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TRANSCRIPT
Data Management for
Quality Improvement:
Tools You Should Be Using Now
Munish Gupta, MD MMSc
Heather Kaplan, MD MSCE
Neonatal Quality at Hot Topics
December 6, 2015
Disclosure Statements
Munish Gupta has no relevant financial relationships with the manufacturer(s) of any commercial product(s) and/or provider(s) of commercial services discussed in this CME activity.
Heather Kaplan has the following financial relationships with the manufacturer(s) of any commercial product(s) and/or provider(s) of commercial services discussed in this CME activity:
• Consultant for Vermont Oxford Network NICQ quality collaboratives.
We do not intend to discuss an unapproved/investigative use of a commercial product/device in our presentation.
We Want to Cover
1. Run charts
2. Control charts
What they are and how to use them!
Introductions
Munish Gupta
Heather Kaplan
All of you?
I am a…..
A. Neonatologist
B. Fellow or resident
C. Nurse practitioner, physician assistant
D. Nurse
E. Student
F. Other
A. B. C. D.
25% 25%25%25%
My experience level with data for quality improvement (QI)…. A. What’s QI?
B. Some, but I’ve never made a control chart
C. A fair amount, I make control charts now and then
D. A good amount, I use control charts fairly regularly
E. I’m a Jedi master A. B. C. D.
25% 25%25%25%
We assume you’re comfortable with…
Model for improvement
• Setting aims
• Developing measures
• Testing changes using PDSA cycle
Measuring for improvement
• Outcome, process, and balancing measures
• Measurement over time
The Model for Improvement
AIMS
MEASURES
CHANGES
Testing Changes
Figure from Institute for Healthcare Improvement (www.ihi.org)
A (Real) NICU Example
You would like to reduce the incidence of NEC
in your NICU.
You have identified two evidence-based
strategies for reducing risk of NEC:
• Increasing the use of human milk; and
• Standardizing feeding practices.
Key Driver Diagram
SMART Aims Primary Drivers Secondary Drivers/Interventions
Use of Human Milk (HM)
Process Measure: % of VLBW infants receiving HM at discharge
Decrease rate of NEC in VLBW infants by 25% by January 2014
Outcome measure: NEC rate per 100 VLBW days
Human Milk Initiation
Process Measure: % of VLBW infants w/ first feeding of HM Process Measure: time to first use of HM for oral care
Milk Continuation
Process Measure: # of days held skin-to-skin in first month
Standardized Feeding Protocol
Process Measure: % of infants who followed feeding protocol
Standardized feeding advancement
Standardized fortification
Donor milk use
First Measure: First Feeding as HM
Month First Feeding HM Total Infants
Jan-11 15 53
Feb-11 9 25
Mar-11 15 47
Apr-11 17 55
May-11 17 42
Jun-11 18 45
Jul-11 17 43
Aug-11 21 54
Sep-11 18 41
Oct-11 20 49
Nov-11 13 39
Dec-11 14 40
What’s good about this approach?
What’s missing?
Statistical Process Control Theory
History of SPC
Manufacturing origins
1920s - Walter Shewhart,
W.E. Deming (Bell Labs)
Easy for non-statisticians
detect process changes
Ramped up extensively during
WWII, post-war Japan, U.S. mfg
Used in all industries, including health care
Walter Shewhart
Statistical Process Control (SPC) and QI
Measurement over
time critical for QI
But all things vary
SPC: analysis of data
over time
Understand variation
Measuring Change in Variation
We are looking for improvement / change in
key data
But natural background variation in all things
we do – fact of life
Need tools to interpret process changes (in
data) versus natural variation in data
We would like to detect true change fast
Definitions
1. Common Cause Variation: Causes inherent as
part of usual process (good or bad).
2. Special Cause Variation: Specific causes not
part of usual process (good or bad).
3. Stable Process: Predictable variation within
natural common cause bounds.
4. Unstable Process: Both special and common
cause variation, variation unpredictable.
