Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance

Background

Laboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases.

Methods

We evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis.

Results

Our study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs.

Conclusion

Laboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.

A comparative analysis of Sigma metrics using conventional and alternative formulas

BACKGROUND AND AIM

The Six Sigma approach, employing Sigma Metrics (SM), is commonly used to evaluate analytical performance in clinical laboratories. However, there is ongoing debate regarding the suitability of the conventional SM formula, which incorporates total allowable error (TEa) and bias. To address this, an alternative formula based on within-subject biological variation (CVI) as the tolerance range (TR) has been proposed. The study aimed to calculate and compare SM values using both formulas.

MATERIAL AND METHODS

Twenty clinical chemistry parameters were evaluated, and SM values were calculated using conventional formula with two TEa goals and the alternative formula. Intermediate precision (CVA%) values were obtained from internal quality control data, while bias values were derived from external quality assessment reports.

RESULTS

The results showed that using the conventional formula, 11 SM values based on CLIA TEa goals and 21 SM values based on BV TEa goals were deemed unacceptable (SM < 3). Additionally, 22 SM values calculated using the alternative formula were below 3.

CONCLUSION

The choice of TR had a substantial impact on the assessed analytical performance. Laboratories should carefully consider the appropriateness of each approach based on their specific quality objectives, analyte characteristics, and laboratory operations.

Six Sigma can significantly reduce costs of poor quality of the surgical instruments sterilization process and improve surgeon and operating room personnel satisfaction

Operating room (OR) management is a complex multidimensional activity combining clinical and managerial aspects. This longitudinal observational study aimed to assess the impact of Six-Sigma methodology to optimize surgical instrument sterilization processes. The project was conducted at the operating theatre of our tertiary regional hospital during the period from July 2021 to December 2022. The project was based on the surgical instrument supply chain analysis. We applied the Six Sigma lean methodology by conducting workshops and practical exercises and by improving the surgical instrument process chain, as well as checking stakeholders' satisfaction. The primary outcome was the analysis of Sigma improvement. Through this supply chain passed 314,552 instruments in 2022 and 22 OR processes were regularly assessed. The initial Sigma value was 4.79 ± 1.02σ, and the final one was 5.04 ± 0.85σ (SMD 0.60, 95%CI 0.16-1.04, p = 0.010). The observed improvement was estimated in approximately $19,729 of cost savings. Regarding personnel satisfaction, 150 questionnaires were answered, and the overall score improved from 6.6 ± 2.2 pts to 7.0 ± 1.9 pts (p = 0.013). In our experience the application of the Lean Six Sigma methodology to the process of handling the surgical instruments from/to the OR was cost-effective, significantly decreased the costs of poor quality and increased internal stakeholder satisfaction.

Use of a Lean Six Sigma approach to investigate excessive quality control (QC) material use and resulting costs

PURPOSE

The Eastern Health Clinical Biochemistry Laboratories cater to the province of Newfoundland and Labrador. Over the last ten years, a significant increase in annual expenses on quality control material and calibrator purchases was observed. Two major Clinical Chemistry Laboratories at the Health Sciences Centre (HSC) and St. Clare's Mercy Hospital (STC), St. John's, work as referral centers for the province. The study's design was based on the Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process and involved tests performed on ten automated Abbott Clinical Chemistry (CC) and Immunoassay (IA) analyzers. The cost of purchasing the QC material from Bio-Rad and Randox had increased due to defective QC and analyzer test assignment process design. The processes were modified. An Individualized Quality Control Plan (IQCP) was developed.

RESULTS

Modification in quality control processes helped in bringing down the cost and usage of both QC and calibrators. The cost and usage of individual control material were reduced by 25 to 52% depending on the type of quality control. Total annual expenditure on the purchase of different QC materials before modification was estimated as CAD 346,395(2019) which was reduced to CAD 255,267 with annual savings of 91,128 CAD (26%) after modification (2020). The average usage reduction for various calibrators was 40% with the highest reduction in the use of urine calibrators. The annual cost of calibrators was reduced from CAD 30,568.42 (2019-20) to CAD 17,517 (2020-21) with the saving of approximately 13,051 Canadian dollars (43 %) for the laboratory.

CONCLUSIONS

There is a constant compulsion in every industry to manage costs. Implementation of Lean and Six Sigma methodology in removing Muda of high costs in a Clinical Chemistry Laboratory is the most warranted strategy in developing a cost-effective laboratory framework.

Evaluation of preanalytical and postanalytical phases in clinical biochemistry laboratory according to IFCC laboratory errors and patient safety specifications

Introduction

The aim of the study was to determine the current state of laboratory's extra-analytical phase performance by calculating preanalytical and postanalytical phase quality indicators (QIs) and sigma values and to compare obtained data according to desired quality specifications and sigma values reported by The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group - Laboratory errors and Patient Safety.

