Effect of Internal Quality Planning using Sigma Metrics in Lean Management of a Clinical Chemistry Laboratory: An Analytical Study

1. Junior Resident, Department of Biochemistry, JIPMER, Puducherry, India. 2. Professor, Department of Biochemistry, JIPMER, Puducherry, India.

Abstract

Introduction: Across the globe, quality control systems serve as the foundation for providing accurate and precise results, and also immediate error detection. However, many laboratories adhere to uniform Quality Control (QC) rules for all parameters, which may result in unnecessary overspending. The present study aimed to establish individual control rules and determine the number of control measurements for each of the 10 parameters using Westgard EZ Rules 3 software. The cost-effectiveness and benefits of applying these new rules were evaluated, alongside the lot-to-date, lot-to-lot, and company-to-company Coefficient of Variation (CV) for quality control materials.

Aim: To assess the impact of sigma-metrics-based internal quality planning on lean management in a clinical chemistry laboratory.

Materials and Methods: This cost-effective analysis study was conducted using commercially available quality control materials. It was done in the Department of Biochemistry in the Super Specialty Block (SSB) Biochemistry laboratory at Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India, from June 2020 to June 2022. Initially, the existing practices were scored. Using Westgard EZ Rules 3 software, OPSpecs charts and power function graphs were plotted using Westgard EZ Rules 3 software, and control rules and the number of control measurements for 10 parameters (Urea, Creatinine, Calcium, Phosphorus, Magnesium, Uric acid, Aspartate Transaminase (AST), Alanine Transaminase (ALT), Alkaline Phosphatase (ALP), and Total protein) were determined. Cost-effective and cost-benefit analyses were conducted using quality cost worksheets. A comparison of lot-to-date (month to month), lot-to-lot, and company-to-company CV was performed using Statistical Package for Social Sciences (SPSS) Software version 19.0.

Results: In the present study, it was found that ALP, calcium, and magnesium followed the 13S rule, whereas the remaining 7 parameters followed the 13S/22S/R4S/41S/10X rule with two control materials. The study revealed a decrease in cost by 95.8%, 92.3%, and 81.5% for ALT, AST, and creatinine, respectively, and by 71.1%, 68.8%, 59.8%, and 54.9% for uric acid, phosphorus, total protein, and urea, respectively, if the new control rules were followed instead of the existing ones. ALP, magnesium, and calcium showed no cost difference, indicating that the current control rules were similar to the newly framed ones. Furthermore, there was no significant difference in lot-to-date (month to month), lot-to-lot, and company-to-company CV on QC rules for most parameters despite changing reagent lots.

Conclusion: In conclusion, the study demonstrated that the control rules for each of the 10 parameters (Urea, Creatinine, Calcium, phosphorus, magnesium, uric acid, AST, ALT, ALP, and total protein), as well as the comparison of QC material CV, proved to be cost-effective.

Analysis of variance, Bias, Calcium, Cost-effectiveness analysis

Quality control systems across the globe serve as the foundation for providing accurate, precise results and immediate error detection (1). Six Sigma uses a structured strategy referred to as Define, Measure, Analyse, Improve, and Control (DMAIC) to enhance process quality and minimise the defects (2). Lean comprises principles and techniques for planning, refining, and leading processes, thereby minimising waste and improving productivity (3).

A power function graph is a tool for detecting the chance of rejection versus error size for a Statistical Quality Control (SQC) procedure. In practice, values less than 0.05 or 0.01 (5% or 1%) for false rejections and more than 0.90 or 90% for error detection can be utilised. The critical systematic error (? SEcrit) denotes the error size that systematically results in a medically important error (4).

Westgard rules ensure that laboratory quality control is within the range before reporting the results. The primary objective of Westgard rule selection is to achieve 90% or above error detection and 5% or less false rejection with the assistance of the power function graph and OPSpecs chart (5).

Quality Assurance (QA) for biochemical parameters cannot be achieved solely with Internal Quality Control (IQC) and External Quality Control (EQC) as they cannot detect the exact number of defects or errors in the laboratory (6). Most laboratories follow the same QC rules to all parameters, which may not be necessary and can lead to overspending. The concept of refining the quality of reported results, with the goal of achieving zero defects, depends on a system that integrates accuracy and process improvement like the Six Sigma management methodology (7). There is a need to use Lean and Six Sigma together as appropriate tools to provide accurate and precise results in a cost-effective manner.

In the present study, individual control rules and the number of control measurements for each of the 10 parameters were established using Westgard EZ Rules 3 software. Cost reduction in the laboratory was done by applying these newly established rules in place of existing practices. A comparison was done between the effect of the lot-to-date (month-to-month) CV of Biorad QC material, a lot-to-lot CV of Biorad QC material, and company (Randox)-to-company (Biorad) CV of QC material for both normal and pathological levels.