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Min
ute
s
Time to Get to Work, Daily
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
Min
ute
s
Time to Get to Work, Daily
Why is this Important
Type of variation type improvement action
Type of
variation
Reduce unnatural
variation
Reduce natural
variation, improve
basic process
Common
cause
Special
cause Establish stable
work process
Improve overall
outcomes
SPC Tools for Measurement
1. Run charts – minimal standard
2. Control charts
Keys:
Plot and evaluate over time
Interpret visually and statistically
Run Charts
Run Charts
Visual display of data over time
Center line: median of data
Can include annotations, goal line
Why Important
Interpreting Run Charts
Perla et al, BMJ Qual Saf 2011; 20:46-51
≥ 6 points ≥ 5 points
Too many or too few
Too few, too many runs
Perla et al, BMJ Qual Saf 2011; 20:46-51
Key Driver Diagram
SMART Aims Primary Drivers Secondary Drivers/Interventions
Use of Human Milk (HM)
Process Measure: % of VLBW infants receiving HM at discharge
Decrease rate of NEC in VLBW infants by 25% by January 2014
Outcome measure: NEC rate per 100 VLBW days
Human Milk Initiation
Process Measure: % of VLBW infants w/ first feeding of HM Process Measure: time to first use of HM for oral care
Milk Continuation
Process Measure: # of days held skin-to-skin in first month
Standardized Feeding Protocol
Process Measure: % of infants who followed feeding protocol
Standardized feeding advancement
Standardized fortification
Donor milk use
First Measure: First Feeding as HM
Month First Feeding HM Total Infants
Jan-11 15 53
Feb-11 9 25
Mar-11 15 47
Apr-11 17 55
May-11 17 42
Jun-11 18 45
Jul-11 17 43
Aug-11 21 54
Sep-11 18 41
Oct-11 20 49
Nov-11 13 39
Dec-11 14 40
How would you interpret this chart?
A. There is a shift – 6 points on one side of median.
B. There is a trend - 5 points increasing or decreasing.
C. There seem to be too few or too many runs.
D. There is an astronomical data point.
E. There is no special cause.
A. B. C. D.
25% 25%25%25%
How would you interpret this chart?
A. There is a shift – 6 points on one side of median.
B. There is a trend - 5 points increasing or decreasing.
C. There seem to be too few or too many runs.
D. There is an astronomical data point.
E. There is no special cause.
A. B. C. D.
25% 25%25%25%
Run Charts
“Minimum standard” for QI project data
(particularly to publish)
Can start with first few data points!
Need at least 10 data points to use rules for
detecting special cause
Simple to create (no software needed)
Can be used with all types of data
But… not as powerful as a control chart
Control Charts
Control Charts
The Shewhart chart (a.k.a. control chart) is a
statistical tool used to distinguish between
common cause and special cause variation
Chart Title
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Run Order
Me
as
ure Center Line
Upper Limit
7.86
Lower Limit
Provost, LP and Murray S. The Data Guide. 2008
Val
ue
of
Re
sult
Unit of Time (e.g. days, weeks, months, quarters)
A control chart is a run chart with some differences.
Run chart: Center line is the median.
Control chart: Center line is often the mean.
“Control limits” that reflect inherent variability in data – need to be calculated, but key to effectiveness
Mean
Upper Control Limit (UCL)
Lower Control Limit (LCL)
Slides Courtesy of Yiscah Bracha, PhD. CCHMC
From Run Charts to Control Charts
Relationship to Probability Theory
9181716151413121111
65
60
55
50
45
40
35
Time
_X=49.77
UCL=58.77
LCL=40.77
1
1
1
1
11
1
Constructing a control chart
Underlying data distribution dictates population parameters. Parameters dictate:
• Measure of central tendency (the “centerline”)
• Measure of variability standard deviation values for the upper and lower control limits.
Underlying distribution depends on type of data being observed (e.g., normal/Gaussian, Poisson, binomial, geometric)
Need to know what type of data you have to construct the proper type of control chart!
