Sigma metrics in clinical laboratory

1. SIGMA METRICS IN CLINICAL
LABORATORY
Laboratory Management
Dr. Mohammed Laqqan
Ahmed Adel Abdallah
IUG
2021

2. INTRODUCTION
 The major role of a clinical laboratory is to produce reliable, reproducible, accurate, timely, and
correctly interpreted test results to aid clinical decision‐making; the ultimate goal must be to
assure desirable clinical outcomes.To achieve this goal, laboratories must establish and maintain
quality in all laboratory processes, while focusing on cost‐effectiveness.
 Currently, clinical laboratories must handle increasing workloads with a broader spectrum of
parameters with the same (or fewer) number of staff and must still deliver consistent results with
improved turnaround times (TATs) and with utmost quality. Laboratories influence more than
70% of critical medical decisions, such as admitting, treating, and discharging patients, but make
up <5% of all health costs. (1)
 Nevertheless, clinical laboratories are under increasing pressure to reduce costs while
maintaining or even improving quality standards.The ongoing expansion of diagnostic laboratory
testing within given budgetary constraints calls for continued efforts to keep the costs
affordable.(2)(3)
 One way to achieve this goal is to simplify the entire laboratory process and eliminate “waste,”
not just from the analytical phase but also from the pre‐ and post‐analytical phases.

3. INTRODUCTION
 Each year more than 1 million preventable injuries and 44,000–98,000 preventable deaths
occur in the United States alone. (Kohn et al., 2000)
 In a 2005 study, general practitioners from six countries (Canada,Australia, England,The
Netherlands, New Zealand, and the United States) reported medical errors in primary
care.
 For all medical errors, they reported that the percentage error rates originating from the
laboratory and diagnostic imaging were 17% and 16% in Canada and other countries,
respectively. In 16 cases of errors reported (3.7%), patients had to be hospitalized, and in
five cases (1.2%), the patients died. (Rosser, 2005)
 In a study conducted by Bonini et al. suggested that the errors reported in
laboratory medicine represent only a small proportion of the actual number of errors and
that the consequences for patient outcomes are likely worse than described.
 Clinical laboratory scientists have noted the importance of errors and have tried to reduce
errors in the pre-analytical, analytical, and post-analytical phases.

4. INTRODUCTION
 The number of steps may vary according to test types and laboratory
organization.
 We can describe nine activity steps in laboratory medicine, test selection and
ordering a
laboratory test request, collecting the sample, identification, transport the sample
to laboratory, preparation of the sample, analysis, reporting test results,
interpretation of test results, action.
 Historically in clinical laboratories, the total testing process was assumed to
consist of only three phases:
 Pre-analytical phase, Analytical phase, and Post-analytical phase.
 Further, the pre-analytical phase contain two sub-phases:
 Outside the laboratory
 Within the laboratory.

5. INTRODUCTION, SIX SIGMA
 Sigma in statistics is used to represent the standard deviation which is an indicator of the degree
of variation in a set of processes. Sigma measures how far a given process deviates from
perfection.
 Six Sigma is one of the popular quality management system tools employed for process
improvement.
 In addition to establishing a culture dedicated to continuous process improvement, Six Sigma
offers tools and techniques that reduce variance, eliminate defects and help identify the root
causes of errors, allowing organizations to create better products and services for consumers.
 While most people associate Six Sigma with manufacturing, the methodology is applicable to
every type of process in any industry.
 In all settings, organizations use Six Sigma to set up a management system that systematically
identifies errors and provides methods for eliminating them.
 People develop expertise in Six Sigma by earning belts at each level of accomplishment.
 These includeWhite Belts,Yellow Belts, Green Belts, Black Belts and Master Black Belts.

6. HOW SIX SIGMA BEGAN
 In the 19th century, German mathematician and physicistCarl Fredrich Gauss developed
the bell curve. By creating the concept of what a normal distribution looks like, the bell
curve became an early tool for finding errors and defects in a process.
 In the 1920s,American physicist, engineer and statisticianWalter Shewhart expanded on
this idea and demonstrated that “sigma imply where a process needs improvement”.
 Sir Bill Smith is the Father of Six Sigma who introduced this quality improvement
methodology to Motorola in 1986.The power of Six Sigma is in its measure of process
performance on the “Sigma Scale.”The Six Sigma Scale typically runs from 0 to 6, but a
process can actually exceed Six Sigma, if variability is sufficiently low as to decrease the
defect rate.
 The best or “world class quality” products have a level of performance of Six Sigma.
 Does it work?
 Motorola reported in 2006 that the company had saved $17 billion using Six Sigma.

7. DEFINITION
 Six Sigma is a management strategy that seeks to improve the quality of process
outputs by identifying and removing the causes of defects (errors) and minimizing
variability in manufacturing and business processes.
 It focuses on improving customer requirements understanding, business systems,
productivity, and financial performance.
 This strategy which is named for statistical measure of a variation (standard
deviation of a normal distribution) meaning the goal of setting tolerance limits for
defective products at 3.4 units per million; or in other hands, having a Confidence
Interval (CI) of correct products estimated in a probability of 99.9999998027%.

