors, five areas should be addressed:

  1. When you develop your QA system, you are provide a structure to measure the success of your system.  This should encourage the lab to 1) identify problems, 2) find and implement solutions (Review Parts 1 and 2 of this series), and 3) monitor the improvement and success of the QA system after the changes were implemented.  Most successful programs have at least these four major components: 1) continual training, 2) periodic competency, 3) error tracking (pro- and retro-tracking) and 4) proficiency testing (e.g., CAP).
  2. Training is needed continuously.  A file of training and ongoing education for each person should be available.  The inspectors like this type of documentation. It is known that well-trained and recognized employees produce fewer errors, have higher productivity and are more willing to take ownership for their instruments and their work.
  3. Competency testing adds a stamp of approval to the healthcare professional's performance.  There are a number of ways to structure this competency program.  It could include quizzes about an instrument or a test that the instrument performs, direct observation of the person performing a task, or a self-learning program ending with a quiz.
  4. Tracking errors is an important tool in the ongoing effort to reduce errors and risks.  It is not an excuse to blame anyone but to improve performance not only of the staff but the instruments as well.  Seeing errors go down will raise the morale.
  5. Periodic surveys of the three areas of errors -- pre-analytical, analytical and post-analytical -- will point to areas the (still) need work and illustrate the value of the overall QA system.

While the number of errors is diminishing, there remains work to be done.  Errors are not only often frustrating, but are time consuming, may cause inappropriate care of patients, and are a drain on financial resources.

David Plaut is a chemist and statistician in Plano, TX. Nathalie Lepage is a clinical biochemist and a biochemical geneticist at the Children's Hospital of Eastern Ontario and an associate professor in the Department of Pathology and Laboratory Medicine at the University of Ottawa, Ontario, Canada.

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