Continuous Data
1. Numerical value for each unit in a group
Discrete (Integer) Data
2. Classification: Presence or not of an attribute
3. Count: How many attributes occur in sample
Type of data
Sample Size
Type of Chart
Math (software)
Constructing Control Charts
Types of Data & Control Charts
Common cause probability
model Example
Dis
cre
te
Classification: Binomial
Parameter: p
Patient develops an SSI (Y/N)
Count: Poisson
Parameter: l
Number of catheter-associated
HAIs
Continuous
Normal
Parameters: m, s
Time to deliver thrombolytics
Healthcare Systems Engineering Institute
Which Control Chart To Use
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Control Charts for Attribute Data (1)
Classification data
• P chart: Percent of observations with a given attribute
• Measure of variability comes from binomial distribution
Example: Late-Onset Sepsis
Performance Metric: Late-Onset Nosocomial Sepsis
What it means operationally: Of all infants discharged in a given month, the percent that had at least one infection during their hospitalization.
Subgroup: The variable number of infants.
Summary stats:
P: The percent of infants with an infection
What we want to see: Percent of infants with a nosocomial infection.
Month
Infants
with Late
Infection Patients
Discharged
4/1/2006 10 61
5/1/2006 13 81
6/1/2006 19 94
7/1/2006 20 78
8/1/2006 7 77
9/1/2006 18 77
10/1/2006 16 84
11/1/2006 12 83
12/1/2006 15 76
1/1/2007 17 90
2/1/2007 16 73
3/1/2007 16 100
4/1/2007 13 75
5/1/2007 16 99
6/1/2007 12 88
7/1/2007 22 105
8/1/2007 16 91
9/1/2007 19 93
Example: Late-Onset Sepsis
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
4/1
/06
5
/1/0
6
6/1
/06
7
/1/0
6
8/1
/06
9
/1/0
6
10
/1/0
6
11
/1/0
6
12
/1/0
6
1/1
/07
2
/1/0
7
3/1
/07
4
/1/0
7
5/1
/07
6
/1/0
7
7/1
/07
8
/1/0
7
9/1
/07
1
0/1
/07
1
1/1
/07
1
2/1
/07
1
/1/0
8
2/1
/08
3
/1/0
8
4/1
/08
5
/1/0
8
6/1
/08
7
/1/0
8
8/1
/08
9
/1/0
8
10
/1/0
8
11
/1/0
8
12
/1/0
8
1/1
/09
2
/1/0
9
3/1
/09
4
/1/0
9
5/1
/09
6
/1/0
9
7/1
/09
8
/1/0
9
9/1
/09
1
0/1
/09
1
1/1
/09
1
2/1
/09
Month
Y Axis: Proportion of
Infants D/C with Late-Onset
Infection
Centerline: Average Proportion
of Infants with Late-Onset
Infection (over 45 months)
Control Limits
P-chart: % of VLBW infants with Late-Onset Infection
Centerline = p-bar = Average of the Statistic
UCL = CL + 3 σs
LCL = CL - 3 σs
100 n
dp
in
pppUCL
1003
in
pppLCL
1003
σs from the
binomial
distribution
Provost, LP and Murray S. The Data Guide. 2008 Slide courtesy of Terri Byczkowski, PhD, CCHMC
P-chart Calculations
Control Charts for Attribute Data (2)
Count Data
• Chart names contain
• “C”, as in Count
• “U”, as in Unit
• Points on chart represent
• Raw number of instances
• Number of instances per opportunities to observe
• Measure of variability comes from Poisson distribution
Example: Catheter-Associated Infections
Metric: Catheter-Associated infections
How it is observed: Data obtained from infection control as reported to CDC. Each day, number of catheters is counted. This is used to obtain catheter days each month. Number of infections (catheter-associated) occurring each month is also reported.
