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ObjectivesThe utilization of reliable quality indicators (QIs) proven to be suitable for monitoring and improvement tools is one of the best choices to minimize of the risk of errors in all laboratory processes called as total testing process (TTP). In 2008, a Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) established by International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) developed the Model of Quality Indicators (MQI) complying with requirements of the ISO 15189:2012 standard for laboratory accreditation. They have also been dealing with harmonizing the QIs in most laboratories worldwide since then. The present study was set out to investigate the frequency of using IFCC WG-LEPS’ pre-QIs by Turkish laboratories and to assess the conformity of them, by taking into account Turkey’s conditions.MethodsA survey consisting nine questions was applied in 81 laboratories using SurveyMonkey.ResultsAccording to the survey results, most of the laboratories reported they have used pre-QIs in the quality standards of health prepared by Turkish Ministry of Health (MOH). A part of IFCC WG-LEPS’ pre-QIs were being utilized by more than 80% of the laboratories, the rest of which only used by 10% of laboratories.ConclusionsThe majority of the medical laboratories have been using the pre-QIs included in the guidelines of Quality Standards prepared by the MOH. The pre-QIs are partially compatible with IFCC WG-LEPS’ pre-QIs. The definitions of IFCC WG-LEPS’ pre-QIs may also be revised to make them more clear and understandable by IFCC WG-LEPS. The insufficiency of Health Information Management Systems (HIMS) limits the use of pre-QIs proposed by IFCC WG-LEPS. Finally, the education of relevant personnel about the use of HIMS and pre-QIs is very crucial to harmonize and to extend the use of IFCC WG-LEPS’ pre-QIs in Turkish medical biochemistry laboratories.

Introduction To interpret test results correctly, understanding of the variations that affect test results is essential. The aim of this study is: 1) to evaluate the clinicians’ knowledge and opinion concerning biological variation (BV), and 2) to investigate if clinicians use BV in the interpretation of test results. Materials and methods This study uses a questionnaire comprising open-ended and close-ended questions. Questions were selected from the real-life numerical examples of interpretation of test results, the knowledge about main sources of variations in laboratories and the opinion of clinicians on BV. A total of 399 clinicians were interviewed, and the answers were evaluated using a scoring system ranked from A (clinician has the highest level of knowledge and the ability of using BV data) to D (clinician has no knowledge about variations in laboratory). The results were presented as number (N) and percentage (%). Results Altogether, 60.4% of clinicians have knowledge of pre-analytical and analytical variations; but only 3.5% of them have knowledge related to BV. The number of clinicians using BV data or reference change value (RCV) to interpret measurements results was zero, while 79.4% of clinicians accepted that the difference between two measurements results located within the reference interval may be significant. Conclusions Clinicians do not use BV data or tools derived from BV such as RCV to interpret test results. It is recommended that BV should be included in the medical school curriculum, and clinicians should be encouraged to use BV data for safe and valid interpretation of test results.

Objective: In this study, we firstly aimed to determine components of biological variations (BVCs) in levels of glucose and glycated hemoglobin (HbA1c) in detail based on guidance from relevant organizations and experts. We also investigated whether reference intervals for both analytes were useful for evaluations, particularly consecutive test results. Methods: The study group consisted of 36 healthy volunteers. Samples were collected from each individual 4 times every 2 weeks for 45 days. All samples were assayed in duplicate within a single run. Finally, we estimated BVCs and the analytical performance specifications of both analytes. Results: Our results were fairly compatible with current biological variations (BVs) in both analytes reported in a database. It was calculated as within biological variation (CVI)=4.2% and between-subject variation (CVG)=5.3% for glucose while calculating as CVI=1.7% and CVG=4.5% for HbA1c. According to these results, the index of individuality (II) of glucose was higher than 0.6 while HbA1c’s II was lower than this value. Conclusion: We thought that guidelines from relevant international organizations should be followed to standardize the study design and to appropriately calculate BVCs for any analyte in BV studies. Finally, reference change value should be used to evaluate meaningful differences in HbA1c levels instead of reference interval.