Six Sigma and Quality Control

design in the LaboratorySue Chan

Chemopharm Sdn Bhd

suechan@chemopharm.com.my

Agenda

• What is Six Sigma?

• How can we use Six Sigma in the Laboratory?

• Westgard Advisor and quality control design.

2

Six Sigma Introduction

• Six Sigma or also called Sigma Metrics was developed by Motorola in 1985.

• Created to help measure and improve the manufacturing production processes.

• In 1995 it became famous when General Electric successfully implemented this methodology.

3

Six Sigma Introduction

• Six Sigma is both a metric and a methodology.

• Six Sigma as a metric is used as a scale for quality.

• It measures DPMO (Defects Per one Million Opportunity’s)

4

-6s -5s -4s -3s -2s -1s 0 1s 2s 3s 4s 5s 6s

- Tolerance

Specification

+ Tolerance

SpecificationTarget

+6 SDs

should fit

into spec

-6 SDs

should fit

into spec

Six Sigma Introduction

1.5s

shiftFew

defects

even with

1.5s shift

-6s -5s -4s -3s -2s -1s 0 1s 2s 3s 4s 5s 6s

Six Sigma Introduction

Six Sigma Introduction

7

World Class

Minimum for stable process

Sigma Metric DPM without shift DPM with 1.5s shift

1.0 317,400 697,700

2.0 45,400 308,637

2.5 12,419 158,686

3.0 2,700 66,807

3.5 465 22,750

4.0 63 6,210

4.5 6.8 1,350

5.0 0.57 233

5.5 0.038 32

6.0 0.002 3.4

Six Sigma Introduction

8

What’s good enough?

99% Good (3.8 Sigma) 99.99966% Good (6 Sigma)

20,000 lost articles of mail per hour (based on 2,000,000/hr)

7 articles lost per hour

5,000 incorrect surgical operations per

week

1.7 incorrect operations per week

2 short or long landings daily at an

airport with 200 flights/day

1 short or long landing every 5 years

2,000,000 wrong drug prescriptions

each year

680 wrong prescriptions per year

No electricity for almost 7 hours each

month

1 hour without electricity every 34

years

Six Sigma Introduction

9

Nevelainen et al. Evaluating laboratory performance with

the six sigma scale. Arch Pathol Lab Med 2000;124:516-9.

Q-Probe Quality Indicator % Error DPM Sigma

TDM timing errors 24.4 244,000 2.20

Cytology specimen adequacy 7.32 73,200 2.95

Surgical pathology specimen accessioning 3.4 34,000 3.30

PAP smear rescreening false negatives 2.4 24,000 3.45

Order accuracy 1.8 18,000 3.60

Surg path frozen sect diagnostic discordance 1.7 17,000 3.60

Duplicate test orders 1.52 15,200 3.65

Laboratory proficiency testing 0.9 9,000 3.85

Wristband errors (not banded) 0.65 6,500 4.00

Hematology specimen adequacy 0.38 3,800 4.15

Chemistry specimen adequacy 0.30 3,000 4.25

Reporting errors 0.0477 477 4.80

10

Quality Today

Analyte estimated

Cholesterol 2.9 - 3.0

Glucose 2.9 - 3.3

Calcium 2.8 - 3.0

Prothrombin Time 1.8

INR 2.4 - 3.5

Fibrinogen 1.8 - 3.2

Glycohemoglobin 1.9 - 2.6

PSA 1.2 - 1.8

Six Sigma Introduction

• Six Sigma as methodology uses the DMAIC model for process improvement.

11

provides you with information

– it does not dictate your actions.

You must decide what to do with that

information!!!

Bad Sigma ???

False rejection often lead to a troubling cycleTrouble-shooting that chases the ghosts of

false rejectsMore calls to technical supportMore complaints to the manufacturer, longer

TAT.Increased field service visitsHigher dissatisfaction with the instrumentNeither the manufacturer nor the customer

are happy.

Six Sigma is still the goal!

Moving a Three Sigma process to Six Sigma is a

significant task

Raising the bar to Seven Sigma is not necessary.

What’s a Laboratory

Must Do?

How do you improve a test?

Chemical or operating conditions???

- requires much more extensive validation and

documentation by the laboratory

Switch to a new method or a new instrument???

- time-consuming and expensive

18

Quality Planning

QualityAssessment

Quality Control

Quality Laboratory Processes

Quality Improvement

Criteria and benchmark

How to do about it

RunHow many?How often?Which one?

Making sense of data collected

Am I meeting to criteria?If not, how to improve?

Quality System

Proper Control Procedures

Minimize bad test results reduces the number of repeat tests

saves time in trouble-shooting

minimizes interruptions

delays in the delivery of test results.

ERROR

DETECTIONFALSE

REJECTION

Doctors will be happier with fast and accurate

test results.

Staff will be happier, too!

There is a huge financial impact in the

laboratory from both inadequate and

excessive quality control.

Six Sigma will help you manage your testing

processes to improve quality and productivity

and reduce costs.

Six Sigma in the Laboratory

• How can we use Six Sigma in the laboratory?

• Define the tolerance limits or quality requirements for a specific test. Specified as the Total Allowable Error (TEa).

• Measure the inaccuracy or bias and imprecision SD or CV for the specific test.

23

Source of CV

• Define your Total Allowable Error (TEa) for each analyte

Prioritization for TEa was established in 1999 at the Stockholm Conference, International Conference (IFCC, IUPAC, WHO) consensus for determining the quality specifications in the medical laboratory.

