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Six Sigma OrientationSix Sigma OrientationPresented By:Presented By:

Joseph DuhigJoseph Duhig

University Medical Center Alliance / Methodist University Medical Center Alliance / Methodist HealthcareHealthcare

November 21, 2003November 21, 2003

The Century of Quality

“We are headed into the next century which will focus on quality… we are leaving one that has been focused on productivity”

Dr. Joseph M. Juran

SIX SIGMASIX SIGMA Sigma, , is a letter in the Greek alphabet. It is used as a symbol to

denote the standard deviation of a process (standard deviation is a measure of variation).

A process with “six sigma” capability means having six standard deviations between the process mean and either specification limit. Essentially, process variation is reduced so that no more than 3.4 parts per million fall outside the specification limits. Hence, as a metric, the higher the number of sigma’s, the better.

The “Six Sigma” term is also used to refer to a:

--philosophy--goal--methodology

to drive out waste, and improve the quality, cost and time performance of any business.

2

3

4

56

308,537

66,807

6,210

SigmaSigma Defects per Million Opportunities

Defects per Million Opportunities

2333.4 .

33 to 6 to 620,000 Times Improvement... A True Quantum Leap20,000 Times Improvement... A True Quantum Leap

What is Six Sigma?What is Six Sigma?

(99.99966% good)

(99.98% good)

(99.4% good)

(93.3% good)

(69.1% good)

Six Sigma BenchmarksSix Sigma Benchmarks

1,000,000

100,000

10,000

1,000

100

3 4 5 6 72

Sigma (Short Term) Scale of Measure

Restaurant Bills

Doctor Prescription Writing

Domestic AirlineFatality Rate(0.43 PPM)

IRS Tax Advice(phone in)

Airline Baggage Handling

AverageAverageCompanyCompany

Best-in-ClassBest-in-Class

1

10

1

De

fect

s p

er M

illio

n

Getting To Six Sigma - Some ExamplesGetting To Six Sigma - Some Examples

Six Sigma99.99966% Good

Six Sigma99.99966% Good

• 20,000 lost articles of mail per hour

• Unsafe drinking water for almost 15 minutes each day

• 5,000 incorrect surgical operations per week

• Two short or long landings at most major airports each day

• 200,000 wrong drug prescriptions each year

• Seven articles lost per hour

• One unsafe minute everyseven months

• 1.7 incorrect operations per week

• One short or long landing every five years

• 68 wrong prescriptions per year

3.8 Sigma99% Good3.8 Sigma99% Good

THE CENTURY OF QUALITYTHE CENTURY OF QUALITY


Dr. Joseph M. Juran

WHATWHAT IS SIX SIGMA QUALITY?IS SIX SIGMA QUALITY?

Quality

ProductFeatures

Freedom fromDeficiencies

That Customers Want

Design for Six Sigma

At Six Sigma Levels

Improve to Six Sigma

METHODOLOGYMETHODOLOGYDEFINE Identify, prioritize, and

select the right project(s)

MEASURE Identify key product characteristics & process parameters, understand processes, and measure performance

ANALYZE Identify the key (causative)process determinants

IMPROVE Establish prediction modeland optimize performance

CONTROL Hold the gains

INPUT

Project

MissionStatement

DefineDefine

Define customers & CTQ’s

•Prioritized list of customers/segments•Prioritized list of CTQ’s

Define process to be improved

•High level Process Map

Define Project Charter

•Project Charter

MeasureMeasure

Establish Project Y’s

•Performance Measurement Matrix

Identify possible X’s

•Detailed Process Map, C&E Diagram, FMEA

Plan Data Collection

•Data Collection Plan

Validate Measurement System

•Gage R&R, Discrete Data Measure Analysis

Determine Process Capability•Baseline Six Sigma values

AnalyzeAnalyze ImproveImprove ControlControl

Develop and test hypotheses on the sources variation and

cause-effect relationships

•Stated theory (s)•Hypothesis testing results

Develop the list of vital few causes of process

performance

•List of “vital few” variations that account for the majority of variation in performance•Quantified $ Opportunity

•List of possible solutions to test or operating parameters for experimentation

Generate Solution Alternatives

•List of possible risks evaluated for level of seriousness and corresponding abatement actions as needed.

