Six sigma in the evaluation of quality indicators using Roche Cobas c501 biochemistry analyzer

Vu Dinh Pham

doi:10.25259/JLP_285_2025

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Original Article

ARTICLE IN PRESS

doi:

10.25259/JLP_285_2025

Six sigma in the evaluation of quality indicators using Roche Cobas c501 biochemistry analyzer

Vu Dinh Pham^1,

1Department of Laboratory, Phuc Hung General Hospital, Quang Ngai, Vietnam.

*Corresponding author: Vu Dinh Pham, Phuc Hung General Hospital, Quang Ngai, Vietnam. phamdinhvu5@gmail.com

Received: 2025-08-21, Accepted: 2025-11-07, Epub ahead of print: 2025-12-29,

Licence

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Pham VD. Six sigma in the evaluation of quality indicators using Roche Cobas C501 biochemistry analyzer. J Lab Physicians. doi: 10.25259/JLP_285_2025

Abstract

Background:

Test results are used to diagnose, detect, monitor, and treat diseases in the clinic, but the test quality management process remains uneven. Six Sigma is a useful tool for total quality management, objectively measuring quality with data through errors in the analysis process to minimize errors, and to ensure that the results are accurate and reliable.

Objectives:

To calculate Six Sigma values and some quality indicators using internal and external inspection data of biochemical tests on the Roche Cobas C501 biochemical system.

Materials and Methods:

The cross-sectional descriptive study design was conducted on the Cobas C501 testing machine of Roche Diagnostics (USA) at the Testing Department of Phuc Hung General Hospital from May to July 2024 with 18 biochemical indexes amylase, albumin, aspartate aminotransferase, alanine aminotransferase, urea (URE), creatinine, glucose, uric acid, bilirubin total, bilirubin direct, total protein, total cholesterol, triglyceride, high-density lipoprotein cholesterol, gamma-glutamine transpeptidase, calcium, creatine kinase, and serum iron by evaluating the quality of the tests using the Sigma scale, applying the Westgard rule to calculate the quality control (QC) frequency according to the sigma scale, calculating the Quality Goal Index (QGI).

Statistical analysis:

Data were entered and processed in Microsoft Excel 2016. Mean and standard deviation from internal QC were used to derive CV%; external QC was compared with target means for bias (%). Sigma metrics were calculated using Sigma = (TEa - Bias)/CV%, with TEa from CLIA. Descriptive statistics of CV%, bias%, sigma metrics, Westgard rule-based QC frequency and QGI were used to evaluate analytical performance.

Results:

Of 18, 15 (83.33%) indexes have sigma values >3, “acceptable” levels, of which 11.11% of indexes have sigma values >6, “Very good” levels, and (16.67%) have sigma values <3, “Poor” levels, calculate QC frequency and quality target index (QGI) for each index.

Conclusions:

Out of 18, 15 tests reached three sigma levels or higher at both concentration levels; three tests for URE, serum iron, and direct bilirubin need to be considered. Sigma is a useful tool in determining accuracy, efficiency, cost reduction, and quantification to help accurately evaluate test quality.

Keywords

Allowable total error

Analytical performance

Biochemical analyzer

Quality goal index

Sigma-metrics

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INTRODUCTION

Laboratory test results play a crucial role in clinical practice, supporting disease diagnosis, monitoring, and treatment decisions. Although laboratory testing accounts for a relatively small portion of total healthcare costs, its results significantly influence major clinical decisions. In Vietnam, higher-level hospitals often hesitate to rely on results provided by lower-level facilities, clinics, or health centers, potentially due to inconsistencies in laboratory quality management processes.

The laboratory testing process comprises three main phases: pre-analytical, analytical, and post-analytical. Quality assessment encompasses indicators such as the rate of defective and rejected samples (pre-analytical phase), the accuracy and precision of test results (analytical phase), and the timely reporting of clinically significant results (post-analytical phase). Accurate evaluation of analytical performance is therefore essential to minimize errors and ensure reliable test results.

