Six Sigma: Defining the Problem

Six Sigma in Healthcare:

Defining and Measuring

the ProblemLeslie W. Hayes, MD

Six Sigma Black Belt

D M A I C

Six Sigma

Greek letter, σ, used to denote variation

(spread) around the central tendency of a

process

A term used to refer to a

Cultural Value or Philosophy

Goal

Measurement System

A process to drive out waste and improve

quality, cost and time performance of any

organization

D M A I C

The letter “f”

Count the number of times the letter f appears

in the following:

Finished files are the result of years of scientific

study combined with the experience of years

D M A I C

How is Six Sigma Different from

other QI approaches?

Strategically deployed

Customer focused

ROI calculations

Statistically Based Y=f(x)

Built-in project management

Preoccupation with error proofing

Quality improvement on steroids

D M A I C

Six Sigma

Some Terminology Sigma – expression of process yield based on the

number of defects per million opportunities (DPMO)

Unit – the item produced or processed

CTQ – Critical to Quality

Truly critical to customer’s perception of quality

Can be measured

Can set specifications to know whether or not the CTQ has been achieved

Defect – any event that does not meet customer specification

Defect Opportunity – a measurable chance for a defect to occur

Defective – A unit with one or more defects

D M A I C

What is Six Sigma Performance?

Sigma Defects per Million

Opportunities

How Good is the

performance?

1 688,680 31.1%

2 308,537 69.1%

3 66,807 93.3%

4 6,210 99.4%

5 233 99.98%

6 3.4 99.99966%

20,000 times

improvement

D M A I C

Sigma Level

1,000,000

100,000

10,000

1,000

100

10

1

0 1 2 3 4 5 6

Domestic Airline

Fatality Rate

(0.43 DPMO)

Airline baggage handling

IRS Tax Advice

(phone in)

Prescription Writing

Healthcare associated harm

Restaurant bill mistakes

Reliable Performance is Elusive….

De

fec

ts p

er

Mil

lio

n

6 sigma accuracy = 3.4 defects per million

LSL USL

p(d)

M

6 4 3 2 1 1 2 3 4 5 65

p(d)

A 6 Sigma Process includes

6 standard deviations between

the mean and the spec limit

Sigma Values

+/- 1 = 68.26%

+/- 2 = 95.44%

+/- 3 = 99.73%

Six Sigma

Point of Inflection

s1

7

Anesthesia deaths

ASA class I

D M A I C

Would you be happy with 99.9%

performance?

1 hour of unsafe drinking water every month

2 unsafe plane landings per day at O’Hare

16,000 pieces of mail lost by the USPS every

hour

500 incorrect surgical operations every week

50 newborn babies dropped at birth by doctors

each day

22,000 checks deducted from the wrong bank

accounts each hour

D M A I C

What is Six Sigma Quality?

Quality

Product

FeaturesThat Customers Want

Freedom from

DefectsAt Six Sigma Levels

D M A I C

The Methodology: DMAIC

DEFINE Identify the right project

MEASURE Identify and understand key

process and outcome

measures

ANALYZE Identify key process

determinants

IMPROVE Establish new model and

optimize performance

CONTROL sustain the improvements

D M A I C

Comparison of different models

11

D M A I C

Project FocusPhase

DEFINE The right project with the right team

MEASURE Process

Problems

Outputs (outcomes)

ANALYZE

IMPROVE

CONTROL

Process inputs

The Vital Few “x’s”

the “Y”

the “x’s”

Goal: Y = f(x)

D M A I C

DEFINE: the goals

Identify the actual problem

Identifying the customers of the project,

including their specifications

Involve the right people

Creating a team charter

D M A I C

Overview of Define

Define the

business

case

Gain

project

approval

Understand

The

customer

Define

the

process

Manage

the

project

• Problem

statement

• Goals

statements

• Costs of poor

quality

• Project plan

• Stakeholder

analysis

• Storyboards

• SIPOC• VOC

• Kano analysis

• CTQ tree

• Project

charter

D M A I C

SMART Problem Statements

Specific

Measurable

Achievable

Relevant

Time Bound

D M A I C

Goal Statements

Keep it brief

Avoid technical language

Use same metrics as your problem statement

Be as specific as possible about dates

D M A I C

Cost of Poor Quality

Rework

Rejects

Inspection

Testing

Customer returns/complaints

Excess inventory

High employee turnover

D M A I C

Voice of the Customer (VOC)