Assess Risk

•Results of DOE and/or pilot and/or simulation

Test Solution Alternatives. Select Solution(s) to optimize

performance

•SPC charts in place•Feedback mechanisms and Mistake Proofing devices implemented

Design and implement sustainable feedback

mechanisms and methods to achieve self control for dominant variables.

•Updated Standard Operating Procedures (SOP), Process Maps, FMEA•Preventative Maintenance Plans•Personnel trained

Control Plans and Documentation.

•Final project report•Audit plan

Document Project work. Close Project

Module

•Deliverables

•Possible Tools

•VOC Continuum, Surveys, •Interviews

•List of Possible Xs

•List of Project Ys

•Reliable Measurement System

•ANOVA, tests for equal variance, regression, t-tests, tests for proportions, contingency tables, non-parametric tests, Detailed Process Map, C&E Diagram, FMEA, Pareto

•Designed Experiments, Pilots, Simulations

SIX SIGMA TOOLBOXSIX SIGMA TOOLBOXAnalysis of Variance (ANOVA)Box Plots BrainstormingCause-effect Diagrams Correlation & RegressionDesign Of ExperimentsEvolutionary Operation (EVOP)FMECA Graphs and ChartsHistogramsHypothesis TestingLean Manufacturing (Lean Enterprise)Measurement System AnalysisMistake ProofingPareto AnalysisProcess Capability StudiesProcess Control PlansProcess Flow DiagramsQuality Function DeploymentResponse Surface MethodsScatter DiagramsStandard Operating Procedures (SOPs)Statistical Process ControlStratification

Why We Need Six Why We Need Six Sigma in HealthcareSigma in Healthcare

Presented By:Presented By:

Joseph DuhigJoseph Duhig

University Medical Center Alliance / Methodist University Medical Center Alliance / Methodist HealthcareHealthcare

November 21, 2003November 21, 2003

The Century of Quality


Dr. Joseph M. Juran

GOOD NEWSGOOD NEWS Incredible Advances in Medicine 2 Million Articles/20,000 Journals/Year Applying this knowledge is like:

“Trying to drink water from a fire hose”

BAD NEWSBAD NEWSThe average time from discovery of knowledge until thatknowledge is in wide-spread use is over 17 years

The IOM RoundtableThe IOM Roundtable

“…Serious and widespread quality problems exist throughout American medicine. These problems… occur in small and large communities alike, in all parts of the country, and with approximately equal frequency in managed care and fee-for-service systems of care. Very large numbers of Americans are harmed as a result….”

Source: 2002 Institute for Healthcare Improvement

The call to action...

What is Wrong??What is Wrong?? OVERUSE (of procedures, medications, visits

that cannot help)

UNDERUSE (of procedures, medications, visits that can help)

MISUSE (errors of execution)


Examples of OVERUSEExamples of OVERUSE 30% of children receive excessive antibiotics for

ear infections

20% to 50% of many surgical operations are unnecessary

50% of X-rays in back pain patients are unnecessary


Examples of UNDERUSEExamples of UNDERUSE 50% of elderly fail to receive pneumococcal vaccine

50% of heart attack victims fail to receive beta-blockers

27% of high blood pressure is adequately treated


Examples of MISUSEExamples of MISUSE

7% of hospital patients experience a serious medication error

44,000-98,000 Americans die in hospitals each year due to injuries in care


What the IOM Said….What the IOM Said…. The patient safety problem is large.

It (usually) isn’t the fault of health care workers.

Most patient injuries are due to system failures.