Six Sigma is a widely adopted methodology for comprehensive quality management, providing an objective measure of process performance by quantifying both systematic and random errors.^[1] In clinical laboratories, quality control is implemented through internal quality control (IQC) and external quality assessment (EQA).^[2] Performance metrics, including bias and coefficient of variation (CV), are compared with total allowable error (TEa), determined based on biological variation, the Clinical Laboratory Improvement Amendments (CLIA) standards, and RiliBÄK guidelines.^[3]

Six Sigma values indicate the potential frequency of errors in laboratory processes; higher Sigma values correspond to fewer errors. Beyond performance measurement, Six Sigma guides are for process improvement and risk mitigation.^[4] It is closely linked with Westgard’s multi-rule principles, as described in the “Westgard Sigma^™ Rules,” enabling laboratories to integrate Six Sigma into routine quality management practices.^[5 ,6] The quality goal index (QGI) can further identify whether deviations are due to imprecision or inaccuracy, supporting targeted corrective actions.^[4,5]

Since its establishment, the Laboratory Department of Phuc Hung General Hospital has maintained quality assurance through IQC and EQA programs. To evaluate the effectiveness of these measures, we conducted a study applying Six Sigma methodology to selected biochemical assays. The study aimed to: Calculate Six Sigma values and related quality indicators for biochemical tests performed on the Roche Cobas C501 analyzer, using data from internal and EQAs;

Apply the Six Sigma scale to evaluate the analytical performance of 18 biochemical parameters, identifying potential areas for process improvement.

This approach not only assesses assay reliability but also provides a structured framework for laboratories in Vietnam to benchmark analytical performance against international standards and regional data.^[7-10]

MATERIALS AND METHODS

Study design and setting

The study was conducted from May to July 2024 at the Laboratory Department, Phuc Hung General Hospital, Quang Ngai Province, Vietnam. A descriptive cross-sectional study design was employed. A purposive convenience sampling method was used to select 18 biochemical parameters routinely analyzed at the laboratory department using the Roche Cobas c501 biochemistry analyzer (Roche Diagnostics, North America), commissioned in 2023.

Materials and analytical methods

IQC samples were PreciControl ClinChem (PCCC) Multi 1 and 2 (Roche Diagnostics Ltd., United Kingdom): PCCC 1 (LOT: 5649780) at a normal concentration and PCCC 2 (LOT: 59544100) at an abnormal concentration. IQC results during the study were monitored according to the laboratory’s control limits and assessed using Westgard rules. All IQC data points violating Westgard rules 1_3S, 2_2S, 4^1S, R_4S, or 10x were excluded from the study, as were data points with gross procedural errors such as using the wrong control level or improper reconstitution procedures. EQA samples, from Randox Laboratories Ltd., United Kingdom, analyzed monthly as part of the EQA program coordinated by the Centre for External Quality Control in Medical Laboratory Testing, University of Medicine and Pharmacy at Ho Chi Minh City, were included in this study. The analytical instrument used was the Roche Cobas c501 biochemistry analyzer (Roche Diagnostics, North America).

From internal QC and EQA data, the mean value, CV, and bias were calculated for each test. For each QC level, the CV was calculated from the mean value and standard deviation (SD) based on internal QC data, using: CV% = SD/Mean × 100. Bias refers to the difference between the expected result and the laboratory’s result, based on EQA data from the external program; the calculation was: Bias% = (Laboratory mean - Target mean)/Target mean × 100. Sigma metrics for the assays were calculated using the formula: Sigma = (TEa - Bias)/CV%.^[1,3] Where: TEa is the maximum permissible variation of a test result from the true value that does not affect the clinical interpretation of the result (TEa values provided by the CLIA program). Bias is the difference between the expected value and the laboratory’s result, determined from EQA data collected from January 2023 to July 2024. CV was obtained from internal QC data over 3 months. The accepted Sigma metric was determined using the lowest Sigma value among the three QC levels to assess laboratory performance. Table 1 shows the conversion between Sigma metric, defects per million opportunities, first-pass yield (in %), and quality assessment. A higher Sigma metric indicates a lower probability of errors, and vice versa. In laboratory quality control, a minimum Sigma value of 3 was considered acceptable in this study.