How often it is observed: For 24 months
Subgroup: Monthly
Summary stats: Unit Count: number of infections per opportunity (catheter day)
What we want to see: Number of catheter-associated infections per 1000 catheter days
Month #
Infections Catheter
Days
10/1/2008 8 2212
11/1/2008 15 3064
12/1/2008 15 3007
1/1/2009 6 2783
2/1/2009 14 2499
3/1/2009 4 2692
4/1/2009 8 2784
5/1/2009 14 2772
6/1/2009 9 2690
7/1/2009 10 3145
8/1/2009 16 3171
9/1/2009 12 3209
10/1/2009 11 3076
11/1/2009 17 2749
12/1/2009 7 2759
Example: Catheter-Associated Infections
Y Axis: CA-Infection Rate
per 1000 line days
Centerline: Average CA-
Infection rate (over 24 months)
Control Limits
U-Chart
Control Charts for Continuous Data
Chart names contain “X”, as in individual value or sample mean
• X-MR (individual value & moving range)
• Xbar-S (average & standard deviation)
Points on chart represent
• Individual values
• Averages
Measure of variability comes from normal (Gaussian) distribution
Example: C-section Decision to Incision
Performance Metric: Time to incision following decision for emergent c-section
What it means operationally: Minutes between decision to do c-section and time of incision
How it is observed: 10 charts sampled per week
Subgroup: The 10 charts sampled each week
Summary stats for the subgroups: X-bar: The average decision to incision time for
the 10 charts sampled each week S: The standard deviation of decision to incision
times for the 10 charts sampled each week.
What we want to see: process behavior over 30 weeks
Week
Decision to
Incision Time
(minutes)
X-barStandard
Deviation
40
45
36
49
50
35
34
50
39
47
49
52
36
49
50
42
38
50
44
50
44
52
36
49
46
41
36
52
43
51
1
2
3
42.5 6.4
46 5.6
45 6.1
Example from Benneyan, Int J Six Sigma and Competitive Advantage, 2008
C-section Incision: X-Bar & S Charts
S chart looks at the variation within subgroups. High variation within subgroups it makes it difficult to interpret variation between subgroups.
X-bar chart looks at the variation between subgroups.
Example from Benneyan, Int J Six Sigma and Competitive Advantage, 2008
Why two charts (Xbar & S)?
2 types of possible
process changes
(unnatural variation)
Mean or standard
deviation
Either can change
without the other
One chart to detect
each type of change
Change in mean
Change in SD
Why two charts (Xbar & S)?
Mean change, SD not SD change, Mean not
How to Interpret a Control Chart
Similar to run charts
Probability-based rules
Goal to detect non-random patterns
Rules designed to balance Type I (alpha
error, p<0.05) and Type II errors
“Rules” for Detecting Special Cause
TEST 1: 1 point outside outer control limit
TEST 2: 2 out of 3 points more than 2 SD from center line
TEST 3: 4 out of 5 points more than 1 SD from center line
TEST 4: Run of 8 points in a row on one side of center line
TEST 5: Trend of 6 points in a row increasing or decreasing
TEST 6: 14 points in a row alternating up and down
“Rules” for Detecting Special Cause (2)
Quiz: Interpretation
Points outside control limits?
Runs of 8 or more consecutive points on one side of the centerline?
Trends of 6 or more consecutive points increasing or decreasing?
Two of three consecutive points near the outer control limits?
Yes
Yes
No
Benneyan JC, et al. Qual Saf Health Care. 2003;12:458-464.
LCL
UCL
Yes
Quiz: Interpretation
This process appears to be in control, i.e. no special cause variation, only common cause variation.
Points outside control limits?
Runs of 8 or more consecutive points on one side of the centerline?
Trends of 6 or more consecutive points increasing or decreasing?
Two of three consecutive points near the outer control limits?
No
No
No
Benneyan JC, et al. Qual Saf Health Care. 2003;12:458-464.
No
ACTION
NO Special Cause
is occurring in
System
Special Cause is
occurring in System
Take action on
individual outcome
(treat special)
MISTAKE 1
OK
Treat outcome as
part of system;
work on changing
the system (treat
common)
OK
MISTAKE 2
ACTUAL SITUATION
Provost, LP and Murray S. The Data Guide. 2008
Using Control Charts to Guide QI
Key Driver Diagram
SMART Aims Primary Drivers Secondary Drivers/Interventions
Use of Human Milk (HM)
Process Measure: % of VLBW infants receiving HM at discharge
Decrease rate of NEC in VLBW infants by 25% by January 2014
Outcome measure: NEC rate per 100 VLBW days
Human Milk Initiation
Process Measure: % of VLBW infants w/ first feeding of HM Process Measure: time to first use of HM for oral care
Milk Continuation
Process Measure: # of days held skin-to-skin in first month
Standardized Feeding Protocol
Process Measure: % of infants who followed feeding protocol
Standardized feeding advancement
Standardized fortification
Donor milk use
For our measure of the percent of infants whose first feeding is human milk, what type of control chart(s) would you use?