Define your customer

Frontline staff involvement

Families/patients perspective

Sampling methods

D M A I C

“Delighters”

“More is better”

“Must haves”

Kano Analysis

D M A I C

S I P O C

StepProcess starts

Step Step StepProcess

Ends

Operations

Sales

Accounts

Legal

Patients

Clinicians

Patient data

Specimens

Equipment

Supplies

Outcomes

Product

Process

Data

Clinician

Lab Tech

Patient

Family

D M A I C

SIPOC Exercise – 20 minutes

D M A I C

CTQ Tree (Key Driver Diagram)

Provides clarity and structure

Turns the customers need into a measureable

specification

General steps

VOC

Key Drivers of the customer’s needs

What element of the key driver is critical to a

quality process/outcome

Specifications

D M A I C

D M A I C

What the

Customer

needs

What the

Customer

Means by

good #1

How do we

Define

Critical element?

How often

Should this

Occur?

VOC Drivers CTQs Specifications

What the

Customer

Means by

good #2

What the

Customer

Means by

good #3

How do we

Define

Critical element?

How do we

Define

Critical element?

How often

Should this

Occur?

How often

Should this

Occur?

D M A I C

GOOD

PIZZA

What the

Customer

Means by

good #1

How do we

Define

Critical element?

How often

Should this

Occur?


What the

Customer

Means by

good #2

What the

Customer

Means by

good #3

How do we

Define

Critical element?

How do we

Define

Critical element?

How often

Should this

Occur?

How often

Should this

Occur?

D M A I C

GOOD

PIZZA

TIMELY

How do we

Define

Critical element?

How often

Should this

Occur?


COOKED

RIGHT

ACCURATE

How do we

Define

Critical element?

How do we

Define

Critical element?

How often

Should this

Occur?

How often

Should this

Occur?

D M A I C

GOOD

PIZZA

TIMELY ≤ 15 minutes100% of the

time


COOKED

RIGHT

ACCURATE

Correct internal

temperature

Product

matches

order

100% of the

time

100% of the

time

Cheese

browned

Crispy

D M A I C

Good

Pizza

Timely ≤ 15 minutes100% of the

time


Cooked right

Accurate

Correct internal

temperature

Product matches

order

100% of the

time

100% of the

time

Cheese browned

Crispy

CTQ Exercise – 20 minutes

D M A I C

Project Charter

Output of Define (summary)

An agreement between management and the

team

Your marching orders going forward

Keeps the team focused

D M A I C

Team Charter

Name ______________________________

Title _______________________________

PURPOSE:

(Includes Problem Statement)

IMPORTANCE:

(business case; benefits to business,

customers, employees)

SCOPE

1. This process starts with

2. This process ends with

3. Inside scope

4. Outside Scope

Project Schedule

5. Project to start (date)

6. Project to end (date)

7. Define to end (date)

8. Measure to end (date)

9. Analyze to end (date)

10. Improve to end (date)

11. Control to end (date)

Budgetary Needs

Goals of Project

DELIVERABLES:

(List items delivered from the

project team)

Improved process, new process documentation, training, etc.

MEASURES:

(Key measures; how much

improvement is needed?)

1. We have (3) key measures.

They are :

2. Today we are operating at

3. Our target is to (increase or

decrease)

4. We will produce these

results by the end of (month and

year project ends)

Purpose

Importance

Scope/focus

Measures

Deliverables

Resources

Elements of a Charter

D M A I C

Consider working on a Project

Charter with your Team This

Weekend

D M A I C

Pulling Define Together

D M A I C

Background

We have a large number of critical lab values that must be reported by the lab techs in a timely fashion to prevent harm from occurring to patients and to maintain compliance with regulatory standards.