Material and Methods

It was a cost-effective analysis study in quality management conducted using commercially available quality control materials. The study took place in the Department of Biochemistry at SSB Biochemistry Laboratory, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India, from June 2020 to June 2022. The study obtained approval from the Post Graduate Research Monitoring Committee (PGRMC) approval Institute Ethics Committee Review exemption certificate (Ref no. JIP/IEC/2020/090).

Study Procedure

First, the scoring of existing practices was completed based on a 15-step proforma. Biorad QC with lot numbers 26470 (26471 for normal level and 26472 for pathological level) and 26490 (26491 for normal level and 26492 for pathological level), as well as Randox QC with lot number 1392UN for normal level and 1174UE for pathological level, were used. A total of 12 vials from each of the aforementioned QC lots were reconstituted as per standard QC preparation guidelines and aliquoted as 250 microlitres each. The stability of reconstituted QC material in aliquots is confirmed for one week when stored at -20°C (the range for QC material storage after reconstitution is -18°C to -24°C) (8). Once all the aliquots prepared from the vial were used, the next QC vial was aliquoted and stored as mentioned above. These aliquoted QC materials were run as patient samples three times a day (morning, afternoon, and night) for 10 parameters, namely, urea, creatinine, calcium, magnesium, phosphorus, uric acid, AST, ALT, ALP, and total protein in the Beckman Coulter AU5800 autoanalyser for three months, and data were collected. In each run, one normal level and one pathological level QC material aliquot from both lots of Biorad and Randox were run.

With the available data, the CV was calculated for every 20 runs using the online Westgard CV calculator. Bias was calculated using the External Quality Assurance Scheme (EQAS) report from CMC Vellore. Total allowable Error (TEa) data were obtained from CLIA guidelines 2019 (9),(10),(11),(12). Medical decision limit data were obtained from the Westgard Website (13),(14). OPSpec chart and power function graph were plotted using Westgard EZ Rules 3 software, and control rules and the number of controls for each parameter were designed (4). Cost-effective analysis were done using quality cost worksheets (15).

Statistical Analysis

Comparison between lot-to-date (month-to-month), lot-to-lot, and company-to-company CV was conducted using SPSS Software Version 19.0. All continuous variables were checked for normality using the one-sample Kolmogorov-Smirnov test. The data were expressed as mean±Standard Deviation (SD). Comparison between two groups was done using independent samples t-test, and comparison between three groups was done using one-way Analysis of Variance (ANOVA) repeated measures.

Cost reduction percentage calculation: The steps involved in calculating the cost reduction percentage by calculating the percentage difference between the new and current QC rules using the Westgard Quality cost worksheets are shown in (Table/Fig 1) (15).

Results

The existing practice score was 21 out of 75, as determined using the proforma mentioned in [Annexure I]. The AST has the maximum CV, creatinine has the maximum Bias%, and ALP has the maximum total allowable error% as shown in (Table/Fig 2),(Table/Fig 3), while calcium has the minimum CV and Bias%, and total protein has the minimum total allowable error%. The medical decision level for all 10 parameters is mentioned in (Table/Fig 2),(Table/Fig 3).

OPSpecs chart and power-function graph, which are helpful in selecting the control rule for ALP and could be used for other parameters as well are shown in (Table/Fig 4),(Table/Fig 5).

The ALP has a maximum sigma-metric value of 6, followed by a sigma-metric value of 5 for magnesium and calcium, a value of 4 for ALT and AST, and a value of 3 for creatinine, uric acid, phosphorus, total protein, and urea as shown in (Table/Fig 6). As the sigma-metric values decrease from 6 to 3, the control rule changes from a single control rule of 1_3S to multicontrol rules of 1_3S/2_2S/R_4S/4_1S/10_X. ALP, magnesium, calcium, ALT, AST, creatinine (normal level QC), uric acid (pathological level QC), and phosphorus (pathological level QC) showed more than 90% probability of error detection, while the remaining parameters showed less than 90% error detection when the above quality control rules were applied. All 10 parameters showed less than 10% probability of false rejection when the above rules were applied.

Cost-reduction percentage, which is maximum for ALT and minimum for ALP, calcium, and magnesium is shown in (Table/Fig 7). It indicates that the current control rules that were followed were similar to the control rules to be followed for ALP, calcium, and magnesium. For the rest of the parameters, new control rules were to be followed for cost reduction.

Except for phosphorus (pathological level QC), all other parameters do not show any significant difference in the month-to-month CV of QC materials for three months as shown in (Table/Fig 8).