The Situation – Health Care CostsThe Situation – Health Care Costs

0

2

4

6

8

10

12

141

99

1

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

Large Firms

All Firms

How Hazardous is Health Care?How Hazardous is Health Care?((Leape)Leape)

1

10

100

1000

10000

100000

1 10 100 1000 10,000 100,000 1,000,000 10,000,000

Tot

al l

ives

lost

pe

r ye

ar

DANGEROUS(>1/1000)

REGULATED ULTRA-SAFE(<1/100K)

HealthcareDriving

Scheduled Airlines

CharteredFlights

ChemicalManufacturing

MountainClimbing

BungeeJumping

European Railroads Nuclear Power

Number of encounters for each fatalitySource: 2002 Institute of Healthcare Improvement

Core ConclusionsCore Conclusions

There are serious problems in quality and safety.--Between the health care we have and the care we

could have lies not just a gap but a chasm.

The problems come from poor systems…not bad people--In its current form, habits, and environment, American

health care is incapable of providing the public with the quality health care it expects and deserves.

We can fix it…but it will require changes.


““The First Law of Improvement”The First Law of Improvement”

Every system is perfectly designed to achieve exactly the results it gets


Quality is a system property

Why Six Sigma?Why Six Sigma?

Safe Timely Efficient Effective Equitable Patient-centered

Variation is the Key:Six Sigma is all about understanding variation in providing

care that is:

How is Six Sigma different from traditional How is Six Sigma different from traditional Performance Improvement ApproachesPerformance Improvement Approaches

Strategically Deployed Financially Focused Trained Professionals vs. Good Intentioned

Amateurs Statistically Based Y = f(x) Project Management is Built-in Measurement System is Validated Focus on Mistake Proofing – Failure Modes and

Effects Analysis (FMEA)

The Business Case – Doing Well by Doing GoodThe Business Case – Doing Well by Doing Good

Six Sigma Impact on Net Income

Six Sigma Results

Discounted FFS

Per Case Per Diem Shared Risk

Decreased cost/unit

Decreased # units/case

Decreased LOS

Decreased # of cases

PROJECT FOCUSPROJECT FOCUS

Process Problems and

Symptoms Process outputs Response variable, Y

Independent variables, Xi

Process inputs The Vital Few determinants Causes Mathematical relationship

Y

X’s

Measure

Analyze

Improve

Control

Pro

cess

Cha

ract

eriz

atio

nP

roce

ss

Opt

imiz

atio

n

Goal: Y = f ( x )

Define The right project(s), the right team(s)

PROCESS CONTEXT FOR PROCESS CONTEXT FOR MEASUREMENTMEASUREMENT

Y = f(X1, X2,... , Xn)

MeasuresMeasuresMeasuresMeasures

PPSS II OO CC

ProcessMap

Suppliers Inputs Process Outputs Customers

CTQs



AHRQ Medicare SMR vs. Standardised Charge, AHRQ Medicare SMR vs. Standardised Charge, 1997 1997 (Random Sample 250 Hospitals Plotted)(Random Sample 250 Hospitals Plotted)

0

20

40

60

80

100

120

140

160

180

0 5000 10000 15000 20000 25000


$ 3,922$ 4,439$ 4,940$ 5,444$ 6,304

Per-capitaMedicare Spending1996 2000

The cohorts had The cohorts had similarsimilar baseline baseline health across quintileshealth across quintilesBut were But were treated differentlytreated differently. .

Ratio: High to Low: 1.61 1.58

$ 5,229$ 5.692$ 6,069$ 6,614$ 8,283

Glucose Levels of Diabetic Cardiac Surgery PatientsGlucose Levels of Diabetic Cardiac Surgery Patients

0 100 200 300 400

LSL USL

Process Capability Analysis for Blood Sugar

USL

Target

LSL

Mean

Sample N

StDev (Within)

StDev (Overall)