In reality, it all started with a page one Monday morning…

D M A I C

S I P O C

LT retrieves

value

Value resulted on

machine

LT determines ifCritical Value

LT determineswho to call

LT delivers results

LT documentsnotification

Patient

Clinician

Collector

Order Enterer

Transporter

Lab Tech (LT)

Order

Specimen

Workers

Machine

Log Book

Telecommunications

Documentation of the Critical

Value

Provider Knowledge of Critical Value

Clinician

Lab Tech

Patient

Family

D M A I C

D M A I C

Give and

Record

important

information

quickly

Accurate

Clinician

information

available

Timely

Notification of

information

Accurate

Patient

Information

Important

information

communicated

On-call list is

accurate

Correct service

information is

available

Patient location is

known

Time from value

available to

initial call to

clinician is short

Complete

notification of

values done in

stated time

Values on

‘Critical Value’

list are truly

critical

100% of time

100% of time

100% of time

Less than 30

minutes

100% of time

20% reduction

in notifications


Problem Statement Goal Statement

Hospital critical lab values are

reported by lab techs to the

appropriate healthcare providers.

Our standard is that notification

occurs within 30 minutes of the

resulted value. Our current rate of

compliance with this standard is

89%.

To increase notification within 30

minutes to 100% by January 31,

2010.

Project Scope Business Case/Financial Impact

Identify improvements to reduce

cycle time. Reduce the amount of

notifications made by analyzing

the criticality of results and making

adjustments.

Reduce time spent in notification

process by lab techs. Reduce the

probability of a citation by regulatory

agencies.

D M A I C

Charter

Customers and CTQs Project Team

Lab Tech

Clinician

Patient/Family

CTQs:

See CTQ Slide

Champion: Pathology MD

Process Owner: Lab

Director

Team Leaders: Black Belt

Core Team Members: Lab

techs, prescribers

D M A I C

Project Plan

Start Finish

Define 9/9/09 9/25/09

Measure 9/26/09 10/21/09

Analyze 10/22/09 10/28/09

Improve 10/29/09 12/14/09

Control 12/15/09 1/31/09

D M A I C

Questions?

D M A I C

Break

D M A I C

Measure

D M A I C

Data helps us…

Separate what we think from what is real

Confirm or disprove ideas

Establish baseline

Observe history of problem over time

Understand variation

Avoid “solutions” that don’t solve the real

problem

D M A I C

Overview of Measure

Develop

Process

measures

Baseline

process

capability

Collect

Process

data

Check

Data

quality

Understand

Process

behavior

• Metrics

• Operational

definitions

• Data worlds

• Distributions

• Process

stability

• Short/Long

term variation

• Measurement

system

analysis

• Collection

methods

• Collection

plans

• Sampling

• Process

capability

• DPMO

• Sigma Levels

• Sigma Shift

D M A I C

Overview of Measure

Develop

process

measures

Baseline

process

capability

Collect

process

data

Check

data

quality

Understand

process

behavior

• Metrics

• Operational

definitions

• Data worlds

• Distributions

• Process

stability

• Variation

• Measurement

system

analysis

• Collection

methods

• Data not

related to

time

• Data related

to time

• Process

capability

• DPMO

• Sigma Levels

• Sigma Shift

D M A I C

Approach to Measure

Data Collection

Data not related to time

Pinpoint occurrence of problems

Data related to time

Identify variation (patterns) in process

Create detailed process maps

Calculate process sigma

D M A I C

Elements to Discuss

Operational Definitions

Data Worlds

Data not related to time

Data related to time

Variation

DPMO

Process sigma

D M A I C

Metrics (KPIs)

CTQs (Key Drivers) are the basis for your Key

Performance Indicators

Measurements that reflect the VOC

Data collection

Balance efficiency and effectiveness

D M A I C

Please write down clipboard

clock time for each of the

following

1. Sign in _________________________

2. Registration done_________________

3. Called back & weighed_____________

4. Placed in room to wait______________

5. Nurse in room to see you____________

6. Doctor in room to see you____________

7. Discharge instructions done__________

8. Leave Clinic_______________________

D M A I C

Granularity of data

needed can

determine collection

methods

D M A I C

What $20 and your patients’

families can do…

D M A I C

2892572251931611299765331

200

150

100

50

0

Clinic Visit

Tim

e in

min

ute

s

_X=84.8

UCL=156.0

LCL=13.7

Total GI Clinic Encounter Time

D M A I C

Operational Definitions Specific and concrete

Measurable

Useful

Questions:

How will you measure your data?

What related conditions will you record?

Sampling technique?

