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9/10/2013
1
Basic Tools for
Data Analysis
Michael D. Chance MSM, MBA, MSQM, CPHQ, CQIA, CSSGB
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Objectives
1. Define data.
2. Explain some barriers to successfully
using data.
3. Explain the purpose and use of select
quality data tools
4. Explain common misconceptions and
limitations that arise from reporting
“averages” or from relying on a single
tool.
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What is Data?
Data is:
Factual information used as a basis for reasoning, discussion, or calculation.
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What Things are Considered Data?
• Medications Given (Dosages, Routes, etc.)
• Vital Signs / Symptoms
• Patient Characteristics (Age, Sex, Race, etc)
• Costs
• Therapies
• Staff Turnover, Working Hours, Ratios
• Times (Admission, Discharge, etc.)
• Anything we can document to measure performance.
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“We measure performance in
healthcare for two basic purposes.
We measure first as a basis for
making judgments and decisions…
Second, we measure as the basis
for future improvements”
Dennis O’Leary Former President,
JCAHO
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Barriers To Putting Data Into Action
• Don’t even know where to get data / info
• Paralysis by analysis
• No one is interested in it
• Incorrect interpretation of data
• Too complex to understand
• Defensiveness
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Stages of Coping with Data
•Stage I: “The data are wrong….”
•Stage II: “The data are right, but it’s not
a problem…”
•Stage III: “The data are right, it’s a
problem, but it’s not my problem…”
•Stage IV: “The data are right, it’s a
problem, it’s my problem…”
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How Do We Make Sense of Data?
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Quality Improvement Tools
“If the only tool you have is
a hammer, you will see
every problem as a nail.”
Abraham Maslow, 1966
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Purpose of QI Tools
•Describe and improve processes
•Evaluate process or output
variation
•Assist with decision-making
•Analyze data in a variety of ways
•Display information
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QI Tools Help Answer 5 Questions
1. Where am I at?
2. Where do I want to be?
3. How do I get there?
4. Am I still on the right path?
5. How well did I do?
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Partial List of QI Tools
Affinity diagram Arrow diagram Balanced scorecard Benchmarking Box and whisker plot Brainstorming Cause-and-effect/Ishikawa/fishbone diagram Cause analysis tools Check sheet Control chart Critical incident Data collection and analysis tools Decision matrix Design of experiments (DOE) Evaluation and decision-making tools Failure mode effects analysis (FMEA) Fishbone/Ishikawa/cause-and-effect diagram Five S (5S) Five whys and five hows Flowchart Force field analysis Gage repeatability Gantt chart Histogram House of quality Idea creation tools Impact effort matrix
Kano model Matrix diagram Mistake-proofing Multivoting Nine windows Nominal group technique Pareto chart Plan-do-check-act (PDCA) cycle or plan-do-study-act (PDSA) cycle Problem concentration diagram Process analysis tools Process decision program chart (PDPC) Project planning and implementation tools Quality function deployment (QFD) Relations diagram Scatter diagram Seven basic quality tools Seven new management and planning tools SIPOC+CM diagram SMART matrix Spaghetti diagram Stratification Success and effect diagram Survey Tree diagram Value stream mapping Voice of the customer table (VOCT)
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Purpose of QI Tools
However,
you don’t
have to use
EVERY tool
for every
problem.
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Histogram
Pareto Chart
Scatter Diagram
Run Chart
Control Chart
Basic Decision Making Toolbox
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Basic Decision Making Tools
•Bar Charts
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Histograms
• A bar graph that shows the distribution
of CONTINUOUS data
• A snapshot of data taken from a process
• Summarize large data sets graphically
• Compare process results to specification
• Communicate information to the team
• Assist in decision making
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Histogram Creation
Range No. of Occurrences
1 – 10
11 – 20
21 – 30
31 – 40
41 – 50
51 – 60
61 – 70
71 – 80
81 – 90
91+
Check Sheet:
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Histogram Creation
Histogram:
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Histogram Analysis
Absorption Time
Fre
qu
en
cy
403632282420
20
15
10
5
0
Histogram of Absorption Time
30
5
X
Variable N Mean St Dev Minimum Median Maximum
Absorption Time 100 30.009 5.002 13.759 30.694 42.076
Mean (or Average):
Standard Deviation:
Descriptive Statistics:
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Histogram Example
Asthma Related Study
• Of the 835 children who performed the
free running test, 2 experienced
considerable dyspnea, cough, and
wheezing that prevented them from
completing the test. There was a 10%
decrease in PEFR in 285 (34%)
subjects, a 15% decrease in 177
(21.2%), and a 20% decrease in 69
(8.2%).