Table 1: Sigma metrics and performance assessment.

Sigma metric	Defects per million	Yield (%)	Assessment
1	691,462	31.0000	Unacceptable
2	308,538	69.2000	Poor
3	66,807	93.3200	Acceptable
4	6,210	99.3790	Good
5	233	99.9770	Very good
6	3.4	99.9997	Excellent

The QGI was calculated using: QGI = Bias%/(1.5 × CV%).^[1]

Interpretation: QGI <0.8 indicates a precision problem; QGI = 0.8-1.2 indicates both precision and accuracy issues; QGI > 1.2 indicates an accuracy problem.

Data collection, processing, and ethical considerations

IQC data were extracted from the laboratory’s IQC records after verification against inclusion and exclusion criteria. EQA data were obtained from the EQA result reports provided by the Centre for External Quality Control in Medical Laboratory Testing, University of Medicine and Pharmacy at Ho Chi Minh City. Other indicators, including CV%, TEa%, and bias (%), were then calculated based on the corresponding formulas and the collected data. Data coding, entry, processing, and statistical calculations were performed using Microsoft Excel 2016; the derived data were subsequently used to calculate mean values, CVs, bias, Sigma metrics, and the quality goal index (QGI).

The study protocol received approval from the hospital administration and the Laboratory Department. The research was conducted solely for scientific purposes, and only internal and external quality-control samples were used; no patient specimens were involved.

RESULTS

As shown in Table 2, the assays with Sigma metrics greater than 6 included creatine kinase (CK) at both concentration levels, and calcium (CAL) and triglycerides (TGs) at concentration level 2. The assays with Sigma metrics between 5 and 6 were total cholesterol at both concentration levels and TGs at concentration level 1. The assays with Sigma metrics between 4 and 5 included CAL at concentration level 1, alanine aminotransferase (ALT), and gamma-glutamyltransferase (GGT) at concentration level 2. The assays with Sigma metrics between 3 and 4 included Aspartate aminotransferase (AST), albumin, amylase (AMY), glucose, total protein, high-density lipoprotein cholesterol (HDL-C), creatinine, uric acid, and total bilirubin at both concentration levels, as well as ALT and GGT at concentration level 1. The assays with Sigma metrics <3 were urea (URE), serum iron, and direct bilirubin at both concentration levels.

Table 2: Total allowable error (TEa%), bias (%), coefficient of variation (CV%), and Sigma metrics. PCCC: PreciControl ClinChem, IQC: Internal quality control. Bold indicates low sigma values.

