using spc charts to monitor improvement oha workshop 2015ohiohospitals.org/oha/media/images/annual...

………………..……………………………………………………………………………………………………………………………………..

Advanced Quality ImprovementUsing SPC Charts to Monitor

Improvement

OHA Workshop 2015

Richard McClead, M.D., M.H.A.

James Dail, M.B.A., C.L.S.S.B.B.

………………..……………………………………………………………………………………………………………………………………..

Who are we?

• Richard McClead, M.D., M.H.A.Associate Chief Medical Officer

Nationwide Children’s Hospital

Columbus, Ohio

• James Dail, M.B.A., C.L.S.S.B.B.Lead Quality Improvement Coordinator

Quality Improvement Services

Nationwide Children’s Hospital

Columbus, Ohio

………………..……………………………………………………………………………………………………………………………………..

Advanced Quality Improvement

I. QI Science – Foundation Background/History/Variations

IHI Model For Improvement

II. SPC – The Outcome & Process Measurement System Why Run Charts & Control Charts

Reading Run and Control Charts Learn to identify “special Cause” change – Table Exercise

III. Types of SPC Charts Selection Considerations

Data Types

Expected Change

Denominator Availability

Numerator Focus

………………..……………………………………………………………………………………………………………………………………..

Quality Improvement “Dimensions”

• “Quality Improvement” means making things “better”

• In our world, it has two sides:

• “touchy feely” – what changes can you make to a

system, how do you get people to accept change?

• “science” – how do you use data to look at your

system?

• Statistical Process Control

………………..……………………………………………………………………………………………………………………………………..

SPC in one slide

• All measurements vary from time to time

• The numbers can fluctuate in a “natural” pattern due to a myriad of

small causes

• When it does, the data follows some distribution (e.g., a “normal”

distribution, binomial distribution, …)

• The numbers can also fluctuate in an “unnatural” pattern due to

abnormal causes

• If we’re collecting data and analyzing it, we can pick out the

“unnatural” variations, identify their causes, and eliminate/replicate

them

• We can also make changes to the system to see if the “natural”

distribution becomes more acceptable

………………..……………………………………………………………………………………………………………………………………..

Where did QI/QC/SPC start?

• 1920s – Western Electric (manufacturer for Bell)

• Repeated adjustment of manufacturing process to compensate

for departures from the process average often resulted in

greater variability

• However, a “large” deviation might still indicate the need for

corrective action

• There was simply no understanding of how to deal with this

dilemma, therefore …

• The only method of quality control was rejecting defectives

before shipping

………………..……………………………………………………………………………………………………………………………………..

Walter Shewhart

• Ph.D. in physics from U.C. Berkeley

• 1924 – asked to figure out how to determine when

process adjustment was needed

• "Dr. Shewhart prepared a little memorandum only about a page in length.

About a third of that page was given over to a simple diagram which we

would all recognize today as a schematic control chart. That diagram, and

the short text which preceded and followed it, set forth all of the essential

principles and considerations which are involved in what we know today as

process quality control."

………………..……………………………………………………………………………………………………………………………………..

………………..……………………………………………………………………………………………………………………………………..

Shewhart

• Control chart: When will search for and removal of

causes of variation reduce variation, and when will

search and action lead to intensified variation or waste?

• Common cause vs. special cause variation

• Chronological display of data

• Fundamentally transformed manufacturing; the quality of

every item in your possession has been influenced by

Shewhart

Statistical Process Control (SPC)

• SPC charts help an

improvement team

distinguish between

variation in data that

is common due to

the process and that

which is special due

to a change in the

process.

………………..……………………………………………………………………………………………………………………………………..

Quality Improvement vs. Research

• QI gets confused/overlaps with research because the best way to change the system isn’t always obvious

• different things may need to be tried and research also involves trying and testing things against each other

• Trying to figure out the “right” thing to do -> research

• Working to meet those expectations 100% of the time -> QI

Measurement

Conclusion

Sample

Environment in an Enumerative Study

(Research)

Environment in an Analytic Study

(QI)

Environment in Enumerative & Analytic Study

Sample

Sample

???

From The Healthcare Data Guide, Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

“Every system is perfectly designed to achieve the results it gets.”

Paul Batalban

Thus, if you want to improve your outcomes whether clinical or operational or administrative, “get a new system.”

Thinking Systemically

Thinking SystemicallyThe science of improvement

offers several

methodologies for fixing

failed systems.

Model for Improvement

Six

Sigma

Lean-

TPS

………………..……………………………………………………………………………………………………………………………………..

Six Sigma vs. LeanSix Sigma (Motorola)

make key processes reliable

reduce errors/variation through process control

observation alone, not sufficient

Lean (Toyota)

improve value, simplify system, reduce waste

reduce errors/variation through streamlining

observation is primary

“Essentially, all models are wrong … but some are useful.”

George E. P. Box (1919-2013) Professor Emeritus of Statistics

University of Wisconsin

Standardize the Approach

All approaches have unique strengths and

weaknesses

How will we know that a change is an improvement?

Plan

DoStudy

Act

What are we trying to accomplish?

What changes can we make that will result in improvement?