8. SIX SIGMA
 The Six Sigma methods are usually applied when the outcome of the process can
be measured.The poor outcomes are measured in terms of defects per million
and are expressed on a sigma scale. Sigma metrics is really the evolution of total
quality management (TQM) with a more quantitative assessment of process
performance and clearer goals for process improvement.
 The exact number of defects or errors done by the laboratory can be quantified
using sigma metrics in the laboratory.
 Thus, it is possible to assess the quality of laboratory testing processes and the
number of QC that is needed to ensure that the desired quality is achieved with
the help of Six Sigma principles and metrics.
 The level of sigma metrics and the corresponding defects per million test is
shown in table below.

9. Sigma metric levels
corresponding DPM
SIGMAVALUES AND CORRESPONDING
PERCENTAGE ACCURACY AND DPMO

10. SIX SIGMA APPLICATIONS INTHE CLINICAL
LABORATORY
 Reduce errors in the pre-analytical, analytical, and post-analytical phases of
testing,
 Improve safety,
 Reduce turnaround times (TATs),
 Improve accuracy of analytical tests,
 Optimize quality control (QC) rules.

11. SIX SIGMA AND QUALITY ASSURANCE
 Six Sigma had its foundation in the quality movement.
 But Six sigma encompasses QA principles, it also goes beyond them.
 The difference between them are:
 Quality assurance
 One of the goals of quality assurance is customer's satisfaction.
 QA seeks to prevent defects in existing processes.
 QA can be performed by a single department or by one individual.
 QA tends to be more narrowly focused.

12. SIX SIGMA AND QUALITY ASSURANCE
 Six Sigma
 Greater focus on the customer: In Six sigma companies customer is the most important.
 Six Sigma encourage to challenge the process. even if it is working well.
 Six Sigma projects normally cross departmental and functional boundaries.
 Six Sigma impacts everyone and every aspect of a company.

13. METHODOLOGY
 In practice, Six Sigma follows one of two sub-methodologies: DMAIC and
DMADV:
 Six Sigma DMAIC
 Define the problem and desired outcome
 Measure the ability of the process
 Analyze the data and identify the root cause of variations (defects)
 Improve or modify the process so that fewer variations (defects) are produced
 Control the process. Prevent and correct variations before they result in defects.

14. METHODOLOGY
 Six Sigma DMADV
 Define design standards that align with the product or process goals
 Measure and identify characteristics of the product or process that are critical to quality
 Analyze the data, and identify possible sources of defects
 Design changes that will eliminate the source of defects or errors
 Verify that the design will meet the requirements

15. DMAICVS. DMADV
 The DMAIC and DMADV methodologies seem similar, but they have different use
cases.
 The DMAIC methodology is designed for existing process or products that aren’t
meeting customers’ needs or performing to standards.
 While we use DMADV when a business needs to develop a product or process that
doesn’t already exist or when a product has been optimized but still falls short.

16. SIGMA METRICS
 Estimation of sigma metrics according toWestgard by the following steps
• Define goals for intended use (TEa),
•Validate method performance (CV%, bias %),
• Sigma calculation,
• SelectWestgard rule.

17. MEASUREMENTS OF ANALYTES
 In a study*, conducted in the clinical biochemistry laboratory of Karwar Institute
of Medical Sciences, Karwar, sigma metrics of 15 parameters with automated
chemistry analyzer, XL 640 were analyzed to assess the errors associated with it.
 Internal quality control (IQC) data of 15 analytes were analyzed retrospectively
over a period of 6 months from March 2015 to August 2015 with XL 640.
 Both normal (L1) and pathological (L2) levels of QC materials were assayed before
commencing reporting of patient samples every day.
 The instruments was calibrated regularly.
 The analytes assessed were glucose, urea, creatinine, uric acid, total bilirubin,
direct bilirubin, total protein, albumin, SGOT, SGPT, ALP,Total cholesterol,
triglycerides, HDL and Calcium

18. TOTAL ALLOWABLE ERROR (TEA)
 Total Allowable Error(TEa), total analytic error (TAE) or AllowableTotal Error
(ATE) defined as an analytical quality requirement that sets a limit for both the
imprecision (random error) and inaccuracy (systematic error or bias) that are
tolerable in a single measurement or single test result to insure clinical usefulness.
 Or, It is the total allowable difference from accepted reference value seen in the
deviation of single measurement from the target value.
 Calculate the analyzer’s observed total errorTE(obs)%, using measured CV% and
measured bias%, according to the formula:
TE(obs)% = 2CV% + Bias%
 Compare measuredTEobs% toTEa%.

