Healthcare Redesign:
Diagnostics
Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC
Bounty Brokers Pty Ltd
Disclaimers
All data published in this presentation is fictional except for published text/figures/tables.
Bounty Brokers P/L accepts no liability for any information provided or its use by participants.
"Portions of the input and output contained in this publication/book are printed with permission of Minitab Inc. All material remains the
exclusive property and copyright of Minitab Inc. All rights reserved."
Your Bio
• Introduction• Novice? • Expert?• Hands Up
– Table– Pie Chart– Bar graph– Run chart– Control chart– Control chart + others
Redesign Model: Measurement
Dip. Govt. PSPGOV50101
Today’s Program
Statistical Thinking
Statistical Methods
Improvement and Problem Solving
Objectives
Following the session, you should be better able to:• Value the use of dynamic data analysis and display• Understand that variation exists in all processes• Monitor a process over time to better understand it• Determine whether or not processes are ‘in control’ • See the effect of a change in a process• Provide a more accurate basis for prediction for the
purposes of planning, scheduling, budgeting, resource allocation, improvement, rewarding, etc…..
Focus of Capability Building
• Apply Deming’s principles• Monitor and improve core
processes• Monitor adverse events
– and the systems that produce them
• Learn to use statistical tools and techniques
• Turn data into information• React appropriately to variation
Statistical Thinkers Can…..
Statistical thinkers have skills to:
• Assign limited resources
• Determine if a change (decision) was effective
• Know when and if to ask “What happened”
• Understand the system before targets are set• Have confidence in making:
– More accurate predictions– Decisions to do something– Decisions to do nothing
5 Problems
1. Limited capacity to appropriately collect, analyse, interpret, report and act on data.
2. Static data display3. Focus on the person instead of the
process4. No Common Language5. Data torturing
Problem 1: Limited capacity to appropriately collect, analyse, interpret,
report and act on data
Type 1 error: Take action or adjust performance when not warranted
Risk: Tampering» Increases variation
within a process» Wastes resources» Impacts
psychologically
Type 2 error: Take no action when warrantedRisk: Molehill grows into a mountain
Risk: Wasting timeDuplicating collecting, analysing, reporting, reviewing, communicating and discussing “new” information that is already ‘known
Risk: Wasting energyby looking for explanations of a perceived trend when nothing has ‘changed’
Problem 2: Static Data Display
Oct-06Nov-06Dec-06
Jan-06Feb-06Mar-06Apr-06May-06Jun-06Jul-06Aug-06Sep-06
Category
Audiology2006 Occasions of Service
LOS 1
LOS 2
LOS 3
1 3.4 4.51.2 3.1 31.6 3 3.61.9 3.3 1.92 3.2 3.7
2.2 2.8 42.3 2.6 3.62.5 3.2 3.52.3 3.3 2.52.7 3.1 42.9 3.4 2.52.8 3 3.32.7 2.8 3.93 3.1 2.3
2.8 2.9 3.72.9 1.9 2.62.9 2.5 2.73.1 2 4.23.6 2.4 33.8 2.2 1.63.6 2.6 3.33.4 2.4 3.13.6 2 3.94 4.3 3.3
3.9 3.8 3.24.1 4 2.24.1 3.8 4.24.6 4.2 2.74.5 4.1 2.74.8 3.8 1.1
Problem 3: Inappropriate People
FocusFocus on the person rather than the
process, by:
• Ranking
• Setting inappropriate goals
• Blaming or giving credit for things over which staff have little or no control
Problem 4: Data Torturing
Data Torturing: When data analysis goes beyond reasonable interpretation of the facts.
Problem 5: No Common Language
Solution
Statistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and Methods
Why Statistical Thinking?
Many clinicians and other healthcare leaders underestimate the great contributions that
better statistical thinking could make toward reducing costs and improving
outcomes.
So convinced am I of the power of this principle of tracking over time that I would
suggest this: if you follow only one piece of advice from this lecture when you get
home, pick a measurement you care about and begin to plot it regularly over time.
You won't be sorry.“
D. Berwick 1995
Common Statistical Traps
• Average Value approach • Ranking • Poorly Presented Percentages• Trending• Smoothing • Tables• Circling
Average Value Approach
As the average is usually near to a midpoint set of data, one should expect to be:– above average about half of the time– below average about half of the time
Feel bad half the
time
Feel good half the time
Comparison to Averages
• Results in a characterization of either “above average” or “below average”
• Characterizes the world in a binary view:– “operating okay– “in trouble”
• Ignores the “dead band” of data on either side of an average
• Treats every fluctuation as important
2 Bucket Average
What is the difference between these 2 Patient
Groups?