How or where will you record your data?

D M A I C

Exercise: Creating an Operational

Definition – 20 minutes

D M A I C

Taguchi Loss FunctionMatching process with specifications

D M A I C

Desired Data CharacteristicsUseful, meaningful data are… Typical problem

Sufficient

enough so the patterns you see are likely to

be real

Insufficient

not enough data to reach reliable

conclusions

Relevanthelps you solve/pinpoint the problem

Irrelevant

describes a characteristic that doesn’t help

you understand the problem

Representative

full range of actual process conditions seen

Biased

only represents certain process conditions

Contextual

collected along with other key information

Isolated

your data is the only information you have

about the process

D M A I C

Data Worlds

“Continuous” “Attribute”“Count”

“Measuring”

something

“Classifying”

something

“Counting”

things

• Calculated averages and

variation

• Resolution only limited

by measurement system

• Counting whole things

• Data can only be

integers

• Categories that do not

necessarily have

numerical value or order

D M A I C

Continuous Data World How to spot continuous data

Not limited to whole numbers

Examples:

Oven temperature

Length of hospital stay

Time to next available appointment

Laboratory values

Invoice processing time

Caution: resolution of the measurement system can affect your data

D M A I C

Count Data World (Poisson)

How to spot count data

Half units not possible

No physical upper limit

Data recorded for a specific area of opportunity

Defects per unit

Examples

Calls to the IT helpdesk each hour

Employee needle sticks

Patient complaints

D M A I C

Attribute Data World (Binomial)

How to spot attribute data

Classifications

Often in percentages

Examples

Tossing a coin

Proportion of patients experiencing harm

D M A I C

Data NOT Related to Time

Tally (check) sheets

Pareto

Cause and Effect Diagram

Detailed Process Mapping

D M A I C

Tally Sheets

.

Study

subject

PIB Type PIB

Placement

PIB Information:

Name Correct

PIB

Information:

Gender

Correct

PIB

Information:

DOB Correct

Age Required

Spanish

interpreter

(Y or N)

1

2

3

4

5

6

7

8

9

10

D M A I C

D M A I C

Documented Sever Severity by s/s Daytime Symp Night Cough/Awa Activity Limits Beta Agonist use Exac in <1 yr

Moderate Pers Persistent NR 1-2/week NR 1-2/month No

Moderate Pers Persistent NR Nightly"could not tolerate PE

last year" 1/month No

Moderate Pers Persistent NR NR

Yes, "missing significant amounts [of school] due to

asthma" NR Yes >2

NR Persistent by ICS NR NR NR NR NR

Mild Pers Persistent 3/week None Some NR No

NR Persistent NR None NoneNo albuterol over 6

months Yes-1

NR Persistent NR NR NR NR Yes-1

NR Persistent by ICS NR NR NR NR No

Moderate Pers Persistent NR None None 1/month winter None



NR Persistent NR Nightly NR NR Yes >2

NR Intermittent <1 month NR NR <1 month No

Moderate Pers Persistent 1-2/week NR Alb before exercise NR Yes >2

NR ? NR NR NR NR NR

NR Persistent NR NR NR NR Yes >2



NR Persistent by ICS NR NR NR NR Yes >2

NR Persistent by ICS NR NR NR NR Yes-1

NR ? <1 month NR NR <1 month No

Moderate Pers Persistent NR NR NR NR Yes-1

What does your data collection

strategy look like?

D M A I C

Pareto Principle

80:20 rule

In the 1800s, an Italian

economist Vilfredo Pareto

noted that 80% of the land

in Italy was owned by 20%

of the population

Principle applies in other

areas

.

D M A I C

Pareto Chart

.