IHI Improvement Model

AIM

Run & Control Chart

Key Drivers & Interventions

Steps to Plan the Changes

Finding the optimal balance between ideal and practical

How will we know that a change is an improvement?

Plan

DoStudy

Act

What are we trying to accomplish?

What changes can we make that will result in improvement?

AIM

Run & Control Chart

Key Drivers & Interventions

Steps to Plan the Changes

Finding the optimal balance between ideal and practical


How will we know that a change is an improvement?Run & Control

Chart


Measures components for success

What you need to know:1. Before & After Analysis (Common Research Approach)2. Run Charts vs Control Charts3. Is the system stable?.... & does it matter?4. What are the improvement indicators?5. What type of control Chart is appropriate?

………………..……………………………………………………………………………………………………………………………………..

Before & After

vs.

Over time

From The Healthcare Data Guide,

Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

Before & After

vs.

Over time

………………..……………………………………………………………………………………………………………………………………..

Data displayed in a time series format

Control Chart advantages:

• Track performance over time

• Use when you don’t have sufficient historical data to

establish a baseline (starting point)

• Help you spot upward and downward trends

• Shows you a general picture of process performance or

outcomes

Run Charts

………………..……………………………………………………………………………………………………………………………………..

Data displayed in a time series format

Control Chart advantages:

• Indicates if process is stable or not

• Differentiates common cause vs. special cause variation

• Prevents two common mistakes

• adjusting the process when it should be left alone

• ignoring the process when it may need to be adjusted

Control Charts

So how does it differ from a run chart?

………………..……………………………………………………………………………………………………………………………………..

Median

No Upper or Lower Control Limits

Good

………………..……………………………………………………………………………………………………………………………………..

Use Mean

Add Upper or Lower Control Limits

Good

-1SD

-3SD

-2SD

+3SD

+2SD

+1SD

MEA

N

68.3%

95.5%

99.7%

Proportion of data by SD (σ) in a normal distribution

Standard Deviation

• a.k.a. Sigma (σ)

• avg. distance of data from mean

• technically:

-1SD

-3SD

-2SD

+3SD

+2SD

+1SD

MEAN

+1σ

+3σ

+2σ

-3σ

-2σ

-1σ

MEAN

TIME

LOWER CONTROL LIMIT (LCL)

UPPER CONTROL LIMIT (UCL)

Control Chart (Shewhart Chart)

• Statistical Process Control (SPC)

• Graphical time-series analysis


Chart



What you need to know:1. Before & After Analysis (Common Research Approach)2. Run Charts vs Control Charts3. Is the system stable?.... & why does it matter?4. What are the improvement indicators?5. What type of control Chart is appropriate?

………………..……………………………………………………………………………………………………………………………………..

Stable System = Absence of Special Cause

Common Cause variation

Special Cause variation

Types of Variation

………………..……………………………………………………………………………………………………………………………………..

Common Cause Variation

Inherent to every process - “background noise”

Affects everyone using process

Random - due to regular, natural, or ordinary causes

Predictable

Examples:

Daily hospital census

ALOS

………………..……………………………………………………………………………………………………………………………………..

Irregular, unusual causes, not inherent to process

Process “out of control”, not predictable

Assignable to a specific cause

Examples:

ED census during H1N1

Lab turnaround times when

machine broken

Special Cause Variation

Move to the New Tower

………………..……………………………………………………………………………………………………………………………………..

Special Cause Variation

Unfavorable

• New medical device results in many patients getting infections.

Favorable

• New employee takes x-ray differently from established method, resulting in far lower x-ray retake rate.

………………..……………………………………………………………………………………………………………………………………..

Stable System

• Process with only common cause variation

• Variation predictable within statistically established limits

• Outcomes in stable system may be “good” or “bad”; they

are predictable

Unstable System

• Size of variation from one period to next unpredictable

• Variation can be large or small, favorable or unfavorable

………………..……………………………………………………………………………………………………………………………………..

Jan 13

System Stability

PredictionPrediction

Unstable System

Stable System

Jan 13

Feb 13

Mar 13

Apr 13

May 13

Feb 13Mar 13

Apr 13

May 13

………………..……………………………………………………………………………………………………………………………………..

Importance of Baseline Data

Is change an improvement?

Without a baseline, hampered from beginning of project!

………………..……………………………………………………………………………………………………………………………………..

Improving Systems

Stable

Redesign by identifying

aspects of process to

change

Test changes using PDSAs

Implement successful

changes

Unstable

• Identify & learn from special

cause change

• Address special cause

change

o If undesirable, remove

o If desirable, add to

process

• Reassess system stability

Project Life Cycle Walk-Through

ADEs 21

36

28

24

30

26

22

23

21

19

21

24

23

21 7 8 11

15

13

16

15

14

Dispensed

Doses

16

1,4

84

14

9,8

29

15

2,4

82

13

6,1

11

14

7,2

90

13

0,0

85

12

8,3

05

13

4,7

26

13

4,4

34

13

5,0

83

12

7,4

02

13

0,8

11

14

6,4

58

13

6,1

66

14

4,7

79

13

4,1

43

12

9,7

23

13

0,3

53

12

8,1

05

13

3,4

12

13

0,5

18

14

0,2

03

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

2010 2011 2012 2013 2014 2015

AD

Es

pe

r 1,0

00 D

isp

en

se

d D

os

es

Adverse Drug Events (ADEs) -- Clinical Severity 4-9

Monthly ADEs Average / Baseline Control Limit(s)

