This document provides information on using data and charts in healthcare quality improvement work, specifically in a Six Sigma framework. It discusses types of charts like run charts and control charts and how they are used to analyze different types of variation in processes over time. Examples are given of charts created from real healthcare data on topics like patient diagnoses, IV fluid administration, and intracranial pressure during a quality improvement project. The document emphasizes how charts can help teams determine if a process is stable or if changes have resulted in improvement.

1. Six Sigma in
Healthcare:
Data Related to Time
Leslie W. Hayes, MD

2. Data Related to Time
 Time Series
 Run Chart
 Control Chart
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3. Time Series
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4. Run Charts
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5. What is a Run Chart?
A run chart is a graphical display of data
plotted in some type of order, that includes
a central tendency.
The horizontal (x) axis is usually a time
scale.
The vertical (y) axis is the quality
indicator studied.
Source: Perla et al. 2011. The run chart: A simple analytical tool.
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6. Why Use Run Charts?
Make process performance
visible
Analyze data for patterns (“signals”)
Determine if changes we make result in
improvement
Source: Perla et al. 2011. The run chart: A
simple analytical tool…
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7. Pujols Stats, Table 1.
Year Batting
Average
On Base
Percentage
2005 0.330 0.430
2006 0.331 0.431
2007 0.327 0.429
2008 0.357 0.462
2009 0.327 0.443
2010 0.312 0.414
2011 0.299 0.366
2012 0.285 0.343
2013 0.258 0.330
2014 0.263 0.330
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8. 1413121110090807060504030201
0.350
0.325
0.300
0.275
0.250
Year
AVG
Pujols Batting Average
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9. 1413121110090807060504030201
0.350
0.325
0.300
0.275
0.250
Year
AVG
Pujols Batting Average
LAA sign Pujols to
$240 million contract
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10. 1413121110987654321
0.46
0.44
0.42
0.40
0.38
0.36
0.34
0.32
Year
OBP
Pujols OBP
LAA sign Pujols to
$240 million contract
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11. Constructing a Run Chart
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12. Parts of a Run Chart
Centerline = .3325
1
3
2
4
5
Durham Bulls’
Team Batting
Avg. recorded on
Mon. of every
week during the
1994 season by
Rob Jackson,
team statistician
October 15, 1994
6
WEEK 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
AVG 300 333 325 332 340 345 350 340 341 345 349 354 350 344 333 325 318 305 298 306 310 315 310 318
7
2423222120191817161514131211109876543210
0.290
0.300
0.310
0.320
0.330
0.340
0.350
0.360
TEAM BATTING AVERAGE (1994)
Weeks of the Season
BattingAverage
1 TITLE 3 X-AXIS 5 CENTERLINE 7 DATA TABLE
2 Y-AXIS 4 DATA POINT 6 LEGEND
Source: www.au.af.mil/au/awc/awcgate/navy/bpi_manual/runchart.pps
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13. Step 1 - Construct the horizontal line (X-axis)
Step 2 – Construct the vertical line (Y-axis)
Step 3 – Plot the data points (20 minimum)
Step 4 – Connect the data points with a line
Step 5 – Determine the median and draw it on the
graph.
Step 6 - Provide a title and a legend
How to Construct a Run Chart
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14. RANK AVG RANK AVG RANK AVG
1 .298 9 .318 17 .341
2 .300 10 .325 18 .344
3 .305 11 .325 MEDIAN: 19 .345
4 .306 12 .332 (.332 + .333) / 2 20 .345
5 .310 13 .333 = .3325 21 .349
6 .310 14 .333 22 .350
7 .315 15 .340 23 .350
8 .318 16 .340 24 .354
How to Determine the Median
• Order data
• Calculate the median
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15. Consequences of not understanding
variation
See trends when there are no trends.
Blame or give credit for things that are not in our
control
Create a negative work climate
You will not be able to understand past
performance or make predictions about future
performance
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16. How do we know that a change is
improvement?
For data over time, run charts and control
charts are the best tools to determine if our
improvement strategies have the desired effect.
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17. Introduction to Control Charts
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18. Run Charts vs. Control Charts
Run charts are not Control charts.
Common cause vs. special cause
variation
Run charts cannot determine if the
process is stable….
Source: www.au.af.mil/au/awc/awcgate/navy/bpi_manual/runchart.pps
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19. Types of Variation
Common Cause
 Inherent in the process
 Due to regular or natural
causes
 Also known as random
variation
Special Cause
 Is due to irregular or
unnatural causes that
are not inherent in the
design of the process
 Results in unstable
process that is not
predictable
 Also known as non-
random variation
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20. Run Charts vs. Control Charts
Perla et al. 2011
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21. Run Charts vs. Control Charts
Perla et al. 2011
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22. Shewhart’s First Control Chart
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23. D M A I C

