advanced spc for healthcare - eventpilot...

12/5/2011

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Advanced SPC for Healthcare

December 5, 2011

Brent James, MD, Intermountain Healthcare

James Benneyan, PhD, Northeastern University

Victoria Jordan, PhD, UT MD Anderson Cancer Center

Introductions

Who are you? Who are we?

1 minute each:

• Name

• Where from

• Why here, what hope to learn

• Level of SPC experience

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Meet the Faculty – Brent James, MD

• Chief Quality Officer and Executive

Director, Institute for Health Care

Delivery Research, Intermountain

Health Care and founder of ATP

• Faculty appointments at the University

of Utah School of Medicine, Harvard

School of Public Health, University of

Sydney, Australia

• Member, National Academy of

Science’s Institute of Medicine

• Fellow, American College of Physician

Executives

• Numerous awards and honors in healthcare including the

Deming Cup, C. Jackson Grayson Medal, Distinguished

Quality Pioneer, Joint Commission Earnest A. Codman

Award, and the National Committee for Quality Assurance

Quality Award

Meet the Faculty – Jim Benneyan, PhD

Areas of expertise:

• Healthcare systems engineering

• Statistical quality control, probability, optimization

• Director, Healthcare systems engineering

program, Northeastern University (Boston)

• Faculty, Northeastern University (systems

engineering and operations research)

• Director, National Science Foundation

Center for Health Organization

Transformation (CHOT)

• PI and senior engineer, New England VA

Engineering Resource Center (VERC)

• IHI faculty, improvement advisor, fellow

• Healthcare Quality and Productivity

Incorporated, Partner

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Meet the Faculty – Victoria Jordan, PhD

• Director, Quality Measurement and

Engineering, UT MD Anderson Cancer Center

• University of Texas Chancellor’s Heath Fellow

for Systems Engineering

• University of Texas McCombs School of

Business Research Fellow

• PhD – Industrial and Systems Engineering

(Applied Statistics), MS – Industrial &

Systems Engineering, MBA, BS-Statistics

• > 25 years Experience in Quality

Improvement

• ASQ Certified Six Sigma Master Black Belt

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Session Objectives

• Important concept of detection power to

assess effectiveness of control charts

• Appropriate chart design and sample sizes

• More advanced charts (EWMA, Cusum) to

improve detection ability

• Measurement error and impact on process

improvement

• Common errors in practice

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Agenda

• Introductions / Review Agenda

• SPC 101: Review of SPC Basics

• Assessing Performance

• Improving Performance

• Lunch

• Case study

• Assessment of Measurement Systems

• Top 10 Common Pitfalls and

─g charts

• Wrap up, Q&A7

Agenda

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8:30 – 8:50 Introductions, Agenda review Exercise

8:50 – 9:30 “SPC 101”: Review of basics 1

9:30 – 10:45 Assessing performance 2

10 – 10:15 break

10:45-11:45 “SPC 201”: Improving performance 3

12:00 - 1:00 lunch

1:00 – 1:45 Case studies 4

1:45 - 3:00 “SPC 301”: Measurement as a system 5

2:15 – 2:30 break

3:00 – 4:00 More advanced topics, Pitfalls to avoid

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Handouts

You should have:

• Agenda

• SPC Excel macro

• Slides electronically

• Data sets electronically

• > 1 laptop per table (relocate if needed)

• Optional: Calculator or Excel

• coffee…9

References

1. Benneyan J (2008), “Design, Use, and Performance of Statistical

Process Control Charts for Clinical Process Improvement”,

International Journal of Six Sigma, 4(3): 219-239

2. Montgomery D (1985). Introduction to Statistical Quality Control, Wiley

3. Benneyan J (1998), “Statistical Quality Control Methods in

Epidemiology. Chart Use, Statistical Properties, and Research Issues”,

Infection Control Hospital Epidemiology, 19(4):265-283

4. Jordan V, Benneyan J (2012), Common Errors in Using Healthcare

SPC, in Statistical Methods in Healthcare, Wiley, to appear

5. Benneyan J (2001), “Number-Between g-type Statistical Control Charts

for Monitoring Adverse Events”, Health Care Management Science,

4:305-318

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Section I: SPC 101

Basic Methods

(Dr Brent James)

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Learning Objectives

• Review basic concepts of SPC

• Different types of variability

• Different types of data and charts

o Binomial: np, p charts

o Poisson: c, u charts

o Normal: Xbar & S charts (or Xbar & R) (or XmR??)

• Chart construction and interpretation

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Review of SPC

• Slides and case study from Brent?

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Applications of Control Charts

1. Detect and monitor process Variation over time.

2. Distinguish Special Cause variation from Common Cause Variation.

3. Common language for discussing process performance.

4. Determine process capability

5. Develop a plan for process improvement

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Definitions

1. Common Cause Variation: Causes that are inherent

over time and affect everyone in the process and the

process outcome.

2. Special Cause Variation: Causes that arise from

specific circumstances and are not part of the process

all of the time.

3. Stable Process: Implies that the variation is predictable

within common bounds.

4. Unstable Process: A process that is affected by both

special cause variation and common cause variation.

The variation from one time period to the next is

unpredictable.

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Attribute control charts are used when it is necessary to classify or

count a particular characteristic of a process instead of measuring it.

Note:A “defective” may be caused by more than one “defect.”

There are four types of Attribute control charts:

1) NP-Chart: For the number defective, where each item is either go/no-

go, good/bad, yes/no, etc., use with constant subgroup size.