Patient A: 50
Patient B: 250
Total: 300
Average: 150
Patient C: 140
Patient D: 160
Total = 300
Average = 150
Consider if this was waiting to see Dr. in ED?
What is the impact of these results on our patients?
Goals
The Use and Abuse of Numerical Goals
“I never use
them”
“ I always provide one and let other people figure
out how to achieve it”Results in:
•Numerical targets expressed as a single point
•Unfairly holding people (departments) accountable for results they are incapable of achieving
•Achieving goals at the expense of other parts of the system
•Falsifying numbers
Goal /Target/ Specification Oriented
ApproachTargets (voice of the customer)
should be based on:• customer expectations• benchmarking• competitive requirements • knowledge of those who will do the work• voice of the process – current system
capability
The Deceptiveness of Poorly Presented Percentage Data
A 2 patients, 1 died = 50% mortality B 20 patients, 1 died = 5% mortality C 200 patients, 1 died = 0.5% mortality
A 2 patients, 2 died = 100% mortalityB 20 patients, 2 died = 10% mortalityC 200 patients, 2 died = 1% mortality
Sample
Double the numerator with same sample size
You need to know the denominator (area of
opportunity)
Appropriateness of Trendlines
• Weight of a baby increases as it gets older
• Reduction in number of kilometres driven - predict that, within 12 months, will be driving minus 350 kilometres per month.
Trending: 6 Possible Sequences for 3
NumbersUpward Trend? Downturn? Setback?
Rebound? Turnaround? Downward trend?
Statistical Representation
of a Trend
15105
35
25
15
Index
C7
Run Chart: Statistical Representation of a Trend
• A sequence of SEVEN or more points continuously increasing or decreasing (SIX if < 20 observations)
• Omit entirely any points that repeat the preceding value: neither add to the length of the run nor do they break it.
Ranking
• Given two numbers if they are not the same, then one will be bigger.
• Ranking provides managers with a tool for choosing who goes, and in what order, as the ship begins to list
Statistical Thinking: Knowledge of Variation
Statistical Thinking
A philosophy of learning and
action based on the following fundamental principles:
»all work occurs in a system of interconnected processes
»variation exists in all processes»understanding and reducing variation are
the keys to success
4 Approaches to Analysis, Interpretation
& Prediction• Average Value Approach
• Specification Approach (goal / target)
• Run chart approach
•Shewhart Control Chart Approach
Solution: Our Scientific Method
is Statistical Process Control (SPC)
0 10 20 30 40
0
5
10
15
Consecutive Observation Number
Indi
vid
ual V
alue
Graph Title & Date
Mean=5.561
UCL=14.14
LCL=-3.016
0102030405060708090
1stQtr
2ndQtr
3rdQtr
4thQtr
East
West
North
The control chart is the tool of choice to appropriately display
variation
Control Charts
• Provides a formal method to detect trends• Provides credibility and rigour at minimum
costs• Accepted industry standard with a long
history• Provides performance objective criteria• Balances false alarms and failures to detect
– Analogous to circuitry in smoke detectors
Control Chart Approach
3 concepts:– Variation (special / common cause / structural /
off-target)
– Pattern matching– Decision Making (optional):
» Do something / Do nothing» Assign limited resources» Determine who is/are the “best”
Variation and Pattern Matching
• Review of process variation when viewed with the mean can help to spot patterns of variation which are:– highly improbable– non-random– unnatural– detectable and therefore
assignable
4 Types of Variation
• Off Target
• Common Cause
• Special Cause
• Structural
Variation
Variation is the constant
companion of any data
If there was no variation,
we would only need 1 number
Effective Measurement System
For accountability or Improvement:
• Objective, reliable, valid data• Control for confounding, e.g.. age, casemix
• Use of graphical presentations• Use of comparative data (over time and between hospitals)• Applying the scientific method when interpreting results
(hypothesis/experiment/test hypothesis)
• Indication of the magnitude of the expected statistical variation
Recommend/Mandate Control Charts
Who WhatAustralian Council on Healthcare Standards
(ACHS).
Clinical Indicator Users’ Manual 2006
Recommends that control charts be used to display
longitudinally, both the absolute numbers and rates
ACHS
Risk Management and Quality Improvement
Handbook. Version 1 2007.