Chart Type: u-Chart

Desired Directionof Change

Nationwide Children's Overall

July 2008 thru October 2011

5R Double Check / Vocera

ADE Huddles

Bar Code & ADEQC 2

PCA Pump Library

Alaris Pump Library

5R Stickies

Insulin Calc


Special Cause – Multiple Rules

ADEs 21

36

28

24

30

26

22

23

21

19

21

24

23

21 7 8 11

15

13

16

15

14 7 13 7 7 16

13

18 7 14

12

15 6 13

10 7 4 5 8 10 9 8 10 8 10 9 8 9 8 9 8 5 4 3 5 10 9 9 3 6 3 5

Dispensed

Doses

16

1,4

84

14

9,8

29

15

2,4

82

13

6,1

11

14

7,2

90

13

0,0

85

12

8,3

05

13

4,7

26

13

4,4

34

13

5,0

83

12

7,4

02

13

0,8

11

14

6,4

58

13

6,1

66

14

4,7

79

13

4,1

43

12

9,7

23

13

0,3

53

12

8,1

05

13

3,4

12

13

0,5

18

14

0,2

03

14

0,6

42

14

4,9

06

14

0,6

94

15

4,4

73

15

6,1

41

14

8,1

67

14

2,6

10

13

7,8

33

14

2,1

03

14

1,8

06

14

3,1

60

15

2,1

16

15

0,0

85

17

3,6

87

18

9,5

98

16

1,4

71

16

7,3

02

17

3,1

10

17

6,6

18

15

4,8

79

16

0,4

25

16

5,2

72

17

4,3

05

18

2,7

68

18

2,2

26

17

7,8

21

18

2,6

22

15

9,8

29

17

4,2

00

17

0,0

68

16

9,5

15

15

5,5

04

17

9,1

01

19

5,3

75

20

2,2

29

20

1,2

19

18

1,9

30

18

8,6

84

19

4,2

14

19

0,0

34

21

1,4

09

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

Ja

nF

eb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Sep

Oct

No

vD

ec

2010 2011 2012 2013 2014 2015

AD

Es

pe

r 1,0

00 D

isp

en

se

d D

os

es

Adverse Drug Events (ADEs) -- Clinical Severity 4-9

Monthly ADEs Average / Baseline Control Limit(s)

Chart Type: u-Chart


Nationwide Children's Overall

July 2008 thru March 2015

5R Double Check / Vocera

ADE Huddles

Bar Code & ADEQC 2

PCA Pump Library

Alaris Pump Library

5R Stickies

Insulin Calc

Amb MAR

EPIC Rev. / Amb Safety Champ

Med Rec Rev.

Prescriber Safety Champ

Insulin Calc Group

Ketone POCT

PCC Standard Admin

PCC Mandatory Ed


………………..……………………………………………………………………………………………………………………………………..

Published Dec 2014J Pediatr 2014;165:1222-1229


Chart




………………..……………………………………………………………………………………………………………………………………..

How do we know if a change was an

improvement?

Common Cause variation

Special Cause variation

Need to be able to separate

………………..……………………………………………………………………………………………………………………………………..

Rules to determine Special Cause

from Common Cause Variation?

1. Single point outside the control limits

2. Upward or downward Trend

3. Shift in the process level

4. 2 out of 3 consecutive points near the control limits (in outer 1/3zone)

5. Alternating Points (or Saw-Tooth) – 14 points alternating up and down

………………..……………………………………………………………………………………………………………………………………..

Single point outside upper or lower control limit.

OUTLIER (Special Cause)

Chart Type: u-ChartDesired

Direction

………………..……………………………………………………………………………………………………………………………………..

≥6 consecutive points up or down


Direction

TREND (Special Cause)

………………..……………………………………………………………………………………………………………………………………..

≥8 consecutive points above or below the center line


Direction

SHIFT (Special Cause)

………………..……………………………………………………………………………………………………………………………………..

2 of 3 points near upper or lower control limit.

2 of 3 consecutive points (Special Cause)


Direction

………………..……………………………………………………………………………………………………………………………………..

≥14 alternating data points

Alternating Points (or Saw-Tooth) (Special Cause)


Direction

………………..……………………………………………………………………………………………………………………………………..

Common or Special Cause?Good

………………..……………………………………………………………………………………………………………………………………..

Common or Special Cause?

Cause:

Why:

Action:

Special

Data point outside

control limits

Identify and eliminate

special cause

Good

………………..……………………………………………………………………………………………………………………………………..


Cause:

Why:

Action:

Common

Stable process but the

mean of the process is

too low

Change the process to

increase the mean or

tighten control limits

Good

………………..……………………………………………………………………………………………………………………………………..


Cause:

Why:

Action:

Special

More than 8 consecutive

points above the

previous mean

Conclude there has

been an improvement

and continue efforts

Good

………………..……………………………………………………………………………………………………………………………………..

