Quantification of Pre-analytical Quality Indicators in a Clinical Laboratory and Formulating the Lean Six Sigma DMAIC Strategy

Introduction : Quality control of the laboratory has gained increased importance in the present years. 70 % of the errors in the clinical laboratory occur in the pre-analytical phase. With various guidelines to gauge the quality of the laboratory, Six Sigma Metrics remains by far the most difficult benchmark that a laboratory can achieve. We aimed to quantify the performance of the quality indicators of the routine clinical Biochemistry laboratory in the pre-analytical phase in the form of sigma metrics and devise measures and identify steps to decrease the percentage of errors by defining the DMAIC approach. Materials and Methods : One year retrospective data was collected from January, 2020 to December, 2020 from the data entry register and pre-analytical variables were quantified. Defects Per Million and sigma metric were calculated for each pre-analytical indicator. DMAIC approach was applied and post intervention sigma scores for the month of Jananuary, 2021, February, 2021 and March, 2021 were calculated. Results : Postinterventional analysis was done on a month-to-month basis to monitor the trend and also to ensure corrective action can be taken without delay. Out of 5 quality indicators which were quantified, the pre versus post sigma scores (March’21) are as follows: missing location of the patient (Sigma 4 versus 3.6), missing registration number (Sigma 3.7 versus 4.3) and both registration number and location missing (Sigma 3.6 versus 4.0), Homolysed sample (4.2 versus 4.6), insufficient sample volume (sigma 3.9 versus 4.7). Encouraging results in the form of improved Sigma scores were seen in four of the quality indicators except for the fact that the patient location were still missing in the forms and hence warrants continuous monitoring.

The application of sigma metrics in the laboratory to assess quality control processes in South Africa

Objective: This study aimed to determine the sigma metrics of analytes when using different total allowable error guidelines.Methods: A retrospective analysis was performed on 19 general chemistry analytes at Charlotte Maxeke Johannesburg Academic Hospital in South Africa between January 2017 and December 2017. Sigma metrics were calculated on two identical analysers, using internal quality control data and total allowable error guidelines from the Ricos biological variation database and three alternative sources (the Royal College of Pathologists of Australasia, the Clinical Laboratory Improvements Amendment, and the European Federation of Clinical Chemistry and Laboratory Medicine). Results: The sigma performance was similar on both analysers but varied based on the guideline used, with the Clinical Laboratory Improvements Amendment guidelines resulting in the best sigma metrics (53% of analytes on one analyser and 46% on the other had acceptable sigma metrics) and the Royal College of Pathologists of Australia guidelines being the most stringent (21% and 23%). Sodium and chloride performed poorly across all guidelines (sigma < 3). There were also month-to-month variations that may result in acceptable sigma despite poor performance during certain months.Conclusion: The sigma varies greatly depending on the total allowable error, but could be a valuable tool to save time and decrease costs in high-volume laboratories. Sigma metrics calculations need to be standardised

Application of six sigma metrics and method decision charts in improvising clinical Chemistry laboratory performance enhancement

Background: Six sigma is a powerful tool which can be used by laboratories for assessing the method quality, optimizing Quality Control (QC) procedure, change the number of rules applied, and frequency of controls run .The aim of this study was to quantify the defects or errors in the analytical phase of laboratory testing by sigma metrics and then represent the sigma value in Method Decision Chart.Methods: A retrospective study was conducted in a tertiary care hospital in Bhubaneswar, India. The clinical chemistry laboratory has been NABL accredited for the past 5 years and strictly quality checked. Internal and external quality control data was collected for a period of six months from January - June 2018 for 20 biochemical analytes. Sigma metrics for each parameter was calculated and plotted on method decision chart.Results: The sigma metrics for level 2 indicated that 6 out of the 20 analytes qualified Six Sigma quality performance. Of these seven analytes failed to meet minimum sigma quality performance with metrics less than three and another seven analytes performance with sigma metrics was between three and six. For level 3, the data collected indicated that seven out of 20 analytes qualified Six Sigma quality performance, six analytes had sigma metrics less than 3 and seven analytes had sigma metrics between 3 and 6.Conclusion: In our study Sigma value was highest for amylase and lowest for potassium. Use of alternative methods and/ or change of reagents can be done for potassium to bring the sigma value within an acceptable range.