No.	Parameter	TEa (%)	IQC	CV (%)	Bias (%)	Sigma
1.	Albumin (mmol/L)	8	PCCC1	4.28	−5.91	3.25
1.	Albumin (mmol/L)	8	PCCC2	3.22	−1.99	3.10
2.	Alanine aminotransferase (U/L)	15	PCCC1	4.58	−2.44	3.81
2.	Alanine aminotransferase (U/L)	15	PCCC2	5.27	−7.29	4.23
3.	Amylase (U/L)	10	PCCC1	2.99	−0.13	3.39
3.	Amylase (U/L)	10	PCCC2	3.02	−0.14	3.36
4.	Aspartate aminotransferase (U/L)	15	PCCC1	4.15	2.43	3.03
4.	Aspartate aminotransferase (U/L)	15	PCCC2	3.02	5.78	3.05
5.	Direct bilirubin (µmol/L)	20	PCCC1	8.03	−0.11	2.50
5.	Direct bilirubin (µmol/L)	20	PCCC2	8.19	−3.18	2.83
6.	Total Bilirubin (µmol/L)	20	PCCC1	6.20	−0.40	3.29
6.	Total Bilirubin (µmol/L)	20	PCCC2	6.24	−3.51	3.77
7.	Cholesterol Total (mmol/L)	10	PCCC1	3.24	−8.57	5.73
7.	Cholesterol Total (mmol/L)	10	PCCC2	2.57	−3.07	5.09
8.	Creatine kinase (U/L)	20	PCCC1	2.56	−3.65	9.24
8.	Creatine kinase (U/L)	20	PCCC2	1.29	−9.22	22.65
9.	Creatinine (µmol/L)	10	PCCC1	4.05	−3.54	3.34
9.	Creatinine (µmol/L)	10	PCCC2	3.92	−3.32	3.40
10.	Gamma-glutamyl transpeptidase (U/L)	15	PCCC1	3.63	1.96	3.59
10.	Gamma-glutamyl transpeptidase (U/L)	15	PCCC2	3.87	−0.77	4.07
11.	Glucose (mmol/L)	8	PCCC1	2.05	0.18	3.81
11.	Glucose (mmol/L)	8	PCCC2	2.90	−2.91	3.76
12.	High-density lipoprotein cholesterol (mmol/L)	20	PCCC1	6.01	1.3	3.11
12.	High-density lipoprotein cholesterol (mmol/L)	20	PCCC2	5.79	−0.51	3.54
13.	Serum iron (µmol/L)	15	PCCC1	9.25	3.72	1.22
13.	Serum iron (µmol/L)	15	PCCC2	8.31	−2.26	2.08
14.	Calcium (mmol/L)	30	PCCC1	6.80	−3.55	4.93
14.	Calcium (mmol/L)	30	PCCC2	2.52	−0.70	12.18
15.	Total protein (g/L)	8	PCCC1	2.55	−0.43	3.31
15.	Total protein (g/L)	8	PCCC2	2.87	−1.58	3.34
16.	Triglyceride (mmol/L)	15	PCCC1	3.01	−0.77	5.24
16.	Triglyceride (mmol/L)	15	PCCC2	3.06	−4.37	6.33
17.	Uric Acid (µmol/L)	10	PCCC1	4.01	−3.27	3.31
17.	Uric Acid (µmol/L)	10	PCCC2	3.82	−1.76	3.08
18.	Urea (mmol/L)	9	PCCC1	6.63	−7.92	2.55
18.	Urea (mmol/L)	9	PCCC2	5.47	−4.79	2.52

Based on the results in Table 3, the analytical performance of the Roche Cobas c501 biochemistry analyzer, as assessed using the Sigma scale, revealed that the “Acceptable” category (Sigma 3-4) accounted for the highest proportion (72.22%), followed by the “Poor” category (Sigma < 3) at 16.67% and the “Very good” category (Sigma ≥ 6) at 11.11%. Moreover, as shown in Table 4, the Six Sigma tool can be successfully applied in the IQC program: Assays with higher Sigma levels required fewer daily QC runs and fewer James O. Westgard rules, whereas those with lower Sigma levels necessitated more intensive QC monitoring. Further investigation through the quality goal index (QGI) in Table 5 indicated that among assays with Sigma metrics <4, only AST at control level 2 exhibited a QGI >1.2 (indicating predominant inaccuracy), while all other low-Sigma assays displayed QGI values <0.8, implicating imprecision as the primary issue.

Table 3: Percentage distribution by sigma levels of the Roche Cobas C501 biochemistry analyzer.

No.	Sigma metric	Sigma classification	Number		Percentage	Sigma classification
			PCCC1 (%)	PCCC2 (%)
1.	Sigma ≥6	Very good	1 (2.78)	3 (8.33)	11.11	11.11
2.	Sigma 5–6	Acceptable	2 (5.56)	1 (2.78)	72.22	72.22
3.	Sigma 4–5		1 (2.78)	2 (5.56)
4.	Sigma 3–4		11 (30.56)	9 (25.00)
5.	Sigma <3	Poor	3 (8.333)	3 (8.333)	16.67	16.67
Total	18	18	100	100

PCCC: PreciControl ClinChem

Table 4: Application of Westgard rules corresponding to Six Sigma levels.