Recommends control charts to display data and give information
for decision making.
NSWHealth.
Healthcare Associated Infection: Clinical
Indicator Manual 2008
NSW to report monthly rates with control charts and EWMA
NSWHealth.
CareSafe Performance Agreement 08/09
Incident Mgt. Indicator 13.3: Clinical RIBs reported to NSW
Health “Use control charts as presented in RIRC reports”
National Health and Medical Research Council
Pilot Program 2005-2007
Level 3-3 Evidence. Experimental study using a comparative
study without concurrent controls such as an interrupted time
series,
Therapeutic Advisory Group.
Indicators for Quality Use of Medicines in
Australian Hospitals. 2007
Promotes the use of control charts for 30 indicators
Recommend/Mandate Control Charts
Who WhatIndependent Pricing & Regulatory
Tribunal.
Framework for Performance Improvement
in Health September 2008
Recommendation 17 ”That NSW Department of Health
investigate models in other health services, such as 's
model of statistical process control charting, and
monitor their impact to see if they are appropriate to
adopt in the future.”NSWHealth and Centre for Healthcare Redesign
Statistical process control ce-learning course
SQUIRE.
Standards for Quality Improvement
Reporting Excellence.
Describes analytic methods used to demonstrate
effects of time as a variable (for example, control
charts) when reporting improvement projects, as
opposed to Introduction, Methods, Results, Discussion
(IMRAD).
Joint Commission (US) The healthcare accreditation agency requires that all
organisations submit control charts of their clinical
indicators
Bristol Royal Infirmary (BRI) Paediatric Cardiac Surgery
In a landmark article The UK Cardiac Surgical Register of mortality rates for children under one year old was analysed using control charts. The 1988-90 data showed that Bristol mortality rate was outside the control limits indicating special cause variation.
Five years later, data for the period 1991-1995 demonstrated that BRI was again above the upper control limit. Although external action to address concerns about paediatric cardiac surgery at BRI took place in 1998, monitoring using the control charts could have provided a basis for action some 11 years earlier.
Mohammed A Mohammed et al. Bristol, Shipman and Clinical Governance: Shewhart’s Forgotten Lessons. The Lancet, vol 357 February 10, 2001
Global Trigger Tool
“Plotting this data on control charts
will give you useful information
about trends and special causes
of variation in harm in your organisation”.
Griffin FA, Resar RK. IHI Global Trigger Tool for Measuring Adverse Events (2nd ed). IHI Innovation Series white paper, Cambridge, Massachusetts: IHI
Common Cause Variation
• Is an inherent part of every process:» chronic - often hidden
• Is random • Due to regular, natural, or ordinary
causes• Produces processes that are stable or “in
control”• If only common cause variation exists,
we can make predictions about the process
• Management “plans” for this
There are no lessons to be learned from comparing high dots to low
dots
8 Tests
for Specia
l Causes
"Portions of the input and output contained in this publicationare printed with permission of Minitab Inc. All material remains theexclusive property and copyright of Minitab Inc. All rights reserved."
Special Cause Variation
• Due to irregular or unnatural causes– acute, often out of the
blue - significant• Not inherent to the process• Affects some, but
necessarily all outcomes in the process
• Produces processes that are unstable or “out of control”
• The process is unpredictable
403020100
40
30
20
10
0
-10
-20
Observation Number
Ind
ivid
ua
l V
alu
e
I Chart for C1
1
222
1
333
Mean=7.027
UCL=20.99
LCL=-6.936
Institute for Clinical Excellence. Blood Transfusion Improvement Collaborative. Final Report. 2003
How Much Data?