Materials and methods

Preanalytical and postanalytical phase data were obtained through laboratory information system. Rejected samples in preanalytical phase were grouped according to reasons for rejection and frequencies were calculated both monthly and for 2019. Sigma values were calculated according to "short term sigma" table.

Results

The number of rejected samples in laboratory was 643 out of 191,831 in 2019. Total preanalytical phase rejection frequency was 0.22%. According to the reasons for rejection, QIs and sigma values were: "Samples with excessive transportation time": 0.0036 and 5.47; "Samples collected in wrong container" 0.02 and 5.11. In December, QIs and sigma values were: "Samples with excessive transportation time": 0.01 and 5.34; "Samples collected in wrong container": 0.03 and 4.98. The postanalytical QIs and sigma values were: "Reports delivered outside the specified time": 0.34 and 4.21; "Turn around time of potassium": 56 minute and 3.84, respectively. There were no errors in "Critical values of inpatients and outpatients notified after a consensually agreed time".

Conclusions

Extra-analytical phase was evaluated by comparing it with the latest quality specifications and sigma values which will contribute to improving the quality of laboratory medicine.

Evaluation of the clinical chemistry tests analytical performance with Sigma Metric by using different quality specifications - Comparison of analyser actual performance with manufacturer data

INTRODUCTION

The interest in quality management tools/methodologies is gradually increasing to ensure quality and accurate results in line with international standards in clinical laboratories. Six Sigma stands apart from other methodologies with its total quality management system approach. However, the lack of standardization in tolerance limits restricts the advantages for the process. Our study aimed both to evaluate the applicability of analytical quality goals with Roche Cobas c 702 analyser and to determine achievable goals specific to the analyser used.

MATERIALS AND METHODS

The study examined under two main headings as Sigmalaboratory and Sigmaanalyser. Sigmalaboratory was calculated using internal and external quality control data by using Roche Cobas c 702 analyser for 21 routine biochemistry parameters and, Sigmaanalyser calculation was based on the manufacturer data presented in the package inserts of the reagents used in our laboratory during the study. Sigma values were calculated with the six sigma formula.

RESULTS

Considering the total number of targets achieved, Sigmaanalyser performed best by meeting all CLIA goals, while Sigmalaboratory showed the lowest performance relative to biological variation (BV) desirable goals.

CONCLUSIONS

The balance between the applicability and analytical assurance of "goal-setting models" should be well established. Even if the package insert data provided by the manufacturer were used in our study, it was observed that almost a quarter of the evaluated analytes failed to achieve even "acceptable" level performance according to BV-based goals. Therefore, "state-of-the-art" goals for the Six Sigma methodology are considered to be more reasonable, achievable, and compatible with today's technologies.

Comparison of test performance of biochemical parameters in semiautomatic method and fully automatic analyzer method

Background

The primary health-care center (PHC) and community health center (CHC) are not well equipped with laboratory services. Semiauto analyzer-based reporting could be an effective modality, provided that the performance standard is comparable to that of the fully automatic analyzer. So, the objective of this study was to analyze the test results of biochemical parameters in semiauto and fully automatic analyzer and to compare the quality performance.

Materials and Methods

One hundred forty-nine patients undergoing routine biochemical investigations in the department laboratory were enrolled in this study. Two millimeter of venous blood was collected from all the participants and processed for urea, cholesterol, triglyceride (TG), serum glutamate-oxaloacetate transaminase (SGOT) (aspartate aminotransferase), and serum glutamate-pyruvate transaminase (SGPT) (alanine aminotransferase) by using standard kits (ERBA) in semiauto analyzer (Transasia Erba Chem5X by Calbiotech Inc. USA, semiautomated clinical chemistry analyzer) and the fully automatic analyzer (Cobas Integra 400 Roche, Germany) method.

Results

There was high variability in the distribution of urea, TG, SGOT, and SGPT values in both measurement methods, whereas cholesterol data followed a normal distribution (skewness: 1.522, 1.037; kurtosis: 2.373, 0.693 in semiauto and automated methods, respectively). A significant positive correlation between both the methods of assessment was observed in urea, cholesterol, TGs, SGOT, and SGPT. The mean difference for urea was -9.85 ± 23.997 (LOA: 37.189, -56.88), whereas it was highest for TG -24.34 ± 38.513 (LOA: 51.144, -99.829), suggesting that both methods can measure urea with less difference in absolute values, whereas for TG the measurement values are highly variable.

Conclusion

The test performance of biochemical parameters such as urea, total cholesterol, TGs, SGOT, and SGPT taken by semiauto analyzer and fully automatic analyzer method of assessment were highly related and comparable.