A. U-chart
B. C-chart
C. P-chart
D. X-bar and S charts
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Any evidence of special cause?
How would you interpret this chart?
A. There is no special cause variation.
B. There are one or more points outside control limits.
C. There is a shift of 8 points above the median.
D. There is a trend of 6 increasing points. A. B. C. D.
25% 25%25%25%
Key Driver Diagram
SMART Aims Primary Drivers Secondary Drivers/Interventions
Use of Human Milk (HM)
Process Measure: % of VLBW infants receiving HM at discharge
Decrease rate of NEC in VLBW infants by 25% by January 2014
Outcome measure: NEC rate per 100 VLBW days
Human Milk Initiation
Process Measure: % of VLBW infants w/ first feeding of HM Process Measure: time to first use of HM for oral care
Milk Continuation
Process Measure: # of days held skin-to-skin in first month
Standardized Feeding Protocol
Process Measure: % of infants who followed feeding protocol
Standardized feeding advancement
Standardized fortification
Donor milk use
Measure: Time to Oral Care with HM
Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11 Sep-11 Oct-11 Nov-11 Dec-11
21 88 88 72 60 40 24 110 28 46 30 63
4 150 69 64 75 40 35 54 25 27 28 45
100 168 60 44 90 14 22 100 60 90 18 44
75 102 33 126 14 24 40 28 42 60 44 57
56 132 34 78 25 16 40 35 25 36 25 96
168 104 6 24 56 50 84 92 45 20 10 54
63 160 68 40 40 138 60 60 30 48 40 15
130 111 16 69 55 30 40 80 55 32 85 10
81 81 80 48 12 105 120 84 21 100 32 72
20 60 140 21 54 35 35 40 15 42 20 100
10 18 80 60 68 27 40 48 30 125 51 110
70 21 84 36 27 22 34 42 18 38 42 12
102 45 6 48 30 95 48 30 16 24 24 54
135 81 40 84 34 42 38 60 18 36 90 48
18 27 48 54 48 14 64 92 44 8 100 18
120 120 69 14 110 90 48 22 56 15 28
60 12 36 36 75 64 27 42 20 90
95 120 65 33 18 95 72 102 65 44
140 45 76 84 36 52 95 50 42
70 38 78 100 57 9
120 28 72 48
28 36
64
Time to Oral Care with Colostrum/Human Milk (hours)
For our measure of the time to oral care with human milk, what type of control chart(s) would you use?
A. U-chart
B. C-chart
C. P-chart
D. X-bar and S charts
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Focusing just on the X-bar chart, is there evidence of special cause variation?
A. There is no special cause variation.
B. There are one or more points outside control limits.
C. There is a shift below the median.
D. There is a trend of decreasing points.
E. B and C. A. B. C. D. E.
20% 20% 20%20%20%
How would you interpret this chart?
A. Neither mean nor
variation has improved.
B. Mean is improving but
variation is unchanged.
C. Variation has improved
(reduced), but mean is
unchanged.
D. Both variation and
mean have improved. A. B. C. D.
25% 25%25%25%
Why Control Charts Over Run Charts?
More sensitive / more powerful for detecting
special cause
Estimate capability of a stable process more
accurately predict performance
But… more difficult to generate
The Goal: Standardize then Improve
Standard
process
Improved
process
0
5
10
15
20
25
30
35
40
45
50
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58
Subgroup number
1 2 3 Sm
alle
r is
bett
er
Unstable
process
c/o J. Benneyan
Testing your New
Knowledge
What type of data is this? Time to first dose of surfactant among 10 VLBW infants intubated in delivery room.