(Table/Fig 9) shows that except for uric acid (normal level QC) and AST (pathological level QC), there was no significant difference in the lot-to-lot CV of QC materials.

(Table/Fig 10) shows that except for magnesium (normal level QC) and AST (pathological level QC), there was no significant difference in the company-to-company CV of QC materials.

Discussion

The sigma-metric-based QC rules appear to be helpful in selecting appropriate control rules for each parameter and also in reducing the overall cost expenditure in the laboratory.

In the present study, among the 10 parameters, ALP had a sigma-metric value >6. Calcium and magnesium had sigma-metric values between 5 and 6. AST and ALT had sigma-metric values between 4 and 5. Urea, creatinine, phosphorus, uric acid, and total protein had sigma-metric values between 3 and 4. When compared with the study by Mao X et al., ALP, magnesium, and urea had similar sigma-metric values of >6, 5-6, and 3-4, respectively. AST, ALT, creatinine, uric acid, and total protein in the present study had low sigma-metric values when compared to Mao X et al., study, which reported a sigma-metric value of >6 for AST, ALT, creatinine, and uric acid, and a sigma-metric value of 5-6 for total protein. The sigma-metric value for calcium and magnesium was not calculated in Mao X et al., study (6).

The difference in the sigma-metric values might also be due to differences in the analyser, reagents, methods and environmental conditions used between this study and Mao X et al., study.

As a result, in the present study, ALP, calcium, and magnesium will follow the 1_3S rule, whereas the remaining seven parameters will follow the 1_3S/2_2S/R_4S/4_1S/10_X rule with two levels of control materials. These rules were framed with the idea of low false rejection of less than 5% and high error detection of more than 90%. Thus, the present study provides additional support to previous study findings of high sigma-metrics reducing the number of control rules and vice versa (16).

With the help of waste and rework and the external failure cost worksheet, it has been found that there would be a decrease in cost for seven parameters if the new control rules were followed instead of the existing control rules, and for the remaining three parameters, no cost reduction was noted, indicating that the current control rules were similar to the new control rules framed. Thus, the present study proves that running two levels of control five times a day for low sigma-metric QC parameters is still cost-effective and beneficial compared to running two levels of control material twice a day using waste and rework and external failure cost worksheets. Similarly, for high sigma-metric QC parameters, running two levels of QC single time a day also proves to be cost-effective.

There was no significant difference in lot-to-date (month-to-month), lot-to-lot, and company-to-company CV on QC rules for most of the parameters despite changing the reagent lot in between. As a result, the number of calibration usages can be reduced, enabling cost reduction.

Limitation(s)

Studies using different company QCs can further strengthen the present study. Studies using at least six or more QC lots can also provide sufficient evidence for the findings in the present study.

Conclusion

It is recommended that each clinical chemistry laboratory establish its own control rules using sigma-metric-based QC rules, aiming to reduce the cost. Having prior knowledge about lot-to-date (month-to-month), lot-to-lot, and company-to-company CV on QC can also reduce costs. By reducing costs and simultaneously improving the quality of test results, the present study provides an idea for managing the laboratory cost-effectively, and the reduced cost can be utilised for further improvements in the laboratory.

Acknowledgement

“To my parents, to science, to humanity”

First and foremost, I would like to express my sincere and earnest gratitude to my guide, Dr. Ramesh. R, Professor, Department of Biochemistry, JIPMER, for his continuous support of my work. I am thankful for his patience, constant motivation, freedom of thought, care and effective guidance, without which this research work would not have made it to this juncture. I could not have imagined having a better advisor and mentor for my work. Thank you, sir, for giving me the privilege of working under your excellent guidance.

I am very much thankful to my parents Mr. M. Sekar and Mrs. V. Hemalatha and my elder brother Mr. S. Jagadeesh Babu, who encouraged and stood by me throughout my life. I hope that my parents and my brother’s blessings remain the same for the rest of my life in doing good deeds.

Finally, and most importantly I would like to thank all my family, faculties, seniors, juniors and friends for their unconditional love, support, guidance and constant encouragement the whole time.

- Dr. S Rohit

DOI and Others

DOI: 10.7860/JCDR/2024/67309.19260

Date of Submission: Aug 31, 2023
Date of Peer Review: Nov 26, 2023
Date of Acceptance: Jan 23, 2024
Date of Publishing: Apr 01, 2024

AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? No
• For any images presented appropriate consent has been obtained from the subjects. No

PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Sep 01, 2023
• Manual Googling: Dec 08, 2023
• iThenticate Software: Jan 20, 2024 (4%)

ETYMOLOGY: Author Origin

EMENDATIONS: 6