Application Comparison of Two Source of Allowable Total Errors inσMetrics Assessing the Analytical Quality and Selecting Quality Control Procedures for Automated Clinical Chemistry / 现代检验医学杂志

Objective To evaluate the difference of two sources of allowable total errors provided by National Health Industry Standard (WS/T 403-2012,analytical quality specification for routine analytes in clinical biochemistry)and National Stand-ard (GB/T 20470-2006,requirements of external quality assessment for clinical laboratories)in assessing the analytical qual-ity byσmetrics,and selecting quality control procedures using operational process specifications graphs.Methods Selected one of the laboratories participating in the internal quality control activity of routine chemistry of February,2014 and the first time external quality assessment activity of routine chemistry in 2014 organized by National Center for Clinical Labora-tories for its coefficient of variation and the bias of nineteen clinical chemistry tests.With the CV% and Bia%,σmetrics of controls at two analyte concentrations were calculated using two different allowable total errors targets (National Health In-dustry Standard (WS/T 403-2012)and National Standard (GB/T 20470-2006).Could obtain a operational process specifica-tions graph by which Could select quality control procedures using the Quality control computer simulat software developed by National Center for Clinical Laboratories and the company zhongchuangyida.Results The σ metrics under National Health Industry Standard (WS/T 403-2012)were from 0 to 7.Most of the values (86% and 76.2%)under National Stand-ard (GB/T 20470-2006)were from 3 to 15.On the normalized method decision chart,the assay quality using the allowable total errors targets of National Standard (GB/T 20470-2006)was at least one hierarchy more than one using National Health Industry Standard (WS/T 403-2012).The quality control rules under National Health Industry Standard (WS/T 403-2012)were obviously more strict than that under National Standard (GB/T 20470-2006).Among the control procedures using National Health Industry Standard (WS/T 403-2012),multirule (n=4):ALB,ALP,Ca,Cl,TC,Crea,Glu,LDH,K, Na,TP,TG and Urea;13s (n=4):Mg;12.5s (n=2):CK,AMY ang Fe;13s (n=2):TBIL;13.5s (n=2):ALT,AST and UA.Conclusion The allowable total errors provided by National Health Industry Standard (WS/T 403-2012)are more stringent than that from National Standard (GB/T 20470-2006).So Laboratories need to improve the analytical quality of their tests furthermore.

Evaluation of the Performance of a Micromethod for Measuring Urinary Iodine by Using Six Sigma Quality Metrics

BACKGROUND: The urinary iodine micromethod (UIMM) is a modification of the conventional method and its performance needs evaluation. METHODS: UIMM performance was evaluated using the method validation and 2008 Iodine Deficiency Disorders survey data obtained from four urinary iodine (UI) laboratories. Method acceptability tests and Sigma quality metrics were determined using total allowable errors (TEas) set by two external quality assurance (EQA) providers. RESULTS: UIMM obeyed various method acceptability test criteria with some discrepancies at low concentrations. Method validation data calculated against the UI Quality Program (TUIQP) TEas showed that the Sigma metrics were at 2.75, 1.80, and 3.80 for 51+/-15.50 microg/L, 108+/-32.40 microg/L, and 149+/-38.60 microg/L UI, respectively. External quality control (EQC) data showed that the performance of the laboratories was within Sigma metrics of 0.85-1.12, 1.57-4.36, and 1.46-4.98 at 46.91+/-7.05 microg/L, 135.14+/-13.53 microg/L, and 238.58+/-17.90 microg/L, respectively. No laboratory showed a calculated total error (TEcalc)