Extra analytical errors, and associated factors in the hematology Laboratory at debre tabor comprehensive specialized hospital in Northcentral ethiopia

Abstract

Background: Laboratory testing is a complicated procedure, and it is not as safe as it could or should be. Errors in medical laboratory elaborated as failure of a planned action to achieve an aim, occurring at any part of the laboratory cycle. Clinical laboratories' quality control methods and quality evaluation programs dealing with the analytical components of testing have long been the focus of clinical laboratories. However, a rising body of information generated over the last few decades shows that clinical laboratory quality cannot be guaranteed by focusing solely on analytical features. Pre-analytical errors account for 46–68% of total errors, with post- analytical errors accounting for 19–47% of total errors. This may have negative impact on clinical decision, patient care and reliability of the laboratory by contributing to inappropriate treatment, an increase in length of hospital stay, and dissatisfaction with healthcare service. Therefore, this study indicates the magnitude of extra analytical errors and associated factors in Debre Tabor comprehensive specialized hospital. This will pave the way for the improvement of the laboratory service. Objective: To determine the magnitude of extra analytical errors and associated factors in the hematology laboratory of Debre Tabor comprehensive specialized hospital from September 23 to November 11, 2021, Debre Tabor, Northcentral Ethiopia. Methods: A hospital-based cross-sectional study design was implemented on 2331 test request forms and test samples of patents. A consecutive convenient sampling technique was employed to select study samples. Completeness of the test request sample, collection method, and other associated factors were assessed based on the structured check list. The data was entered into Epi Data version 4.16 and exported to SPSS version 20 for analysis. Bi-variable and multivariable binary logistic regression were used to identify associated factors. In multivariable analysis, a P- value of < 0.05 was considered a statistically significant association. Result: The magnitude of pre-analytical error and post-analytical error was 80.9% (95%CI; 79.2–82.5%) and 28.4% (95% CI; 26.6–30.3), respectively. From the total sample of 2331 observed, 29.9% of the tests' turnaround times were prolonged from the TAT set by the laboratory; 69 (3.0%) of the requests did not have the hospital‟s patient ident