• Mean decreases in PEFR during the
free running test are shown in the
histogram in Figure 2. The distribution
was skewed to the left and most
values were between 0 and -20. The
interval with the largest concentration
of results (approximately 20% of the
total sample) was between -5 and -10.
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• Histograms are a
snapshot in time and
show “distribution”.
• They do NOT show
trends over time.
Histogram Analysis
0
5
10
15
20
25
0
5
10
15
20
25
0
5
10
15
20
25
0
1
2
3
4
5
6
7
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Basic Decision Making Tools
•Pareto Chart
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What is a Pareto Chart?
•Bar chart arranged in
descending order of height
from left to right
•Bars on left relatively more important than those on right
•Separates the "vital few"
from the "useful many"
(Pareto Principle)
•80/20 Rule
-80% of the gain from 20% of
the categories
Budget Allocation
80
3827
14 115
0
20
40
60
80
100
Co
de
20
Co
de
10
Co
de
50
Co
de
30
Co
de
40
Co
de
60
Thousands
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Why use a Pareto Chart?
• Breaks big problems into smaller pieces
• Displays causes or problems in priority order
• Identifies most significant factors
• Shows where to focus efforts
• Allows better use of limited resources
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Case Study
Overall Rating
75%
80%
85%
90%
95%
100%
1-24 2-7 2-21 3-6 3-20 4-3 4-15 4-30 5-15 5-30 YTD
Goal Satisfaction
Where do we start?
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Example - Pareto Chart
Delay: 27
Communication: 15
Procedural / Competency Issues: 15
Staff Issues: 13
Environment – Cold: 12
Documentation/Admin/Billing: 11
Appointments: 7
Entertainment: 4
Location: 4
Parking: 4
Gown Size: 3
Other: 7
N = 122
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Example - Pareto Chart
27
15 15
1312
11
7
4 4 43
7
22%
34%
47%
57%
67%
76%
82%85%
88%92%
94%
100%
0
5
10
15
20
25
30
Dela
y
Com
m
Pro
c/Com
pSta
ff
Env
iron
Adm
inApp
t
Ent
erta
in
Loca
tion
Par
king
Gow
n
Oth
er
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Complaints Cumulative
N = 122
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Example - Pareto Chart
27
15 15
1312
11
7
4 4 43
7
22%
34%
47%
57%
67%
76%
82%85%
88%92%
94%
100%
0
5
10
15
20
25
30
Dela
y
Com
m
Pro
c/Com
pSta
ff
Env
iron
Adm
inApp
t
Ent
erta
in
Loca
tion
Par
king
Gow
n
Oth
er
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Complaints Cumulative
N = 122
Break point
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Example - Pareto Chart
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Basic Decision Making Tools
•Scatter Diagram
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Scatter Diagrams
A graph of paired data points plotted on a
table that helps identify the possible
relationship between the changes observed
in two different sets of variables.
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Why use Scatter Diagrams?
• Supplies the data to confirm a hypothesis that two
variables are related.
• Provides both a visual and statistical means to test
the strength of a potential relationship.
• Provides a good follow-up to a Cause and Effect
Diagram to find out if there is more than just a
consensus connection between causes and the
effect.
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Interpreting Scatter Diagrams
0
1
2
3
4
5
6
7
8
9
10
Pai
n F
ree
Tim
e
Amount of Drug Given
Does Amount of Drug Given Affect Pain-Free Time?
Correlation
0
0
1
2
3
4
5
6
7
8
9
10
Pat
ien
t S
atis
fact
ion
Rat
ing
Patient Waiting Time
Does Patient Waiting Time Affect Patient Satisfaction?
0
1
2
3
4
5
6
7
8
9
10
Min
ute
s O
ut
of
Res
trai
nt
Mg/Kg Drug Given
Does Amount of Drug Given Affect Minutes Patient Out
of Restraints?
0.00
5.00
10.00
15.00
20.00
0 50 100 150
Pre
ssu
re (
PSI
)
Temperature (F)
Pressure of Gas at Temperature
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Is there a correlation?
