Analysis of the Six Sigma Principle in Pre-analytical Quality for Hematological Specimens.

Abstract

Introduction Blood tests are essential for detecting and treating hospitalized individuals with diseases. Laboratory blood tests provide doctors with critical information required to treat their patient's illnesses. The most common sources of error in clinical laboratories are pre-analytical errors. Although quality control measures can remediate analytical errors, there is a requirement for stringentquality checks in the pre-analytical sector as these activities are performed outside of the laboratory. Pre-analytical errors when combined with the sigma value can reflect a better pictureas the sigma value represents the laboratory's performance. Aim In this study, six sigma and the Pareto principle were utilizedto assess pre-analytical quality indicators for evaluating the performance of a clinical hematologylaboratory. Methodology This is a retrospective observational study conducted from 2015 to 2023 (for a period of eight years). Information about the frequency of pre-analyticalerrorswas retrieved from the hematology section of the central diagnostic researchlaboratory information system and the data was entered into an MS Excel sheet and datawas evaluated utilizingSPSS version 23(IBM Corp., Armonk, NY). Results In the current research, total of 15 pre-analytical errors were noted. Out of the total 15 pre-analytical errors studied, 55.4% of pre-analytical errors were notedamong which80% errors were due to lack of mention of sample type or received timeand20% of errorswere attributedto no mention of diagnosis in requisition forms. The next most common errors noted were insufficient samples (8.26%) followed by absence of physician's signature (7%), incomplete request form (5.4%), age (4.2%), unique hospital identification (UHID) number (3.7%), clotted samples and transportation of the samples (3.6%), date and incorrect vials (2.6%). Gender (0.95%), hemolysed(0.85%), and lipemic samples (0.45%).Hemolysed andlipemic samples hada sigma value of 4.4 and 4.6, respectively, whereas gender and age had a sigma value of 4.3 and 3.8, inadequate sample for testing and an incorrect anticoagulant to blood ratio had a sigma value of 3.6, indicating that sample collection has to beimproved as the inverse relationship is noted between sigma value and laboratory performance. Conclusion Pareto chart and sigma value can help recognize most common pre-analytical errors, which consequently will help to prevent further recurrence of pre-analytical errors. Adequate training with regard to best practices in phlebotomy for interns, clinicians and technicians must be provided to decrease quantitative errors, which will further enhancetotal quality management in the laboratory.