19. TOTAL ALLOWABLE ERROR (TEA)
 IfTEobs% <TEa % (or very close to it), then the quality requirement is met and
instrument is considered suitable for measurement of that analyte.
 TEa values of various parameters were taken from Clinical laboratories
Improvement Act (CLIA) guidelines.

20. TOTAL ALLOWABLE ERROR
ANALYTE
± 0.10 up to 0.50 g/L; 20% > 10 0.50 g/L
Alpha-1-antitrypsin (α1AT)
6 mg/dL or 10%
Glucose
10%
Cholesterol
11.63%
Cholesterol, HDL
25.99%
Triglycerides
15.55%
Urea
+/- 0.3 mg/dL or +/- 15% (greater)
Creatinine
RECOMMENDED TOTAL ALLOWABLE ERROR LIMITS

21. Six Sigma Performance Goals
CLIA acceptable
values and six
sigma precision.

22. ESTIMATION OF COEFFICIENT OF
VARIATION (CV%)
 The Coefficient ofVariation (CV%) is the ratio of the standard deviation to the
mean and is expressed as a percentage.
 CV%=(SD/mean) x 100%

23. BIAS
 Bias is the systematic difference between the expected results obtained by the
laboratory’s test method and the results that would be obtained from an accepted
reference method.
 Bias was derived as follows

24. SIGMA METRICS
 Sigma metrics were calculated from CV, percentage bias and total allowable error
for the parameters by the following formula:
Σ (σ) = (TEa- bias) / CV%.

25. RESULTS

26. RESULTS

27. RESULTS

28. RESULTS

29. RESULTS

30. RESULTS

31. RESULTS

32. DISCUSSION
 In industries outside healthcare, 3 Sigma is considered the minimal acceptable
performance for a process.
 When performance falls below 3 Sigma, the process is considered to be essentially
unstable and unacceptable.
 When sigma values is <3
 A very stringent internal QC has to be followed, and the frequency of internal QC (n)
should be increased and corrective action should be taken. Upgraded analyzers and
better methodologies may help in achieving sigma values.
 For a method with sigma below 3 calls for improvement in the method as quality of the
test cannot be assured even after repeated QC runs

33. DISCUSSION
 For a 3 sigma process, use a multi rule procedure with number of QC of 6 or 8 have
to be used.
 For a 4 sigma process, use 2.5 SD control, limits or a multi rule procedure with n=4
have to be used.
 For a 5 sigma process, use 3.0 SD control limits with n=2 have to be used.
 For a 6 sigma process (or higher), use 3.5 SD control limits with N (number
of controls to be run per day)=2 have to be used.

34. DISCUSSION
 Functioning at the 3-sigma level is regarded as the minimum acceptable level of
quality.
 The six sigma idea asserts an association between the numbers of product
defects, wasted operating costs and levels of customer satisfaction.
 It can be inferred that as sigma increases, the consistency and steadiness of
the test improves, thereby reducing the operating costs.
 As sigma increases, the consistency, reliability, steadiness and overall
performance of the test improves, thereby decreasing the operating costs.
 The rule is, strive for 6 sigma, >4 sigma is ideal, <3 sigma cannot be controlled
with statistical QC protocols.

35. DISCUSSION
 So ultimately the rule is;
• Scale 6 σ or above (excellent performance), evaluate with one level of
QC per day (alternating levels between days) and 1:3.5s rule.
• 4 σ - 6 σ (good/acceptable performance), evaluate with two levels of
control once daily and 1:2.5 s rule
• 3 σ - 4 σ – (poor performance), use two levels of controls twice daily.
• scale <3 σ – (problem analyte), root cause analysis should be
performed; method performance must be improved before the method
can be routinely used.

36. REFERENCE
 Adiga, A Preethika, K Swathi - Al Am een J Med Sci, 2015. Sigma metrics in clinical chemistry
laboratory-A guide to quality control, US Elder.
 B. L. (2008). Six Sigma in the Microbiology Laboratory. Clinical Microbiology Newsletter, 30(19),
143–147. doi:10.1016/j.clinmicnews.2008.09.001
 SWestgard, QCWestgard - Abbott Laboratories MS-09, 2009, Six Sigma metric analysis for
analytical testing processes.
 Ahmed Naseer Kaftan1, Ann KhazaalYaseen, Zina Hasan, October 2017 International Journal of
Research in Medical Sciences 5(11):4690, Assessment of sigma metrics results of serum glucose
and lipid profile tested by automated chemistry analyzer in medical city hospitals in Iraq.
 Westgard JO, Klee GG. Quality management. In: Burtis CA, Ashwood ER, Bruns DE, editors.
Tietz textbook of clinical chemistry and molecular diagnostics. St Louis, MO: Elsevier Saunders
Inc., 2006:485–529.
 RosserW, Dovey S, Bordman R,White D, Crighton E, Drummond N. Medical errors in primary
care. Can Fam Physician 2005;51:386–7

37. THE E N D
Thank you.