Improving patient safety by
reducing human errors.
According to a study by Johns Hopkins, more than 250,000
people in the United States die every year because of medical
mistakes, making it the third leading cause of death after heartdisease and cancer.1
60–70% of the critical medical decisions made by physicians
are based on laboratory test results2, making accuracy of theseresults essential for patient safety. The accuracy and reliabilityof these results depend on the quality of each step in thelaboratory testing workflow. Errors in any of these steps cannegatively affect patient safety and have the potential to damage
an institution’s reputation, diminish confidence in their services,and increase total operating costs.
Pre-analytical errors account for up to 75% of laboratory
errors.3 Pre-analytical errors occur during sample collection,transportation, storage, or processing. A review of recent
publications shows that 2.23% of samples collected for analysis
suffer from a human pre-analytical error.

For research use only. Not for use in diagnostic procedures.

Table 1. Compilation of most common pre-analytical errors indicate errors occur in 2.23% of clinical samples analyzed in these studies.4 - 7

Mis-labeled sample

Hemolysed

Wrong tube
type

Inadequate
volumeClottedTotalerrors

Total samples

Study 14

289

95

149

136

102

771

135,8085

Study 2

203

607

N/A

36

N/A

846

96,358Study 3

222

222

43

149

79715

329,582

Study 47

49,802

16,46016,038

14,772

7,175

104,247

4,220,518

TOTALS

50,516

17,384

16,23015,093

7,356

106,579

4,782,266% of Total Errors

47%

16%

15%

14%

7%

100%% of Total Samples

1.06%

0.36%0.34%

0.32%

0.15%

2.23%

Peer Reviewed Study

6

Reducing laboratory errorswith automation
Automation improves the accuracy of laboratory tests byreducing human errors once the sample is received by thelab and by identifying errors occurring before the samples
get there. Pre-analytical sample preparation automation andstandardization helps reduce laboratory test errors and costs
by automating the repetitive, error-prone, and bio-hazardous
processes. Once a sample reaches the laboratory allprocesses can be automated and standardized from sample
registration sample screening for analysis suitability, to setup
for analysis, to the analysis itself. Errors including mis-labeledtubes, inadequate volume, incorrect tube type, and clotted orhemolyzed blood can all be identified and flagged as incorrectprior to analysis, reducing the chance of reporting incorrect
results to a patient. Fully automated storage and retrieval ofarchived samples also reduce the potential for human errors.

References
1. Makary, M. A., & Daniel, M. (2016). Medical error—the third
leading cause of death in the US. BMJ, i2139.
doi: 10.1136/bmj.i2139
2. Forsman, L. R. (1992) Why is the laboratory an afterthought
for managed care organizations, Clin Chem, 42,813–816. PMID: 8653920.
3. Bonini, P., Plebani, M., Ceriotti, F., et al. (2002) Errors inlaboratory medicine. Clin Chem. 48:691-698.
4. Upreti, S. (2013). Types and Frequency of PreanalyticalErrors in Haematology Lab. Journal of Clinical andDiagnostic Research. doi: 10.7860/jcdr/2013/6399.3587
5. Chawla, R., Goswami, B., Tayal, D., & Mallika, V. (2010).
Identification of the Types of Preanalytical Errors in theClinical Chemistry Laboratory: 1-Year Study at G.B. Pant
Hospital. Laboratory Medicine, 41(2), 89–92.
doi: 10.1309/lm9jxzbmlsvjt9rk
6. Carraro, P., & Plebani, M. (2007). Errors in a Stat
Laboratory: Types and Frequencies 10 Years Later.
Clinical Chemistry, 53(7), 1338–1342.
doi: 10.1373/clinchem.2007.088344
7. Barak, M., & Jaschek, R. (2014). A new and effective way
for preventing pre-analytical laboratory errors. ClinicalChemistry and Laboratory Medicine (CCLM), 52(2).
doi: 10.1515/cclm-2013-0597

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