Distance from the Baseline versus Time needed to Identify major Special Causes (MINITAB)
0
1
2
3
4
0 1 2 3 4 5 6 7 8 9 10
Months
Sta
nd
ard
De
viat
ion
s f
rom
B
ase
line
1 Point Outside 3S Control Limits
2 of 3 Outside 2 Standard Deviations
4 of 5 Outside 1 Standard Deviations
9 same side
6/7 points in a row up / dow n
Unlocking the Secrets of Simple Statistical
Methods
yx
nn
ss
yyxxyyxxyyxxnr
2211
1
Tools that Generate Knowledge for Improvement
Process/system Improvement Tools
Collaborative Work Tools
Planning & Analysis Tools
Statistical Thinking Tools
Flowchart Brainstorming Affinity Diagram
Run chart
Control Chart
Cause & Effect Nominal Group Technique
Force Field Analysis
Scatterplot
Pareto chart Multi-voting Prioritisation Matrices
Histogram
Degree of Difficulty
Three Uses of Control Charts
• Evaluate the past
• Evaluate the present
• Predict the range of values likely to see in the near future (where appropriate)
Average/Mean
Mean minus 3s
Mean plus 3s
The Standard Deviation
6 Sigma
6s
UCL
LCL
Mean
0 10 20 30 40
0
5
10
15
Consecutive Observation Number
Ind
ivid
ua
l Va
lue
Graph Title & Date
Mean=5.561
UCL=14.14
LCL=-3.016
Basic Control Chart
A run chart with:• average (green
horizontal line) • control limits -three
standard deviations from the mean (red horizontal lines):– upper control limit (UCL)
and lower control limit (LCL),
– or +3 or -3 sigma limits
(+ or - 3.0SL)
13121110987654321
12
10
8
6
4
2
0
Observations in time sequence (x axis)
Num
ber
bein
g M
easu
red (
y a
xis)
UCL=12
LCL= minus 1
_X=5.38
Title of GraphDate
-3SD -2SD -1SD +1SD +2SD +3SD
90-98%
60-75%
99-100%
3 sigma limits are notprobability limits - notbased on theory (thatrandom samples froman underlyingpopulation would givethis result by chance xtimes out of 100)
Empirical = observed.Only assumption is thatthese data are outputs of aprocess
Xbar
Empirical Rule
Control Limits are NOT Confidence Intervals
The control limits describe the natural variability of a process over time and are usually set to three standard deviations (SDs) or sigma.
Confidence limits of a distribution describe the degree of certainty that a given point is different from the average score (populations) – as when “outlier” performance is demonstrated using comparison data.
NEEDACTION
NEEDS NOACTION
TAKE ACTION I TYPE 1OVER ADJUST
Take action or adjustperformance whennot warranted
TAKE NOACTION II TYPE 11
UNDER ADJUSTNo action taken whenaction is warranted
Because tampering is such a bad thing, common control charts have limits set to produce:
•low risk of tampering (type 1 error)•moderate risk of under-controlling (type 11 error)
Torki
2 or 3 Standard Deviations?
What was the Question?
The Choice of Control Chart is determined by the:
• Research Question:• Number of falls (x)• Patients that fell
• Area of Opportunity
Area of Opportunity
MEASURE AREA OF OPPORTUNITY
Number of bacteria Agar plate
Number of referrals to Dr.
Day
Number of dents A car
Miles Gallon
Number of complaints Bed days
Annual mortality number Patients who could die each year
Number of vaginal births All births
Choosing a Control Chart
Minitab® Statistical Software
Variable /Continuous Data
Normal / non-Normal models
Variable Data - 2 Graphs
• Continuous data has no denominator to estimate variation - uses own variability:– Xbar-S Chart
»average subgroup standard deviations
– Xbar-R Chart»average of subgroup ranges
– I MR Chart»artificial subgroups created from individual
measurements
Common Cause Variation
121110987654321
6.6
6.4
6.2
6.0
Observation
Indiv
idual V
alu
e
_X=6.2717
UCL=6.6633
LCL=5.8800
121110987654321
0.45
0.30
0.15
0.00
Observation
Movin
g R
ange
__MR=0.1473
UCL=0.4812
LCL=0
Average LOS
6.7
6.6
6.5
6.4
6.3
6.2
6.1
6.0
5.9
5.8C1
Indiv
idual V
alu
e
_X=6.2717
UCL=6.6633
LCL=5.8800
Average LOS
Individuals Moving Range (IMR chart)
Individuals / X Chart
Individuals Chart: special cause variation
0 5 10 15 20 25 30 35 40 45
0
100
200
300
Consecutive patients
Min
ute
s
Time spent waiting
1
5
1
Mean =142.2
3.0SL=295.5
-3.0SL=-11.23
Length of Stay
Case study: Is there a Difference?
Balestracci
Variable n= Average LOS
Standard
Deviation
LOS 1 30 3.027 0.978
LOS 2 30 3.073 0.6680
LOS 3 30 3.127 0.8175
Case study: Appropriate Analysis?