No.	Six Sigma level	Roche Cobas C501		QC frequency	Westgard rules
		PCCC1	PCCC2
	≥6	CK	CK, CAL, TG	Once per day or every 1,000 patient samples	1_3S
2.	5–6	TC	TG, TC	Twice per day or every 450 patient samples	1_3S, 2_2S, R_4S
3.	4–5	CAL	AST, GGT	Twice per day or every 200 patient samples	1_3S, 2_2S, R_4S, 4_1S
4.	3–4	AST, ALB, AMY, G, PRO, HDL-C, CRE, UA, BILT, ALT, GGT	AST, ALB, AMY, G, PRO, HDL-C, CRE, UA, BILT	Twice per day or every 45 patient samples	1_3S, 2_2S, R_4S, 4_1S, 8_x
5.	<3	URE, BILD, IRON	URE, BILD, IRON	Three times per day or every 10 patient samples	1_3S, 2_2S, R_4S, 4_1S, 8_x, 10_x

AMY: Amylase, ALB: Albumin, AST: Aspartate aminotransferase, ALT: Alanine Aminotransferase, URE: Urea, CRE: Creatinine, GLU: Glucose, BILT: Bilirubin total, BILD: Bilirubin direct, PRO: Protein TP, TC: Total cholesterol, TG: Triglyceride, HDL-C: High-density lipoprotein cholesterol, GGT: Gamma glutamine transpeptidase, CAL: Calcium, CK: Creatine kinase, PCCC: PreciControl ClinChem

Table 5: QGI values.

No.	Parameter	IQC	CV%	Bias%	Sigma	QGI	Assay issue
1.	Albumin (mmol/L)	PCCC1	4.28	−5.91	3.25	−0.92	Precision
1.	Albumin (mmol/L)	PCCC2	3.22	−1.99	3.10	−0.41	Precision
2.	Amylase (U/L)	PCCC1	2.99	−0.13	3.39	−0.03	Precision
2.	Amylase (U/L)	PCCC2	3.02	−0.14	3.36	−0.03	Precision
3.	Aspartate aminotransferase (U/L)	PCCC1	4.15	2.43	3.03	0.39	Precision
3.	Aspartate aminotransferase (U/L)	PCCC2	3.02	5.78	3.05	1.28	Accuracy
4	Direct Bilirubin (µmol/L)	PCCC1	8.03	−0.11	2.50	−0.01	Precision
4	Direct Bilirubin (µmol/L)	PCCC2	8.19	−3.18	2.83	−0.26	Precision
5.	Total Bilirubin (µmol/L)	PCCC1	6.20	−0.40	3.29	−0.04	Precision
5.	Total Bilirubin (µmol/L)	PCCC2	6.24	−3.51	3.77	−0.38	Precision
6.	Creatinine (µmol/L)	PCCC1	4.05	−3.54	3.34	−0.58	Precision
6.	Creatinine (µmol/L)	PCCC2	3.92	−3.32	3.40	−0.56	Precision
7.	Glucose (mmol/L)	PCCC1	2.05	0.18	3.81	0.06	Precision
7.	Glucose (mmol/L)	PCCC2	2.90	−2.91	3.76	−0.67	Precision
8.	High-density lipoprotein cholesterol (mmol/L)	PCCC1	6.01	1.3	3.11	0.14	Precision
8.	High-density lipoprotein cholesterol (mmol/L)	PCCC2	5.79	−0.51	3.54	−0.06	Precision
9.	Iron Serum (µmol/L)	PCCC1	9.25	3.72	1.22	0.27	Precision
9.	Iron Serum (µmol/L)	PCCC2	8.31	−2.26	2.08	−0.18	Precision
10.	Total Protein (g/L)	PCCC1	2.55	−0.43	3.31	−0.11	Precision
10.	Total Protein (g/L)	PCCC2	2.87	−1.58	3.34	−0.37	Precision
11.	Uric Acid (µmol/L)	PCCC1	4.01	−3.27	3.31	−0.54	Precision
11.	Uric Acid (µmol/L)	PCCC2	3.82	−1.76	3.08	−0.31	Precision
12.	Urea (mmol/L)	PCCC1	6.63	−7.92	2.55	−0.80	Precision
12.	Urea (mmol/L)	PCCC2	5.47	−4.79	2.52	−0.58	Precision