2) P-Chart: For the proportion defective, where each item is either go/no-

go, good/bad, yes/no, etc., and changing or constant subgroup size.

3) C-Chart: For counting defects with a constant area of opportunity

where the defects are drawn from. There is no upper limit on the

number of defects that could occur.

4) U-Chart: For counting defects per changing area of opportunity. There

is no upper limit on the number of defects that could occur.

Which chart should I use? Ask:

1) Is there a maximum count for each group? Yes or No?

2) Is each subgroup the same size? Or changing?

Attribute Control Charts

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Understanding Variation Choosing the right control chart

Group Exercise

Which Control Chart Should be Used?

For each of the following, identify different ways that the variable could be measured and the appropriate control chart(s) for each:

o Patient wait time

o Patient and family complaints

o Falls

o Medication Errors

o SCIP bundle delivered

o Glucose level

o Percent of patients receiving genetic counseling

o Time to next available appointment

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Control Charts

Zone

A

B

C

C

B

A

UPPER CONTROL LIMIT

LOWER CONTROL LIMIT

CENTERLINE

Tests for patterns in data are based on the laws

of probability, assuming a normal distribution

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68.36%99.73% 95.44%

Understanding Variation

Non Random Patterns That Should be Investigated

TEST 1:

POINT OUTSIDE

CONTROL LIMIT

(p = 0.0027)

TEST 2:

TWO OUT OF THREE POINTS ON

ONE SIDE OF THE CENTER LINE

IN ZONE A OR BEYOND

(p = 0.0015)

TEST 3:

FOUR OF FIVE POINTS ON

ONE SIDE OF THE CENTER

LINE IN ZONE B OR BEYOND

(p = 0.0027)

TEST 4:

RUN OF EIGHT POINTS ON THE

SAME SIDE OF THE CENTER LINE

(p = 0.0039)

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Test 5. SIX POINTS IN A

ROW STEADILY INCREASING

(OR DECREASING)

(p = 0.0024)

Test 6. FOURTEEN POINTS

IN A ROW ALTERNATING

UP & DOWN

Test 7.

STRATIFICATION - 15

POINTS HUGGING

THE CENTERLINE

(p = 0.0033)

Test 8 . EIGHT POINTS IN A

ROW ON BOTH SIDES OF THE

CENTER LINE WITH NONE IN

ZONE C

(p = 0.0001)

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Understanding VariationNon Random Patterns That Should be Investigated

Table Exercise

Assessment of Control

• For the following slides, determine what

type of control chart is used and whether

or not the process is in control.

• If the process is not in a state of control,

explain why (what rule is violated) and

what that means in terms of the specific

process.

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Control Chart Example 1

343128252219161310741

0.95

0.90

0.85

0.80

0.75

0.70

Time (Weeks)

Proportion

_P=0.80

UCL=0.92

LCL=0.68

Handwashing Compliance


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343128252219161310741

0.95

0.90

0.85

0.80

0.75

0.70

Time (Weeks)

Proportion

_P=0.80

UCL=0.92

LCL=0.68


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343128252219161310741

0.95

0.90

0.85

0.80

0.75

0.70

Time (Weeks)

Proportion

_P=0.80

UCL=0.92

LCL=0.68



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0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

Day of Week

Proportion

_P=0.06

UCL=0.09

LCL=0.03

Available Beds

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343128252219161310741

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

Day of Week

Proportion

_P=0.06

UCL=0.09

LCL=0.03

Available Beds

343128252219161310741

0.09

0.08

0.07

0.06

0.05

0.04

0.03

0.02

Day of Week

Proportion

_P=0.06

UCL=0.09

LCL=0.03

Available Beds


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343128252219161310741

5000

4500

4000

3500

3000

Time (Weeks)

Dollars

_X=4066

UCL=5060

LCL=3072

343128252219161310741

1200

900

600

300

0

Time (Weeks)

Moving Range

__MR=374

UCL=1221

LCL=0

Amount Spent on Clinic Supplies


343128252219161310741

5000

4500

4000

3500

3000

Time (Weeks)

Dollars

_X=4066

UCL=5060

LCL=3072

343128252219161310741

1200

900

600

300

0

Time (Weeks)

Moving Range

__MR=374

UCL=1221

LCL=0

Amount Spent on Clinic Supplies

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343128252219161310741

7.00%

6.00%

5.00%

4.00%

3.00%

Time (Days)

Proportion

_P=5.17%

UCL=7.28%

LCL=3.07%

Percentage of Barcodes Containing Errors


343128252219161310741

7.00%

6.00%

5.00%

4.00%

3.00%

Time (Days)

Proportion

_P=5.17%

UCL=7.28%

LCL=3.07%


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343128252219161310741

7.00%

6.00%

5.00%

4.00%

3.00%

Time (Days)

Proportion

_P=5.17%

UCL=7.28%

LCL=3.07%



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25

20

15

10

5

0

Time (Weeks)

Number of Complaints

_C=14.23

UCL=25.54

LCL=2.91

Patient Complaints

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343128252219161310741

25

20

15

10

5

0

Time (Weeks)


_C=14.23

UCL=25.54

LCL=2.91

Patient Complaints


343128252219161310741

25

20

15

10

5

0

Time (Weeks)


_C=14.23

UCL=25.54

LCL=2.91

Patient Complaints

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Section II:

Assessing Chart

Performance

(Jim Benneyan)

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