Observation
Ind
ivid
ua
l Va
lue
30272421181512963
5
4
3
2
1
_X=3.027
UCL=3.595
LCL=2.458
1
11
111
1
16
11
1
222
2
22
22
13
11
11
1
11
I Chart of los1
Observation
Indiv
idual V
alu
e
30272421181512963
4.5
4.0
3.5
3.0
2.5
2.0
_X=3.073
UCL=4.119
LCL=2.028
5
51
5
5
5
1
1
6
6
5
6
11
I Chart of los2
Observation
Indiv
idual V
alu
e
30272421181512963
6
5
4
3
2
1
0
_X=3.127
UCL=5.841
LCL=0.412
I Chart of los3
Balestracci
Binomial Model
Defectives
‘p’ & ‘np’ charts
Data Categorised by the Binomial Model
• Count of occurrences and non-occurrences when the area of opportunity is known and equal, e.g.:
Head / tail Acceptable /not acceptable e.g. audits Infection/no infection Full bed/empty bed Operation / cancellation Working/broken Dead/alive• Patient fall/no patient fall• Either/or………Defective/Not………….Fraction
Any Percentage Data=P Chart
Chair-Step Limits
Sample
Pro
port
ion
252321191715131197531
0.6
0.5
0.4
0.3
0.2
0.1
0.0
_P=0.1613
UCL=0.2796
LCL=0.0430
Tests performed with unequal sample sizes
P Chart of Compliance all Elements of Care Bundle
Historical Control Chart
NSW Therapeutic Advisory Group Inc. Indicators for Quality Use of Medicine in Australian Hospitals. 2007
Where are the Control Limits?
Sample
Sam
ple
Count
18161412108642
1.50
1.25
1.00
0.75
0.50
__NP=1UCL=1LCL=1
Triage Category 1Proportion meet DoH Goal
2008-2010
Poisson Distribution
C charts
U charts
Area of opportunity may not be known
Run Chart or Control Chart?
464136312621161161
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Sample
Sam
ple
Count
Per
Unit
_U=0.3
Complaints per Thousand Bed days
Q: How high
is too high?
U Chart
464136312621161161
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Sample
Sam
ple
Count
Per
Unit
_U=0.3
LCL=0
UCL=0.7
Complaints per Thousand Bed days
Tests performed with unequal sample sizes
Is the Process Capable of Reaching Target?
Process NOT CAPABLE of Meeting Goal
Sample
Sam
ple
Count
2321191715131197531
60
50
40
30
20
_C=41.58
UCL=60.93
LCL=22.24
Weekly Complications
Goal
Process IS CAPABLE of Meeting Goal
90
85
80
75
70
65
60
55
Months
Indiv
idual V
alu
e
_X=83%
UCL=90%
LCL=77%
Goal: 80%
11
22
2
5555
Proportion Patients Would Recommend Happy HospitalMonthly Sample 50 Patients
Comparisons: ANOM
St. V
incen
ts
RPAH
Insti
tute
RNSH
Roya
l New
castle
POW
Oran
ge Ba
se
Nepe
an
John
Hun
ter
HKH
Conc
ord
4.0
3.5
3.0
2.5
2.0
1.5
1.0
Facilities
Sam
ple
Count
Per
Unit
_U=2.465
Units Transfused by Facility
Clinical Excellence Commission BloodWatch Program
Process Capability
Aim:Process
spread is smaller than and contained within the specification spread
20161284
LSL: 4.0 USL: 8.4 mmol/litre
LSL 4Target *USL 8.3Sample Mean 5.74845Sample N 97StDev(Within) 0.476507StDev(Overall) 2.6834
Process Data
Cp 1.50CPL 1.22CPU 1.78Cpk 1.22
Pp 0.27PPL 0.22PPU 0.32Ppk 0.22
Overall Capability
CapabilityPotential (Within)
Process Capability of BSL
Harold Shipman
In 2000, Harold Shipman, a general practitioner in Manchester (U.K.) was convicted of murdering 15 of his patients and of forging the will of one.
The clinical audit revealed clear evidence of a higher level
of death than would have been expected and not just in the more recent years. It was concluded that the excess of death did not appear to be explicable of grounds that Shipman’s practice served populations with markedly different demographic or health profiles.
Mohammed A Mohammed et al. Bristol, Shipman and Clinical Governance: Shewhart’s Forgotten Lessons. The Lancet, vol 357 February 10, 2001
Early Warning of Poor Performance
19971992198719821977199819931988198319781973
300
250
200
150
100
50
0
Year
Death
per
Thousa
nd P
atients
_U=98.3
UCL=205.5
LCL=0
Comparative GPs Shipman1
1
222
1
Harold Shipman Versus Comparative GP Death Rate/ 1000 PatientsFemales aged 75 Years or Above
1973 - 1998
Department Health: Harold Shipman's Clinical Practice 1974 - 1998
n=11
n=17.5
For women aged 75 years or over, it was predicted that
Shipman had 177 more deaths than expected.