Improvement and Reduced variation

Good

Class exercise


………………..……………………………………………………………………………………………………………………………………..Seen < 30' 1

0

10 9 11

11 8 9 8 9 12

12

10 9 9 12

10 8 13 9 7 10 9 6 9 10

10

10

12

14 8 9 9 10

14 7 13

11 6 14 8

Total Audits 25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01

Jan Feb Mar Apr May Jun Jul Aug Sep Oct

2014

Su

cc

es

sfu

l P

erc

en

tag

e

Seen within 30 minutes

Weekly Success Baseline Mean(s) Baseline Period Control Limits Goal (None)

Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Seen < 30' 10

10 9 11

11 8 9 8 9 12

12

10 9 9 12

10 8 13 9 7 10 9 6 9 10

10

10

12

14 8 9 9 10

14 7 13

11 6 14 8

Total Audits 25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 2

4

23

25

25

28

22

24

27

27

27

24

23

20

24

23

26

28

33

24

31

26

20

19

26

28

23

24

25

24

27

20

21

31

29

23

23

23

22

26

23

Total Audits 50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Seen < 30' 24

23

25

25

28

22

24

27

27

27

24

23

20

24

23

26

28

33

24

31

26

20

19

26

28

23

24

25

24

27

20

21

31

29

23

23

23

22

26

23

Total Audits 50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 1

7

15

16

11

14

14

16

11

13

12 9 13

14

12

12

16

13

12

22

12

13

14

15

12

16

12

15

10

12

16

15

12

11

14

12

14

17

15

12

16

Total Audits 30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 1 – point outside limits

Seen < 30' 17

15

16

11

14

14

16

11

13

12 9 13

14

12

12

16

13

12

22

12

13

14

15

12

16

12

15

10

12

16

15

12

11

14

12

14

17

15

12

16

Total Audits 30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 2

4

23

25

25

28

22

24

27

27

27

24

23

20

24

23

28

26

33

24

31

26

28

19

26

20

24

23

25

24

27

20

21

31

29

23

23

23

22

26

23

Total Audits 50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

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sfu

l P

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en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 5 – 14+ alternating points

Seen < 30' 24

23

25

25

28

22

24

27

27

27

24

23

20

24

23

28

26

33

24

31

26

28

19

26

20

24

23

25

24

27

20

21

31

29

23

23

23

22

26

23

Total Audits 50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 1

5

17

10

14

14

11

16

12 7 10

12

12

14

10 9 6 15

10

16

17

11

19

14

18

19

17

13

16

18

12

16

11

10

19

13

12

26

15

18

16

Total Audits 30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 1

5

17

10

14

14

11

16

12 7 10

12

12

14

10 9 6 15

10

16

17

11

19

14

18

19

17

13

16

18

12

16

11

10

19

13

12

26

15

18

16

Total Audits 30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e


Weekly Success Baseline Mean(s) Baseline Periods Control Limits Goal (None)

Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 7 6 1

1 8 10

11

10 8 9 8 9 9 12

12

12 9 15 8 9 10 8 8 4 7 8 9 10

12 9 15 9 9 13 8 9 13 9 11

11

12

Total Audits 50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

50

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

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sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 4 – 2 out of 3 in outer 3rd

Seen < 30' 11

10

12

15

13

11

14

15

11 8 15

17

11

12 9 14

18

16 6 12

13

16

12 9 14

10

10

13

11

14

15

12

13

14 8 11

13

16

14

16

Total Audits 25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..Seen < 30' 3

3

34

31

32

30

34

37

29

29

33

32

33

33

32

31

33

33

37

32

31

27

29

33

28

31

34

25

30

29

33

32

31

35

31

30

33

33

30

28

32

Total Audits 40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Seen < 30' 33

34

31

32

30

34

37

29

29

33

32

33

33

32

31

33

33

37

32

31

27

29

33

28

31

34

25

30

29

33

32

31

35

31

30

33

33

30

28

32

Total Audits 40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

40

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Su

cc

es

sfu

l P

erc

en

tag

e



Week

Chart Type: p-Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s

Minutes Spent on Phone

Minutes Wasted Baseline Average Baseline Period Control Limits Goal(s)

Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

160

180

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

0

20

40

60

80

100

120

140

160

180

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 1 – point outside limit

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

160

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..


0

20

40

60

80

100

120

140

160

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 3 – shift; 8+ points on 1 side of CL

0

20

40

60

80

100

120

140

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

20

40

60

80

100

120

140

160

180

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..