• Recommendations for choosing analytical goals:

1. Quality specifications in specific clinical situations2. Biological Variation or medical opinions3. Professional/ regulatory recommendations 4. State of the Art

Total Allowable Error (TEa)

Biological Variation table – Ricos et. al

Analyte

Biological Desirable

Variation specification

CVw CVg I(%) B(%) TE(%)

S- Albumin 3.1 4.2 1.6 1.3 3.9

U-

Albumin, concentration, first

morning 36 55 18 16.4 46.1

S- Aldosterone 29.4 40.1 14.7 12.4 36.7

U- Aldosterone, concentration 32.6 39 16.3 12.7 39.6

S- Alkaline phosphatase 6.4 24.8 3.2 6.4 11.7

S- Alkaline phosphatase, bone 6.2 35.6 3.1 9 14.1

S- Alkaline phosphatase, liver 10 27 5 7.2 15.4

U- Ammonia, output, 24h 24.7 27.3 12.4 9.2 29.6

27

Source of Bias

• Proficiency Testing program

• Interlaboratory Comparison program

Proficiency Testing Program

29

•Bias =Your mean – Peer mean

Peer mean X 100

•Example:

•Your mean = 2.15

•Peer mean = 2.80

•Bias = (2.15-2.80)/2.80 X100

= -7%

Interlaboratory Comparison Program

Interlaboratory Comparison Program

Six Sigma in the Laboratory

• Six Sigma = TEa - bias

CV

Example: TEa = 10 % CV = 2 % Bias = 0 %

Sigma = (10 – 0)/2 = 5

TEa = 10 % CV = 4 % Bias = 0 %

Sigma = (10 – 0)/4 = 2.5

TEa = 10 % CV = 2 % Bias = 2 %

Sigma = (10 – 2)/2 = 4

31

Six Sigma in the Laboratory

• What is the relationship between Six Sigma and QC?

• Critical Systematic Error (DSEcrit)

– Index used by Dr Westgard to describe size of error that needs to be detected by QC procedure

– DSEcrit = [(TEa – Bias)/CV] – 1.65

– Can relate DSEcrit to rejection characteristics of QC rules and numbers of QC measurements using known power curves

32

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.0 1.0 2.0 3.0 4.0

1.65 2.65 3.65 4.65 5.65

13s

/2of32s

/R4s

/31s

/6x

0.07 0.07 6 1

13s

/22s

/R4s

/41s

0.03 0.03 4 1

12.5s

0.04 0.04 4 1

12.5s

0.03 0.03 2 1

13s

/22s

/R4s

0.01 0.01 2 1

13s

0.00 0.00 2 1

13.5s

0.00 0.00 2 1

13s

0.00 0.00 1 1

Pfr

Ped

N R

Pro

bab

ility

fo

r R

eje

ctio

n (

P)

3 4 5 6

Desirable

Error

Detection

Desirable

False

Rejection

Systematic Error (SE, multiples of s)

Sigma Scale

Six Sigma in the Laboratory

Westgard Advisor 2.0

• Westgard Advisor meets lab need to implement and document Six Sigma and QC design.

• Westgard Advisor is integrated in Bio-Rad’sUnity Real Time 2.0 software.

• How do we get started?

Westgard Advisor 2.0 wizard

• Unity Real Time® 2 is regularly updated with these values necessary for calculating analytical goals.

• Specifications currently provided in Unity Real Time:

• User definable specification field available.

Biological Variation (Dr Ricos & Fraser) CLIA (Clinical Laboratory Improvement Amendments) RCPA (Royal College of Pathologist of Australasia) Rilibak (German Guidelines) IPH Belgium (Institute for Public Health Belgium) QMP-LS (Quality Management Program – Laboratory Services

Ontario Canada) Qualab (Committee for quality assurance – Switzerland)

Westgard Advisor 2.0 wizard

Select TEa

Westgard Advisor 2.0 wizard

• All current data and Westgard rules are displayed.

Westgard Advisor 2.0 wizard

• All Unity interlab comparaison data is available

Westgard Advisor 2.0 wizard

• Manual N selection upon practical request!!!

(always defined as multiple of levels used)

Select the date range

Select the level of the control, or select by performance

Select bias source(new user defined

setting)

Westgard Advisor 2.0 data grid

Westgard Advisor 2.0 data charts

Sigma Metrics Chart

Westgard Advisor 2.0 data charts

• OPSpecs chart

Region ofUnacceptablePerformance

Region ofmarginal

Performance

Region ofoptimal

Performance

Westgard Advisor 2.0 Advanced option

• New advanced option: will display all Westgard ruleselections and the corresponding Ped and Pfr.

• Listed by highest Detection Level and lowest Pfr, this is the used selection by the Westgard Advisor.

All selections can be displayed and/or applied in Unity Real Time.

Report example

Report example

Report example

Westgard Advisor 2.0

• Bio-Rad patented technology for automatedrule selection.

• Reduces false rejections and desensitization to false error flags.

• Selects optimal QC rules

• Integrates automatically in the Unity Real Time data managment software

• Now made even more practical for lab use.

1) Performance quantification using Six Sigma allows to tailorQC rules for every analyte

Use multirule QC when really needed

Simplify rules, after careful evaluation, when possible !

2) Sigma indexes are good indicators to monitor analyticalperformance

Quality requirements (TEa), imprecision and bias

in one equation !

Conclusion

Thank you