Interpreting Scatter Diagrams
97.998
98.198.298.398.498.598.698.798.898.9
9999.199.299.399.499.599.699.799.899.9100
0 10 20 30 40 50 60 70
Ou
tco
me
Input
Scatter Diagram
Outcome (y)
Linear (Outcome (y))
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Beware of False Correlation
Interpreting Scatter Diagrams
97.998
98.198.298.398.498.598.698.798.898.9
9999.199.299.399.499.599.699.799.899.9100
0 10 20 30 40 50 60 70
Ou
tco
me
Input
Scatter Diagram
Outcome (y)
Linear (Outcome (y))
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Example Scatter Diagrams
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Basic Decision Making Tools
•Run Chart
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What is a Run Chart?
A line graph of data points plotted in
chronological order that helps detect
special causes of variation.
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What is a Run Chart?
•A running record of process behavior over time.
•Requires no statistical calculations.
•Shows process behavior at a glance.
•Can detect some special causes.
•Time sequence is plotted on horizontal axis.
•Measure of interest is always plotted on the vertical axis.
•Center Line is the mean score (or median).
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Run Chart
Patient Restraint Rate
0.00%
0.10%
0.20%
0.30%
0.40%
0.50%
0.60%
0.70%
0.80%
0.90%
1.00%
Ja
n-0
2
Feb
-02
Ma
r-0
2
Ap
r-0
2
Ma
y-0
2
Ju
n-0
2
Ju
l-0
2
Au
g-0
2
Sep
-02
Oct-
02
No
v-0
2
Dec-0
2
Ja
n-0
3
Feb
-03
Ma
r-0
3
Ap
r-0
3
Ma
y-0
3
Ju
n-0
3
Ju
l-0
3
Au
g-0
3
Sep
-03
Oct-
03
No
v-0
3
Percen
tag
e R
est
ra
ined
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Example Run Chart
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Basic Decision Making Tools
•Control Chart
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What is a Control Chart?
A statistical tool used to distinguish
between process variation resulting
from common causes and variation
resulting from special causes.
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When people do not
understand variation
•see trends where there are no trends
•blame and give credit to others for things over
which they have little or no control
•build barriers, decrease morale, and create an
atmosphere of fear
•never be able to fully understand past
performance, make predictions about the future
and make significant improvements in processes
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Control Chart
•Elements of a Control Chart
Measurement
Scale
Horizontal Axis
x-axis
Time Units or Sequence
10
9
8
7
6
5
4
3
2
1
0
Centerline
1 5 10 15 20
Vertical Axis
y-axis Upper Control Limit
(UCL)
Lower Control Limit
(LCL)
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Why use Control Charts?
• Monitor process variation over time
• Differentiate between special cause and
common cause variation
• Assess effectiveness of changes
• Establish the basis for determining process
capability
• Communicate process performance
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Control Chart vs Bar Chart
90
73 74
3543
51 48
05
1015
2025
3035
404550
5560
6570
7580
8590
95100
Sun Mon Tue Wed Thu Fri Sat
Consecutive Patients
Tim
e i
n M
inu
tes
Daily Avg
Goal
Weekly Avg
N=250 WOW!
Goal <60
Avg = 59
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Control Chart vs Bar Chart
0
25
50
75
100
125
150
175
200
225
250
275
Consecutive Patients
Tim
e i
n M
inu
tes
Door to MD
Avg
N=250
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Control Chart vs Bar Chart
0
25
50
75
100
125
150
175
200
225
250
275
Consecutive Patients
Tim
e i
n M
inu
tes
Door to MD
Avg
Goal
UCL
LCL
N=250 Goal = <60
Mean = 59
Std Dev = 55
UCL = 224
LCL = 0
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Control Chart vs Bar Chart
0
25
50
75
100
125
150
175
200
225
250
275
Consecutive Patients
Tim
e i
n M
inu
tes
Door to MD
Avg
Goal
UCL
LCL
N=250 Goal = <60
Mean = 59
Std Dev = 12
UCL = 95
LCL = 23
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Example Control Chart
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Improvement Strategies
After making a Run Chart or a Control chart, what’s next?
The type of variation determines your approach:
SPECIAL CAUSE VARIATION?
If negative, eliminate it.
If positive, emulate it.
But don’t change the process!
COMMON CAUSE VARIATION?
If process is functioning at an unacceptable level, change the process!
Don’t “tamper” with individual data points!
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Resources
The Memory Jogger 2
- Goal QPC
The Quality Toolbox
- Nancy Tague
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Basic Tools for Data Analysis
Michael D. Chance
MSM, MBA, MSQM, CQIA, CPHQ, CSSGB
Quality Improvement Specialist
Phone: 832-824-1308 Email: [email protected]