0

20

40

60

80

100

120

140

160

180

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

0

50

100

150

200

250

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

0

50

100

150

200

250

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

01

02

03

04

05

06

07

08

09

Jan Feb

2014

Min

ute

s



Day

I Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 9 6 6 6 9 3 1

1 7 7 6 8 5 10 8 12 3 10

12 2 8 9 5 6 4 3 9 8 9 2 7 11 6 10 8 5 5 5 5 3 12

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

14

16

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts

Medication Errors (all types)

Events Baseline Mean(s) Baseline Period Control Limits Goal (None)

Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Med Errors 9 6 6 6 9 3 11 7 7 6 8 5 10 8 12 3 10

12 2 8 9 5 6 4 3 9 8 9 2 7 11 6 10 8 5 5 5 5 3 12

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

14

16

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 7 6 5 3 8 3 3 7 7 4 3 5 6 6 5 9 4 1

0 4 8 6 3 4 3 8 4 6 3 4 6 3 5 7 10 7 2 6 6 4 3

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

14

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Med Errors 7 6 5 3 8 3 3 7 7 4 3 5 6 6 5 9 4 10 4 8 6 3 4 3 8 4 6 3 4 6 3 5 7 10 7 2 6 6 4 3

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

14

01

08

15

22

29

05

12

19

26

05

12

19

26

02

09

16

23

30

07

14

21

28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 1

7

14 9 7 13

10

14

15

12

17

11

13

10

13

14

10 9 25

11 9 14 8 12

10

14 7 11

14 6 10 5 17

12

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Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

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2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 1 – point outside limits

Med Errors 17

14 9 7 13

10

14

15

12

17

11

13

10

13

14

10 9 25

11 9 14 8 12

10

14 7 11

14 6 10 5 17

12

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Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

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11

18

25

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30

06

13

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27

03

10

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 2

7

17

21

17

20

24

29

26

21

21

23

29

17

15

23

22

20

22

19

22

13

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19

25

18

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24

28

16

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24

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26

30

19

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Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

10

15

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25

30

35

40

01

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15

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29

05

12

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26

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16

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14

21

28

04

11

18

25

02

09

16

23

30

06

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20

27

03

10

17

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..


Med Errors 27

17

21

17

20

24

29

26

21

21

23

29

17

15

23

22

20

22

19

22

13

22

21

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19

25

18

27

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28

16

23

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30

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Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

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30

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40

01

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29

05

12

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05

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21

28

04

11

18

25

02

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16

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30

06

13

20

27

03

10

17

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 2

5

22

28

25

21

20

24

15

29

25

23

22

22

24

19

15

20

32

20

33

11

13

10

11 5 6 8 9 10

18

13

11

14 6 12 8 12

11

19 7

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

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25

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01

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05

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02

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27

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10

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..

Rule 3 – shift; 8+ points on 1 side of CL

Med Errors 25

22

28

25

21

20

24

15

29

25

23

22

22

24

19

15

20

32

20

33

11

13

10

11 5 6 8 9 10

18

13

11

14 6 12 8 12

11

19 7

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

10

15

20

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30

35

40

01

08

15

22

29

05

12

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05

12

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02

09

16

23

30

07

14

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28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts


Events Baseline Mean(s) Baseline Periods Control Limits Goal (None)

Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 2

1

17 6 22

15

15

19

18

15

19

13

12

23

17

17

15 9 19

19

12

16

10

21

20

22

19

12

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17

12

14

18 7 13

25

19

11

14

14

22

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

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01

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14

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28

04

11

18

25

02

09

16

23

30

06

13

20

27

03

10

17

24

01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..


Med Errors 21

17 6 22

15

15

19

18

15

19

13

12

23

17

17

15 9 19

19

12

16

10

21

20

22

19

12

15

17

12

14

18 7 13

25

19

11

14

14

22

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

5

10

15

20

25

30

01

08

15

22

29

05

12

19

26

05

12

19

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02

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07

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02

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13

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27

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..Med Errors 1 4 4 9 1 6 5 4 8 5 4 3 8 4 9 7 5 5 8 3 1

0 7 7 5 4 4 8 7 3 7 4 6 7 7 4 5 3 10 6 7

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

01

08

15

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29

05

12

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05

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28

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30

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13

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27

03

10

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01


2014

Eve

nts



Week

Chart Type: c-Chart

………………..……………………………………………………………………………………………………………………………………..

No special causes

Med Errors 1 4 4 9 1 6 5 4 8 5 4 3 8 4 9 7 5 5 8 3 10 7 7 5 4 4 8 7 3 7 4 6 7 7 4 5 3 10 6 7

Week 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0

2

4

6

8

10

12

01

08

15

22

29

05

12

19

26

05

12

19

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02

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07

14

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28

04

11

18

25

02

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16

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30

06

13

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27

03

10

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24

01


2014

Eve

nts



Week

Chart Type: c-Chart

Questions?

5 Minute Break



Chart




………………..……………………………………………………………………………………………………………………………………..

Types of data

• In order to work with data in a QI project, it is necessary

to understand what types of data exist, and what is

necessary to work with each of them

• The type of data will affect how you collect, analyze,

and present your data

• The type of data you collect will determine how

easy/hard it is to collect vs. how much information

you get from your data

………………..……………………………………………………………………………………………………………………………………..

Types of dataFor QI efforts, five types of data have been shown to be

useful:

• Continuous measurements

• Temperature, weight, time

• Counts of observations

• Number of people, events, complaints

• Documentation of feelings/thoughts

• Was this better or worse?

• Ratings

• On a scale of 1-10 …

• Rankings

• From most to least important …

………………..……………………………………………………………………………………………………………………………………..