QGI: Quality goal index, PCCC: PreciControl ClinChem, IQC: Internal quality control

DISCUSSION

We collected internal and external quality control data for 18 biochemical parameters and evaluated analytical performance using Six Sigma metrics and the Quality Goal Index (QGI) on the Roche Cobas C501 analyzer. Two IQC concentration levels over a 3-month period (May–July 2024) were used to calculate Sigma values, coefficients of variation (CV%), and QGI.

Overall, 11.11% of assessed parameters achieved Sigma >6 (“world-class”), specifically CK at both IQC levels and CAL and TGs at level 2. Conversely, URE, serum iron, and direct bilirubin scored Sigma <3 (“poor”), representing 16.67% of parameters. The remaining 72.22% fell within 3–6 Sigma (“acceptable”). CK had the highest Sigma value; serum iron had the lowest. The CV range (1.29-9.25%) indicates that imprecision is a significant contributor to suboptimal Sigma values for some analytes.

Comparative regional and international data indicate heterogeneity in Sigma performance but reveal similar patterns for several analytes. Panchal et al.^[4] (India) integrated Sigma metrics with QGI and OPSpecs charts. They reported that while many analytes reached high Sigma levels after targeted QC optimization, several routine tests remained below acceptable Sigma thresholds and required parameter-specific QC adjustments.^[5] Sapna Vyakaranam et al.^[5] (Journal of Laboratory Physicians) applied a sigma-matrix approach combined with QGI and root-cause analysis in a tertiary hospital (data spanning 2022–2023):

Their findings showed most analytes in the 3-6 Sigma range and a minority <3, with corrective actions improving subsequent Sigma values - a pattern comparable to our laboratory’s distribution.^[5] Bhattacharjee et al.^[10] (Asian J Med Sci) compared two NABL-accredited laboratories and demonstrated that Sigma and QGI analyses effectively identified imprecision- or inaccuracy-driven deficiencies and guided corrective actions, although the proportion achieving Sigma ≥6 varied between laboratories.^[6] Other contemporary studies have similarly emphasized that differences in Sigma outcomes across settings frequently reflect variation in analytical platforms, QC material sources, TEa selection, reagent lots, and operational conditions.^[3,8,9]

When benchmarked to these recent reports, our proportion of world-class assays (11.11%) is lower than that reported in some Indian series (which have reported higher percentages after QC optimization) but aligns with many tertiary-care laboratories where a majority of analytes lie in the “acceptable” 3–6 Sigma interval and a minority require intervention.^[5,6] The presence of analytes with Sigma <3 in our laboratory (e.g., serum iron, URE, and direct bilirubin), mirrors findings reported regionally and underscore the need for targeted root-cause analysis: in our QGI assessment, most low-Sigma analytes showed QGI < 0.8 (pointing to inaccuracy), while AST at IQC level 2 had QGI >1.2 (indicating imprecision), thus directing different corrective strategies for different analytes.