A. Discrete - count
B. Discrete - classification
C. Continuous/variable
D. I wonder what the football score is…
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Time to first dose of surfactant among 10 VLBW infants intubated in delivery room.
What type of data is this? Number of unplanned extubations each month per number of ventilator days.
A. Discrete - count
B. Discrete - classification
C. Continuous/variable
D. Wow, I really can’t wait for the reception tonight…
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Number of unplanned extubations each month per number of ventilator days.
What type of data is this? Percent of infants admitted each month with initial temperature < 36.0 ⁰C.
A. Discrete - count
B. Discrete - classification
C. Continuous/variable
D. Are they really still talking up there?
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Percent of infants admitted each month with initial temperature < 36.0 ⁰C.
What type of data is this? Average initial temperature of all VLBW infants admitted each month.
A. Discrete - count
B. Discrete - classification
C. Continuous/variable
D. I wonder what the football score is…
A. B. C. D.
25% 25%25%25%
Which Control Chart To Use
Type of
Data Discrete /
Attribute (data is counted or
classified)
Continuous /
Variable (data is measured
on a scale) Count
(events/errors
are counted;
numerator can be
greater than
denominator)
Classification (each item is
classified;
numerator cannot
be greater than
denominator) Equal or
fixed area
of
opportunit
y
Unequal
or variable
area of
opportunit
y
Equal or
unequal
subgroup
size
Subgroup
size = 1 (each subgroup
is single
observation)
Subgroup
size > 1 (each subgroup
has multiple
observations)
C chart Count of
events
U chart Events per
unit
P chart Percent
classified
X and MR
charts Individual
measures and
moving range
X-bar and S
charts Average and
standard
deviation
Adapted from Provost & Murray, The Health Care Data Guide, 2011, and Carey, Improving Healthcare with Control Charts, 2003.
Average initial temperature of all VLBW infants admitted each month.
Review
Run charts: minimum standard for QI
Control charts: powerful tool for QI,
somewhat complex but not that hard
Other Topics We Cannot Cover
Topic Possible approaches
Other SPC Tools • Comparison or funnel charts
• Pareto charts
Sample Size • Sample size charts
Fixing and revising
limits
• Rules for “fixing limits” and
comparing new points to existing
mean vs. “updating limits”
• Setting new limits when evidence of
special cause
Other Topics We Cannot Cover
Topic Possible approaches
Rare Event Charts • G and T Charts (NEC example)
Non-normal data • Larger samples
• EWMA, Cusum charts
• Transformations, probability limits
Natural trending
of cyclical data
• Trend control charts
• Auto-correlated charts
Thanks!
References
For more information on this topic, see the following publications:
1. Benneyan, J.C., R.C. Lloyd, and P.E. Plsek, Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care, 2003. 12(6): p. 458-64.
2. Benneyan, J.C., The design, selection, and performance of statistical control charts for healthcare process improvement. Int J Six Sigma and Competitive Advantage, 2008. 4(3):p.209-239.
3. Carey, R.G., Improving healthcare with control charts : basic and advanced SPC methods and case studies. 2003, Milwaukee, WI: ASQ Quality Press. xxiv, 194 p.
4. Langley, G.J., R.D. Moen, K.M. Nolan, T.W. Nolan, C.L. Normal, and L.P. Provost, The Improvement Guide. 2nd ed. 2009, San Francisco, CA: Jossey-Bass. 490 p.
5. Lee, K. and C. McGreevey, Using control charts to assess performance measurement data. Jt Comm J Qual Improv, 2002. 28(2): p. 90-101.
6. Lee, K.Y. and C. McGreevey, Using comparison charts to assess performance measurement data. Jt Comm J Qual Improv, 2002. 28(3): p. 129-38.
7. Perla, R.J., L.P. Provost, and S.K. Murray, The run chart: a simple analytical tool for learning from variation in healthcare processes. BMJ Qual Saf, 2011. 20(1): p. 46-51.
8. Provost, L.P. and S.K. Murray, The health care data guide : learning from data for improvement. 1st ed. 2011, San Francisco, CA: Jossey-Bass. 445 p.