A summary of the review of Shipman’s Clinical practice found Shipman issued 521 death certificates compared with the highest number of any of six comparison practitioners being 210.
The excess number of deaths were evidenced from the first few years of Shipman’s career as a GP; An excess of deaths occurred at home or in his practice premises.
How Will we Know that a Change is an
Improvement?
Q2: How will we know that a change is an improvement?
Teams use quantitative measures to determine if a specific change actually leads to an improvement. e.g. Proportion of reconciled medications.
Nolan et al
3 Ways to Get Better Numbers
1. Improve the System2. Distort the System3. Distort the Figures
»Outliers» Inliars»Darn Liars
Strategies for Getting Better Results
• Dis-aggregate:– e.g. LOS
• Stratify: aggregate and chop and
splice– e.g. Patient falls,
Cancelled OR cases• Experiment
– e.g. Waiting list management
• Standardisation
Disaggregate: LOS
• Pre-op
• Intra-op
• Recovery
• Post-op Ward
• Rehabilitation
Stratification: Slicing
FACTORS EXAMPLES – slice the data by….
WHAT Type of adverse event, Triage code, Cost
WHEN Month, Day of week, Time of day
WHERE Area health service, Facility, Location, e.g. sacrum
WHO Other GPs
Stratify: Comparing AHS
HGFEDCBA
90
80
70
60
50
Perc
ent
80%
Complaints Resolved within 35 DaysComparison of AHS Performance
BMKDoH
Pareto Principle
BackFin
gerChest
NeckThroat
HandE ye
Others
124 48 30 23 22 15 13 65
36.5 14.1 8.8 6.8 6.5 4.4 3.8 19.1
36.5 50.6 59.4 66.2 72.6 77.1 80.9 100.0
0
50
100
150
200
250
300
350
0
20
40
60
80
100
Location
Count
Percent
Cum %
Pe
rce
nt
Co
un
t
Paret Chart: Location of Staff Injury
80% of the trouble comes from 20% of the problem
Standardisation
Ganley H. Cameron M. Critical Paths – A Continuous Quality Improvement Approach to Improving Patient Care . The Quality Magazine. Australian Quality Council 1996.
Ganley HE, Cameron MJ. Momentum, Australian Quality Council. 2001
Ganley HE, Cameron MJ. Momentum, Australian Quality Council. 2001
Pro
port
ion
Mar-2007Mar-2006
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
_P=57%
UCL=78%
LCL=35%
1
1
Composite Compliance: Proportion Leg Ulcer Bundle ImplementedMarch 2006 & March 2007
Sample Size 6 & 7Tests performed with unequal sample sizes
Pro
port
ion
Mar-2007Mar-2006
1.0
0.8
0.6
0.4
0.2
0.0
_P=84%
UCL=100%
LCL=68%
Composite Compliance: Proportion of Leg Ulcer Bundle ImplementedMarch 2006 & March 2007
Sample Size 6 & 7
26%
O’Brien M, Lawton J, Conn C, Ganley H. Best Practice Wound Care. International Wound Journal. Wiley-Blackwell. 7 (4) 2011.
Causation:Feeling Challenged?
Sample
Sam
ple
Count
2321191715131197531
12
10
8
6
4
2
0
_C=1.43
UCL=5.03
LCL=0
1
C Chart of Damage Index
80757065605550
12
10
8
6
4
2
0
Degrees Fahrenheit
Dam
age Index
Challenger Data: Relationship between Temperature / Likelihood of Damage
The Aim is Improvement
Common cause variation reduced
Process improved
Special causes present
Process out of control - unpredictable
Special causes eliminated
Process under control - predictable
Adapted from R. Lendon
Take Away Messages
Plot the dots - make the variation visible • Include sample size information• Interpret smoothed data with caution• Wary of drawing conclusion from few data points• Employ subject matter expertise to understand
data• Take care in extrapolating data• Stop tampering• Be willing to think differently
Plot the dots - make the variation visible
Deming’s Common Principles for Action
A common focus on ....................................................................Quality
A common vision ................................................High Quality Service
achieved by fighting
A common enemy .............................................................Variability
using
A common method ............................................Process Improvement
and communicated through
A common
language................................Statistics
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