TEa selection substantially affects the Sigma calculations. We applied the updated CLIA TEa (2024) in our analysis; several TEa thresholds became stricter (e.g., AMY was reduced from 30% to 10%; for serum iron, AST, and ALT from 25% to 15%; for HDL-C from 30% to 20%; for TGs from 25% to 15%; and for uric acid from 17% to 10%, among others), which tends to reduce computed Sigma values relative to older or biologically based TEa criteria. This stricter benchmarking, while producing lower Sigma numbers compared with some historical reports, yields a more rigorous assessment of analytical quality and aligns with contemporary practice recommendations.^[4,8]

Implications for laboratory practice are practical and immediate. Sigma metrics, combined with QGI and OPSpecs/Westgard guidance, enable parameter-specific QC planning (choice of multirule, run size, and control frequency) and prioritization of corrective actions for analytes with the greatest patient-safety risk.^[1,3,10] For analytes with imprecision-dominated defects (QGI >1.2), laboratories should review instrument maintenance, lot-to-lot reagent variation, and IQC procedure adherence; for inaccuracy-dominated defects (QGI <0.8), recalibration strategy, EQA peer-group comparisons, and method standardization should be prioritized.

Limitations of our study include constrained access to peer-group IQC data (Precicontrol), which necessitated the use of manufacturer or EQA summary values for some bias estimates; and EQA bias calculations based on nonsynchronous months, reducing longitudinal precision. Future work should include extended EQA participation, broader peer-group data sharing, and inter-laboratory comparisons to strengthen benchmarking and assess the effect of implemented corrective measures over time.

In conclusion, our application of Six Sigma metrics and QGI identified both high-performing assays and analytes requiring corrective action. Comparison with recent regional studies (2020-2024) indicates that while many laboratories reach acceptable Sigma performance, achieving world-class Sigma across all assays remains challenging. Integrating Sigma metrics, QGI, and OPSpecs into routine quality management provides a robust, data-driven pathway to targeted improvements in analytical reliability.

CONCLUSIONS

The quality assessment using the Six Sigma tool showed that 15 out of 18 tests achieved Sigma levels of three or higher at both concentration levels. The Sigma scale can be applied regularly to evaluate the performance of tests based on the available internal and external quality control data of the laboratory. The Sigma metrics for URE, serum iron, and direct bilirubin should be carefully reviewed. Six Sigma is a useful method for determining accuracy, efficiency, cost reduction, and providing a quantitative approach to accurately assess laboratory test quality.

Acknowledgments:

The authors are grateful to the participants and management of the hospital for their cooperation in carrying out this study.

Author contributions:

The author was solely responsible for the conception and design of the study, data collection, data analysis and interpretation, drafting of the manuscript, and approval of the final version for publication.

Ethical approval:

Ethical approval was waived because this study involved only retrospective evaluation of internal and external quality control data from routine laboratory operations and analytical performance metrics (Six Sigma) on the Roche Cobas C501 biochemistry analyser.

Declaration of patient consent:

Patient’s consent not required as there are no patients in this study.

Conflicts of interest:

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation:

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.

Financial support and sponsorship: Nil.

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INTRODUCTION

MATERIALS AND METHODS

Study design and setting
Materials and analytical methods
Data collection, processing, and ethical considerations

RESULTS

DISCUSSION

CONCLUSIONS

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[1] Westgard JO, Westgard SA. Basic quality management systems Madison, WI: Westgard QC; 2014. p. :171-88.
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Six sigma in the evaluation of quality indicators using Roche Cobas c501 biochemistry analyzer

Abstract

Background:

Objectives:

Materials and Methods:

Statistical analysis:

Results:

Conclusions:

Keywords

Allowable total error

Analytical performance

Biochemical analyzer

Quality goal index

Sigma-metrics

INTRODUCTION

MATERIALS AND METHODS

Study design and setting

Materials and analytical methods

Data collection, processing, and ethical considerations

RESULTS

DISCUSSION

CONCLUSIONS

Acknowledgments:

Author contributions:

Ethical approval:

Declaration of patient consent:

Conflicts of interest:

Use of artificial intelligence (AI)-assisted technology for manuscript preparation:

References

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