Data Types

Variable = continuous

• Measured on a continuum

• Takes on any value

• Require a measuring system

(inches, degrees)

• Accuracy depends on

measurement (feet vs. inches)

• Time, length, cost

Attribute = discrete

• Countable or classifiable

• Can put into bins

• Only takes on certain values

• Counts, characteristics,

categories

• Number of defects, percent

defective (good/bad), color,

pass/fail

………………..……………………………………………………………………………………………………………………………………..

Importance of data type

Classification:

% of infants born prematurely (< 37 weeks)

Count: # of infants born prematurely in Franklin County this year

Normalized count: # of premature infants per 1000 women living

in the county

Continuous:

Average gestational age of infants born prematurely

Infant Mortality in Ohio

From The Healthcare Data Guide, Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

U-Chart - CLABSIs

CLABSIs

15

9

13

9

15

7

76

35

36

28 4 3 2 3 4 4 3 4 3 7 3 1 2 1 1 0 2 4 4 7 5 1 0 0 2 1 1 2 4 4 3 2 4 3 6 1 2 4 2 2 3 2 3 1 4 4 5 2 3 4 7 4

Central Line

Days 30

,91

0

31

,68

3

34

,86

4

35

,54

9

37

,34

0

36

,75

2

34

,92

2

2,8

24

2,4

32

3,1

84

3,0

32

2,9

90

2,8

76

2,7

50

2,9

50

2,6

76

3,1

45

2,9

16

3,0

52

2,9

75

3,1

26

3,2

21

3,0

89

3,1

72

3,3

27

3,2

90

3,1

09

2,9

26

2,9

30

2,9

24

3,1

81

3,0

35

2,5

63

2,3

87

3,0

48

3,2

46

2,7

54

2,7

41

2,4

52

2,8

14

2,8

46

3,0

77

2,6

13

2,5

62

2,6

03

2,8

73

2,7

61

2,9

86

2,8

59

3,2

25

3,1

65

3,0

69

3,3

04

2,7

45

2,7

95

3,4

43

3,3

04

3,7

78

3,6

19

0.0

1.0

2.0

3.0

4.0

5.0

6.0

2004

2005

2006

2007

2008

2009

2010

Jan

Feb

Ma

rA

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vD

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Jan

Feb

Ma

rA

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Ma

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lA

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vD

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Jan

Feb

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rA

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Jan

Feb

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rA

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Jan

Feb

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vD

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Yearly 2011 2012 2013 2014 2015

CL

AB

SIs

pe

r 1

,00

0 C

en

tra

l L

ine

Da

ys

Central Line-Associated Blood Stream Infections

Monthly CLABSI Rate Baseline Average Control Limit

Chart Type: u-chart


Nationwide Children's Total Inpatient2004 thru April 2015

Feb '07 - CLABSI Reduction ScoringSystem Initiated

Jan '09 - NICU Compliance Score Increased

Sep '09 - MaxPlus Clear Caps

Jan '10 - Unit LeadershipAccountability & Committee

Mar '10 - CLABSI Reduction ScoringSpread to All Units

Jan '12 - Sage®CHG Cloths

Apr '11 - NACHRI MaintenanceBundle Hospital-wide

Nov '11 - Curos Port Protector

Dec '11 - MicroCLAVE® ClearConnector

………………..……………………………………………………………………………………………………………………………………..

P-Chart – Insertion Bundle Compliance

………………..……………………………………………………………………………………………………………………………………..

I-MR Chart – Patient LOS

-20

-10

0

10

20

30

40

50

60

05/0

6

06/1

4

07/0

7

07/2

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9

09/2

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01/0

8

01/1

6

03/0

2

02/0

9

02/1

1

02/1

6

06/1

8

09/0

3

11/0

8

2013 2014

LO

S (

Da

ys)

RMH NAS LOS

LOS (Days) Baseline Average Baseline Period Control Limits Goal(s)

I Chart

………………..……………………………………………………………………………………………………………………………………..

Xbar-Chart – Patient LOS

………………..……………………………………………………………………………………………………………………………………..

Let’s pause …

What questions do you have?

Some Additional Types of Control Charts

25-9

Other types of control charts for count/classification data:1. NP (for classification data)2. G-chart [# of event, items, etc. between rare events]3. Cumulative sum (CUSUM)4. Exponentially weighted moving average (EWMA)

5. Standardized control chart

Other types of control charts for continuous data:6. X-bar and Range7. Moving average8. Median and range9. T-chart (time between rare events)