Cp

CPU

CPL

Cpk

Cpm

Pp

PPU

PPL

Ppk

% < LSL

% > USL

% Total

% < LSL

% > USL

% Total

% < LSL

% > USL

% Total

150.000

*

80.000

193.386

329

30.8392

55.9094

0.38

-0.47

1.23

-0.47

*

0.21

-0.26

0.68

-0.26

0.30

76.60

76.90

0.01

92.03

92.04

2.13

78.11

80.24

Process Data

Potential (Within) Capability

Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance

Within

Overall

OR First Case Start TimeOR First Case Start Time

0 10 20 30 40 50 60 70 80

USLUSL

Process Capability Analysis for Case Rolled

USL

Target

LSL

Mean

Sample N

StDev (Within)

StDev (Ov erall)

Cp

CPU

CPL

Cpk

Cpm

Pp

PPU

PPL

Ppk

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

30.0000

*

*

37.1050

238

8.4564

10.0149

*

-0.28

*

-0.28

*

*

-0.24

*

-0.24

*

710084.03

710084.03

*

799601.67

799601.67

*

760977.44

760977.44

Process Data

Potential (Within) Capability

Ov erall Capability Observ ed Perf ormance Exp. "Within" Perf ormance Exp. "Ov erall" Perf ormance

Within

Overall

SOURCES OF VARIATIONSOURCES OF VARIATION

People

ProcessProcess

Place Procedure

Provisions Measurement Patrons

“Y”

5 P’s + 1 M

Measurements

Commonor

Special?

CommonCausesMEASURE

ANALYZE

Investigate all of the variation

Develop solutions forthe “vital few” process

and input XsIMPROVE

Develop solutions forspecial causes and

implement asappropriate

IMPROVE

SpecialCausesMEASURE

COMMON vs. SPECIAL CAUSESCOMMON vs. SPECIAL CAUSES

CONTROL

Sustain The Improvements

Investigate specific data points

ANALYZE

Measurements

Two Types Of Mistakes

How you treat variation . . .

Common Causes Special Causes

Common

Causes

Special

Causes

Mistake 1Tampering

(increases variation)

Focus on fundamentalprocess change

Mistake 2

Underreacting(missed prevention)

Focus on investigatingspecial causes

What the

variation

really is...

COMMON vs. SPECIAL CAUSESCOMMON vs. SPECIAL CAUSES

Suppose we say that there are 4 key characteristics which must be executed (without error) in order to par the hole. In this case, what is the probability of accomplishing the task error free?

Rolled Yield .7581 .9999864

33 66With

Shifting

Tee Shots .9331 .9999966

Fairway Shots .9331 .9999966

Chipping .9331 .9999966

Putting .9331 .9999966

Rolled Throughput Yield

CALCULATING SIGMA - YIELDCALCULATING SIGMA - YIELD

Remedy 1: Remedy 1: Reduce Parts/StepsReduce Parts/Steps

Remedy 2:Remedy 2:Improve Sigma per Part/StepImprove Sigma per Part/Step

Yields thru Multiple Steps/Parts/ProcessesZst

(distribution shifted 1.5)

# of parts, steps, or

processes3 4 5 6

1 93.32% 99.38% 99.9767% 99.99966%

5 70.77% 96.93% 99.88% 99.9983%

10 50.09% 93.96% 99.77% 99.997%

20 25.09% 88.29% 99.54% 99.993%

50 3.15% 73.24% 98.84% 99.983%

100 53.64% 97.70% 99.966%

200 28.77% 95.45% 99.932%

500 4.44% 89.02% 99.830%

1000 0.20% 79.24% 99.660%

2000 62.79% 99.322%

10000 9.76% 96.656%

Yields thru Multiple Steps/Parts/ProcessesZst

(distribution shifted 1.5)

# of parts, steps, or

processes3 4 5 6

1 93.32% 99.38% 99.9767% 99.99966%

5 70.77% 96.93% 99.88% 99.9983%

10 50.09% 93.96% 99.77% 99.997%

20 25.09% 88.29% 99.54% 99.993%

50 3.15% 73.24% 98.84% 99.983%

100 53.64% 97.70% 99.966%

200 28.77% 95.45% 99.932%

500 4.44% 89.02% 99.830%

1000 0.20% 79.24% 99.660%

2000 62.79% 99.322%

10000 9.76% 96.656%

CALCULATING SIGMA - YIELDCALCULATING SIGMA - YIELD

YIELD DECREASES WHEN COMPLEXITY INCREASESYIELD DECREASES WHEN COMPLEXITY INCREASES

“Is the variation (spread) of my measurement system too large to study the current level of process variation?”

