Evaluation of analytical reliability based on variance index score, measurement uncertainty and root cause analysis in a 2-year external quality assessment scheme

Assessment of Analytical Quality in Clinical Laboratory Using Sigma Metrics

Six sigma assessments provided a comprehensive and quantitative measure of analytical quality in a clinical laboratory and incorporated sigma metrics into routine quality assurance enhances reliability, optimizes QC protocols, and strengthens patient safety.

Analytical Performance Specifications Derived from External Quality Assessment Data: Comparison with Biological Variation-Based Models for Intermediate Precision Assessment.

Background Analytical performance specifications (APSs) are essential for quality management in medical laboratories. Considering the discrepancies between laboratory performance and existing guidelines, lack of consideration of concentration-dependent variability, and absence of recommendations for certain parameters, we aimed to define APSs for internal use in biochemistry, hemostasis, and hematology based on external quality assessment (EQA) peer group data and evaluate their suitability for intermediate precision assessment versus biological variation (BV)-based APSs. Methods EQA-based allowable CV (CVallowable) was estimated from pooled CVs derived from EQA peer group results. Allowable bias, expanded measurement uncertainty, and total allowable error were calculated. CVallowable targets were assessed using intermediate precision data from different analytical systems within a laboratory group and compared with BV-based APSs. Concordance between theoretical specifications derived from mathematical models and observed analytical performance (AP) was evaluated using (i) the proportion of internal QC CVs meeting the predefined CVallowable across laboratories and (ii) observed analytical imprecision expressed as a percentage of the allowable imprecision budget. Results APSs were established for 110 biochemical analytes, 23 hemostasis parameters, and 28 hematology parameters across different concentration ranges. EQA-derived APSs agreed with observed intermediate precision for 102 biochemical, 14 hemostasis, and 24 hematology parameters. BV-based APSs showed agreement for only 49 biochemical, two hemostasis, and 11 hematology parameters, while overly restrictive goals or lack of agreement were observed for several analytes. Conclusions APSs derived from pooled EQA peer group data provide realistic and technically achievable intermediate precision targets consistent with current AP and thus can complement BV-based specifications.

Measurement uncertainty, reference change value, and total error of total and free prostate specific antigen tests

Abstract Objectives The dispersion of measurement results is defined as the measurement uncertainty (MU), and the reference change value (RCV) indicates whether the difference between the current result and previous results is significant. The purpose of this study is to evaluate whether the analytical performance of total PSA (tPSA) and free PSA (fPSA) measurements conforms to international quality standards by determining analytical coefficient of variation (CVA), Bias, MU, and RCV values and comparing them with European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)’s allowable limits. Methods The study includes Internal Quality Control (IQC) and External Quality Assurance (EQA) results for the period from August 2021 to February 2022 of tPSA and fPSA. While the long-term CV was obtained from IQC for MU and RCV values, Total Error (TE) values were calculated with the monthly IQC CV and EQA bias. MU Extended values (MU%) are calculated according to ISO/TS 20914. Results The CVA of tPSA and fPSA was 3.44 % and 7.26 %, respectively. tPSA’s RCV and MU were found to be 17.78 and 9.74, and fPSA’s RCV and MU were found to be 23.7 and 15.23, respectively. Conclusions Our results indicate that tPSA demonstrates better analytical performance than fPSA due to its lower CV and bias values. We found that the RCV of a test, such as tPSA and fPSA, which play a key role in post-operative follow-up, must be calculated and shared with the surgeon. In addition, since the cut-off value decides whether or not to request a reflective fPSA, MU should also be calculated and reported.

Evaluation of Pre-Analytical Performance Using IFCC Quality Indicators, Six Sigma Metrics and Root Cause Analysis in a Biochemistry Laboratory—A Retrospective Study

Background: Pre-analytical errors account for the majority of failures across the total testing process. This study evaluated pre-analytical performance in a tertiary-care biochemistry laboratory in Riyadh, Saudi Arabia, using IFCC-aligned Quality Indicators (QIs), Six Sigma metrics, and structured Root Cause Analysis (RCA). Methods: A retrospective analysis of all biochemistry tests processed between January and December 2024 at a tertiary-care hospital was conducted. Rejected tests were classified into seven IFCC-aligned QI categories. Sigma metrics assessed process capability, Pareto analysis identified the vital few contributors, and RCA using the Ishikawa framework identified human, equipment, environmental, and process-related factors. Rejection patterns were described by department and work shift. Results: Of 845,647 tests performed, 10,783 (1.28%) were rejected, yielding an overall process capability of 3.89σ (Minimum Acceptable). Hemolysis was the leading cause (8186 tests; 75.92%) at 3.97σ, the only indicator classified as High against the IFCC WG-LEPS registry. The remaining six indicators demonstrated Good to Very Good performance (4.59σ–5.33σ). Pareto analysis identified hemolysis and inappropriate tube use (7.60%) as the vital few, jointly responsible for 83.52% of rejections. RCA implicated venipuncture technique, needle gauge selection, workload pressure, and prolonged tourniquet application as key contributors. The Emergency Department generated the highest inpatient rejection burden (38.7%). Conclusions: Although the overall rejection rate compared favorably with international benchmarks, hemolysis was the principal process vulnerability, with inappropriate tube selection as a secondary target. Recommended quality improvement strategies include structured phlebotomy training, real-time hemolysis index feedback, and Emergency Department-specific initiatives; structural solutions such as dedicated inpatient phlebotomy services warrant prospective evaluation alongside training-based interventions.

Sigma metric and Quality Goal Index assessment of hemoglobin A1c analytical performance in an ambulatory accredited laboratory.

Hospital quality indicator weighting using Analytical Hierarchy Process (AHP) method

Hospital quality measurement systems frequently lack appropriate weighting among indicators, limiting their capacity to represent actual performance outcomes and to support evidence-based policy prioritization. Developing a composite index with empirically derived weights is therefore essential to enhance the precision and practical utility of hospital quality assessments. An integrated approach is needed to combine various indicators into a single aggregate measure which can serve as a comprehensive tool for assessing hospital performance, facilitating evidence-based evaluation, and enabling continuous monitoring of quality improvement over time. This study aimed to determine the relative importance of hospital quality indicators for constructing a composite quality index using the Analytical Hierarchy Process (AHP). The AHP method allows structured quantification of expert judgments through systematic pairwise comparisons across multiple dimensions of hospital quality. A quantitative analytical observational study with a cross-sectional design was conducted at teaching hospital as a preliminary investigation. Eight core indicators were identified from INM, IMPRS, and IKM datasets (2018–2023) using Confirmatory Composite Analysis (CCA). The AHP method was then applied to assess the relative weighting of each indicator based on expert input. AHP analysis indicated that hand-hygiene compliance and emergency cesarean-section response time were the two with the highest weight indicators, followed by compliance with clinical pathways and medical-record documentation completeness. These results emphasize that hospital quality not only formed from one element, but combination of many which are elements of safety, timeliness, critical effectiveness and process reliability. The resulting weighted hierarchy provides a structured, evidence-based foundation for developing a hospital quality composite index and offers policymakers a reliable tool to evaluate and enhance hospital performance.