10. Cumulative sum (CUSUM)11. Exponentially weighted moving average (EWMA)

12. Standardized control chartFrom The Healthcare Data Guide,

Provost & Murray

Warning: With these Advanced Control Charts, Can Not Use Standard

Rules 2-5 for Determining a Special Cause

SPC-

OK


Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

CLA-BSI – Pediatric ICU Data2004 – Sep 2013

> 1050 days without a BSI

CLABSIs # # # 8 3 6 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0

Central Line

Days 3,7

49

4,5

90

4,5

72

3,8

64

4,0

75

4,1

05

3,6

35

32

2

23

5

30

3

26

0

21

6

23

5

13

7

21

5

19

5

25

4

24

1

22

5

27

2

33

3

33

3

30

6

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1

30

3

26

2

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3

24

7

25

2

24

1

34

2

40

7

23

2

22

5

19

7

25

2

22

0

21

7

16

0

22

6

20

4

26

0

27

3

27

9

19

5

24

7

18

4

16

2

15

2

33

6

28

0

23

4

26

5

26

5

25

3

30

4

29

6

38

5

30

2

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

2004

2005

2006

2007

2008

2009

2010

Jan

Feb

Ma

rA

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Ma

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nJu

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Se

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No

vD

ec

Jan

Feb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Se

pO

ct

No

vD

ec

Jan

Feb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Se

pO

ct

No

vD

ec

Jan

Feb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Se

pO

ct

No

vD

ec

Jan

Feb

Ma

rA

pr

Ma

yJu

nJu

lA

ug

Se

pO

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No

vD

ec

Yearly 2011 2012 2013 2014 2015

CL

AB

SIs

pe

r 1

,00

0 C

en

tra

l L

ine

Da

ys

Central Line-Associated Blood Stream Infections

Monthly CLABSI Rate Baseline Average Control Limit

Chart Type: u-chart


H02B (PICU)2004 thru April 2015

Jul '09 - NACHRI Bundle

Sep '09 - MaxPlus Clear Caps

………………..……………………………………………………………………………………………………………………………………..

Monitoring Rare Events with SPC

• Geometric or G-charts

• Alternative to P-chart, C-chart or U-chart

• Plots event when it occurs not at the end of a time period

• Plots number of units (surgeries, insertions, admissions, etc.) between

events

• Calculation of control limits:

• g = number units between incidences

• CL = ĝ= median of g’s (center line)

• UCL = ĝ + 3[ĝ * (ĝ +1)]1/2 [UCL ~ 4 x CL]

• LCL = no LCL for G chart


Provost & Murray

NOTE: A 1/sqrt(x) transformation was used to calculate appropriate control limits. These limits may not be equidistant from the centerline.

0

0.5

1

1.5

2

2.5J

an

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2010 2011 2012

PD

A L

iga

tio

ns

PDA Ligations per month

Hours of Exercise Baseline Average Baseline Period Control Limits Goal(s)

Month

I Chart

………………..……………………………………………………………………………………………………………………………………..

117

Ligation every

5th patient

Calendar Days Between Events2

Patients Between Events

297 PatientsWithout a Ligation

0

50

100

150

200

250

300

350

400

05/2

9

06/0

8

06/1

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01/2

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04/3

0

2009 2010 2011 2012 2013 2015

Pa

tie

nts

Sin

ce

Pre

vio

us

PD

A L

iga

tio

n

Date of PDA Ligation

VLBW Patients between PDA Ligation 2009-Current

PDA Ligation Expected Baseline Median * Control Limit Baseline Period Goal (None)

Chart Type: g-Chart / t-Chart

Team Formation

Protocol Start

………………..……………………………………………………………………………………………………………………………………..

What can you do if your control charts

do not indicate evidence of special

cause variation despite multiple

interventions (PDSAs)?.

………………..……………………………………………………………………………………………………………………………………..

CUSUM

The cumulative sum statistic (S) is the sum of the deviations of the individual measurements (both positive and negative) from a target value, for example:

Si = S i-1 + (Xi - T),

Xi = the ith observation,

T = Target (often from historical average)

Si = the ith cumulative statistic.

Month %Sat(X) Target (Average) X-Target (Si) CUSUM CUSUM + Target

J-02 82 88.296 -6.296 -6.296 82

F 79 88.296 -9.296 -15.592 72.704

M 84 88.296 -4.296 -19.888 68.408

A 82 88.296 -6.296 -26.184 62.112


Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

Run Chart of

Patient

Satisfaction Data

% Patient Satisfaction Very Good/Ex.

70

75

80

85

90

95

100

J-

02

F M A M J J A S O N D J-

03


04

F M

Per

cent

Process Changes

CUSUM Chart: Patient Satisfaction

0

10

20

30

40

50

60

70

80

90

100

J-

02


03


04

F M

CU

SU

M

Process Changes

Cusum Chart of Patient Satisfaction Data Target = Avg = 88.296

CUSUM Chart


Provost & Murray

60

65

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95

100J

an

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2010 2011 2012

Pe

rce

nt

Go

od

or

Ve

ry G

oo

d

Patient Satifaction Scores

Hours of Exercise Baseline Average Baseline Period Control Limits Goal(s)

Survey Months

I Chart

PDSAs

Adapted from Provost & Murray, 2011

Run Chart

60

65

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80

85

90

95

100J

an

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2010 2011 2012

Pe

rce

nt

Go

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oo

dPatient Satisfaction Scores

Mothly Percent Good or Very Good Baseline Average Baseline Period Control Limits Goal(s)

Month of Survey

I Chart


PDSAs

60

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2010 2011 2012

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Patient Satisfaction Scores

Mothly Percent Good or Very Good Baseline Average Baseline Period Control Limits Goal(s)

Month of Survey

I Chart

-8

-11

-6-8

+2

-15

+4

-17

-7-6

+3+5

+1

+5

+3

+5

+3

+8

+2

+4+2

-1 -3

-6

-3

-6-6


CUSUM of Patient Satisfaction Data

PDSAs


………………..……………………………………………………………………………………………………………………………………..