+ =

(Observed Variability)

Total VariabilityProduct VariabilityProcess Variability

Variationin the

measurementprocess

THE FUNDAMENTAL MSA QUESTIONTHE FUNDAMENTAL MSA QUESTION

To address actual process To address actual process variability, the variation due to the variability, the variation due to the measurement system must first be measurement system must first be identified and separated from that of identified and separated from that of the process. the process.

Observed Process Variation

Actual Process Variation

Measurement Variation

Long-term Process Variation

Short-term Process Variation

Repeatability

Accuracy

Stability

Linearity

POSSIBLE SOURCES OF VARIATIONPOSSIBLE SOURCES OF VARIATION

Reproducibility

LEVELS OF ANALYSISLEVELS OF ANALYSIS

Measure 1 Individual Experience

Measure 2 Group Experience

Analyze 3 Graphical Interpretation of Observed Data

Analyze 4 Statistical Interpretation of Observed Data

Improve 5 Graphical Interpretation of Experimental Data

Improve 6 Statistical Interpretation of Experimental Data

“ Think Directional”

Continuous DataContinuous Data Discrete DataDiscrete Data

DiscreteData

ContinuousData

X

Y

THE ANALYSIS TOOL DEPENDS ON THE THE ANALYSIS TOOL DEPENDS ON THE QUESTION AND THE DATA TYPEQUESTION AND THE DATA TYPE

Variance Different ?

Statistical: Test of Equal Variances

Graphical: Stratified Box Plots

Variance Different ?

Statistical: Test of Equal Variances

Graphical: Stratified Box Plots

Means Different ?

Statistical: t-test; ANOVA

Graphical: Histogram(s)

Means Different ?

Statistical: t-test; ANOVA

Graphical: Histogram(s)

How does change in X affect change in Y ?

Statistical: Correlation /Regression

Graphical: Scatter Plots


Statistical: Correlation /Regression

Graphical: Scatter Plots


Statistical: Logistic Regression


Statistical: Logistic Regression

Are the outputs different ?

Statistical: Chi Square, Proportion tests

Graphical: Stratified Pareto Diagrams

Are the outputs different ?

Statistical: Chi Square, Proportion tests

Graphical: Stratified Pareto Diagrams

Allows us to answer the practical question:“Is there a real difference between Dr. A and Dr. B ?”

A practical process problem is translated into a statistical hypothesis so that we may answer the question above.

In hypothesis testing, we use relatively small samples to answer questions about large populations. There is always a chance that we selected a sample that is not representative of the population - a “weird” sample. Therefore, there is always a chance that the conclusion obtained is wrong.

With some assumptions, inferential statisticsnferential statistics allows us to estimate the probability of getting a “weird” sample. Hypothesis testing quantifies the probability (P-Value) of a wrong conclusion.

Data vs. Gut Feeling

HYPOTHESIS TESTING DESCRIPTIONHYPOTHESIS TESTING DESCRIPTION

TruthTruth

HoHo HaHa

Fail to Reject HoFail to Reject Ho

Reject HoReject Ho

Type IError

Type IIError

Correct

Decision

CorrectDecision

Also called: Type II error Consumers’ Risk

Also called: Type I error Producers’ Risk

is the risk of finding a difference when there really isn’t one.

is the risk of not finding a difference when there really is one.

ALPHA & BETA RISKALPHA & BETA RISK

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