D M A I C

Count 2 2 2 1 140 13 7 6 4 3 3 2

Percent 2 2 2 1 147 15 8 7 5 3 3 2

Cum % 93 95 98 99 10047 62 70 77 81 85 88 91

Mechanism

Pulle

d be

hind

ATV

on inne

rtube

v Pole

Peds

v Tre

e Lim

b

Water

craft

TV

Peds

v Tra

ctor

Assa

ult

Motor

cycle

GSWFa

l lBike

ATV

Peds

v A

uto

MVA

9080706050403020100

100

80

60

40

20

0

Co

un

t

Pe

rce

nt

Mechanism of Injury

D M A I C

Transfers 7 7 6 6 6 5 841 39 33 26 19 17 14 8

Percent 3 3 2 2 2 2 317 16 14 11 8 7 6 3

Cum % 84 87 90 92 95 97 10017 33 47 57 65 72 78 81

Unit

OtherM

7W5

OSCU

GynXW

6P7S7P9W9

W8P8S86SS9

250

200

150

100

50

0

100

80

60

40

20

0

Tra

nsfe

rs b

y U

nit

Pe

rce

nt

Transfers to MICU Oct-12 through Aug-13

D M A I C

D M A I C

Documented Sever Severity by s/s Daytime Symp Night Cough/Awa Activity Limits Beta Agonist use Exac in <1 yr

Moderate Pers Persistent NR 1-2/week NR 1-2/month No

Moderate Pers Persistent NR Nightly"could not tolerate PE

last year" 1/month No

Moderate Pers Persistent NR NR

Yes, "missing significant amounts [of school] due to

asthma" NR Yes >2


Mild Pers Persistent 3/week None Some NR No

NR Persistent NR None NoneNo albuterol over 6

months Yes-1


NR Persistent by ICS NR NR NR NR No

Moderate Pers Persistent NR None None 1/month winter None



NR Persistent NR Nightly NR NR Yes >2

NR Intermittent <1 month NR NR <1 month No

Moderate Pers Persistent 1-2/week NR Alb before exercise NR Yes >2

NR ? NR NR NR NR NR

NR Persistent NR NR NR NR Yes >2



NR Persistent by ICS NR NR NR NR Yes >2

NR Persistent by ICS NR NR NR NR Yes-1

NR ? <1 month NR NR <1 month No

Moderate Pers Persistent NR NR NR NR Yes-1

29

21

58.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0

5

10

15

20

25

30

35

40

45

50

yes no

Nig

ht

sym

p

Categories

Nighttime Symptoms Recorded 2014?

D M A I C

21

8

3 32 2 2

1 1 1 1 1 1 1 1 1

42.0%

58.0%

64.0%

70.0%74.0%

78.0%82.0%

84.0%86.0%

88.0%90.0%

92.0%94.0%

96.0%98.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0

5

10

15

20

25

30

35

40

45

50N

igh

t sym

p

Categories

Nighttime Symptoms Present 2014?

D M A I C

21

14

10

32

42.0%

70.0%

90.0%

96.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

0

5

10

15

20

25

30

35

40

45

50

NR <2x/month >1x/week 7x/week 3-4x/month

Nig

ht

sym

p

Categories

Nighttime Symptoms Present 2014 (Desired Wording)

D M A I C

Questions about Pareto Charts?

D M A I C

Histograms

D M A I C

Distribution Shapes and the

Normal Distribution

Right skewed (positive skew)

Left skewed (negative skew)

Normal distribution

D M A I C

24020016012080400

Median

Mean

80706050403020

1st Q uartile 13.250

Median 43.000

3rd Q uartile 114.500

Maximum 244.000

55.429 82.291

27.000 66.289

59.433 78.635

A -Squared 4.71

P-V alue < 0.005

Mean 68.860

StDev 67.691

V ariance 4582.021

Skewness 0.852634

Kurtosis -0.496777

N 100

Minimum 0.000

A nderson-Darling Normality Test

95% C onfidence Interv al for Mean

95% C onfidence Interv al for Median

95% C onfidence Interv al for StDev

95% Confidence Intervals

Summary for Age at ICU Admission (months)

D M A I C

Normal Distribution

D M A I C

• 6σ performance = 99.99966%

• 6 Std Dev = 99.7% (4.3 σ)

DPMO

Sigma Defects per Million

Opportunities

How Good is the

performance?

1 688,680 31.1%

2 308,537 69.1%

3 66,807 93.3%

4 6,210 99.4%

5 233 99.98%

6 3.4 99.99966%

20,000 times

improvement

D M A I C

Sigma Table

D M A I C


D M A I C

24 steps 22 steps 12 steps

7 steps !!!


D M A I C

Process Mapping Group Time

D M A I C

Six Sigma: Defining the Problem

Education

Transcript of Six Sigma: Defining the Problem