Rational Subgrouping

with Shewhart Charts

………………..……………………………………………………………………………………………………………………………………..

Rational Subgrouping with Shewhart Charts

Shewhart (1931):

Obviously, the ultimate object is not only to detect trouble

but also to find it, and such discovery naturally involves

classification. The engineer who is successful in dividing

his data into rational subgroups based on upon rational

hypotheses is therefore inherently better off in the long

run than the one who is not thus successful

………………..……………………………………………………………………………………………………………………………………..

The concept of subgrouping is one of the most important components in using the Shewhart chart method.

Organize (classify, stratify, group, etc.) data from the process in a way that ensures the greatest similarity among the data in each subgroup and the greatest difference among the data in different subgroups.

Include only common causes of variation within a subgroup with all special causes of variation occurring between subgroups.

DG 5-12

What is Rational Subgrouping?

………………..……………………………………………………………………………………………………………………………………..

How to use Rational Subgrouping

Posing theories or questions and then grouping

data so subgroups answer that question

Put things in same group we suspect are more

alike so differences are contrasted between

groups

Use same data - look at it from different directions

Subgrouping by time, DRG, provider, treatment

method, operating suite, treatment location, etc.

Only ordering and stratification with Individuals

Chart (I chart)

• Blue = Bedside Silo

• Purple = Primary Closure

• Orange = OR Silo

NOTE: A 1/sqrt(x) transformation was used to calculate appropriate control limits. These limits may not be equidistant from the centerline.

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LO

S (

Da

ys

)Gastro LOS vs Closure Type (by Birthdate)

LOS (Days) Baseline Average Baseline Period Control Limits Goal(s)

Closure Type (by Birthdate)

I Chart

Bedside SiloOR Silo

Primary OR Closure

2010-2014

2010-2014

2010-2014

Primary ClosureOR SiloBedside Silo

100

90

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Closure

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S

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43

29.5

Boxplot of LOS

• Organized by Discharged date. Spans 8/2010 through 11/2014.

• Both the BS and PC are statistically different than the OR silo by LOS.

N = 21N = 16

N = 21

………………..……………………………………………………………………………………………………………………………………..

Application of Statistical Process Control in healthcare improvement: systematic review. Johan Thor, et al.

Qual. Saf. Health Care 2007;16;387-399

Six Limitations:

1 Sharing performance data in control chart format does not automatically lead to improvement in healthcare organizations

2 Statistical control does not necessarily equal clinical control nor desired performance

3 Cause and effect relationships are not always obvious, even if a change is identified with statistical confidence

4 Differences between patients may limit the appropriateness of combining data about their care onto one control chart

5 The ability of stakeholders to apply SPC correctly may be limited

6 Limitations regarding data for use in control charts


Provost & Murray

………………..……………………………………………………………………………………………………………………………………..

Application of Statistical Process Control in healthcare improvement: systematic review. Johan Thor, et al.

Qual. Saf. Health Care 2007;16;387-399

Barriers:

1. Lack of knowledge on how to apply SPC correctly

2. Data collection and analysis can be time consuming and costly

3. Constructing the most appropriate control chart can be difficult

4. Lack of access to reliable data in a timely fashion can be a barrier to real-time SPC application

5. Software problems can hamper SPC application


Provost & Murray

SPC Software Solutionshttp://www.capterra.com/spc-software/?utf8=%E2%9C%93&users=&commit=Filter+Results

“Inexpensive”

QI Macros SPC Software Excel $http://www.capterra.com/spc-software/?utf8=%E2%9C%93&users=&commit=Filter+Results

Moderately Expensive $$$

Minitab 17http://www.capterra.com/spc-software/spotlight/109731/Minitab%2017/Minitab

SQCPackhttp://www.pqsystems.com/quality-solutions/statistical-process-control/SQCpack/?WhereFrom=Capterra

Expensive $$$$

SAShttp://www.sas.com/en_us/home.html?gclid=CjwKEAjwqLWrBRC-_OaG-IfL0kASJAAbzKsVtUjyAF99atNQDyEfRijBqL_C6YzaNQWloxqL1KBethoCMcHw_wcB&keyword=sas&matchtype=e

SPSShttp://www-01.ibm.com/software/analytics/spss/

At least 25-30 other products on the market.

http://www.capterra.com/spc-software/?utf8=%E2%9C%93&users=&commit=Filter+Results

http://www.capterra.com/spc-software/?utf8=%E2%9C%93&users=&commit=Filter+Results

http://www.capterra.com/spc-software/spotlight/109731/Minitab 17/Minitab

http://www.pqsystems.com/quality-solutions/statistical-process-control/SQCpack/?WhereFrom=Capterra

http://www.sas.com/en_us/home.html?gclid=CjwKEAjwqLWrBRC-_OaG-IfL0kASJAAbzKsVtUjyAF99atNQDyEfRijBqL_C6YzaNQWloxqL1KBethoCMcHw_wcB&keyword=sas&matchtype=e

http://www-01.ibm.com/software/analytics/spss/

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