24. D M A I C

25. D M A I C

26. D M A I C

27. D M A I C

28. D M A I C

29. Control Charts
When, What and Why?
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30. Control Charts
 SPC Charts or Shewhart Charts
 Only time ordered data that differentiates
common cause from special cause variation
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31. Uses for Control Charts
 Appropriate response to data points
 Special cause vs. common cause variation
 Vital few X’s
 Understand (and predict) process capability
 Monitor interventions
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32. Common Cause Variation
 A normal part of the process
 Process inputs and conditions that contribute to
everyday variation
 Example: time it takes to drive to work
 Traffic lights
 Amount of traffic
 Waiting to make left turns
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33. © Larry V. Staker MD
Understanding Control Charts
TIME
Key
Process
Variable
MEAN
SPECIAL CAUSE VARIATION
SPECIAL CAUSE VARIATION
HUGGING THE MEAN
NUMBER OF RUNS
TRENDS
SHIFTS
COMMON CAUSE VARIATION
CERTAIN PATTERNS
UNDER THE CURVE
ALSO SIGNAL
SPECIAL CAUSE VARIATION
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34. Special Cause Variation
 Do not normally occur
 Result of a specific change in the process
 Example: causes of shifts in driving time
 An accident
 A flat tire
 A Holiday
 Snowmaggedan 2014
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35. Process Stability
 Stable process
 In-control
 Predictable
 Output due to common cause
 Unstable process
 Out of control
 Unpredictable
 Output due to special cause
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36. hh
Special
Cause
Variation
Special
Cause
Variation
Common Cause Variation
Understanding Variation
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37. hh
Special
Cause
Variation
Special
Cause
Variation
Common Cause Variation
Understanding Variation
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38. D M A I C
Understanding a Control Chart

39. QI Macros Healthcare IHI Rules
 Any point outside the CLs
 8 points in a row above or below center line
 6 points in a row increasing or decreasing
 15 points “hugging the mean”
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40. Special Cause Variation Fun
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41. QI Macros Healthcare IHI Rules
 Any point outside the CLs
 8 points in a row above or below center line
 6 points in a row increasing or decreasing
 15 points “hugging the mean”
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42. Leslie’s Rule of Sevens
 There must be 21 (7x3) sequential data points
 For every 21 data points:
 There must be between 7 and 14 runs
 There are no trends ≥ 7 in length
 There are never ≥ 7 points consecutively above or below the
mean (shifts)
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43. Questions?
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44. Frontline Work Using Control Charts
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45. TBI QI STUDY
GOAL: To determine adherence to an existing
COA algorithm and guideline for management of
TBI patients in the COA pediatric ICU. We will also
identify and explore opportunities for more
standardized care to improve both process and
outcomes in pediatric TBI patients.
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46. D M A I C

47. Survey
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48. Survey Results
AVERAGES ALL (27) MDs (11)
Familiarity score 7.18/10 8.00/10
Est. % Compliance 69.25-72.64% 57.73-66.36%
% Correctly answered
Tube Feeds Day
question
60.71% 63.64%
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49. Compliance
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50. Elements
Goals of Management
 CPP/MAP
 CVP
 Temp
 WATS
 Seizure prophylaxis
 Na
 Glc
 O2 and PCO2
 Hct
 Feed date
Other Elements of Interest
• Seizure Rx
• Sedation Rx
• Paralytics +/- EEG
• Pressors
• Initial/Extra Fluids
• Surgery/EVD
• CT Dx
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51. 554943373125191371
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Patients
percent
_
X=27.40%
UCL=74.25%
% Goals of Management met in patients with no EVD
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52. 252219161310741
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Patients
percent
_
X=27.83%
UCL=56.96%
% Goals of Management met in patients with EVD
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53. 252219161310741
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Patients
percent
_
X=53.22%
% of time ICP<20
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54. 191715131197531
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Patients
percent
_
X=36.20%
UCL=87.30%
% time CVP in range when measured
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55. 252219161310741
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
Patients
percent
_
X=74.91%
LCL=6.39%
% time Sodium >=140
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58. 918273645546372819101
40
30
20
10
0
Observation
IndividualValue
_
X=7.31
UCL=11.79
LCL=2.84
New algorithm implemented
goal <20
Pilot Patient ICP
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59. How else do you receive data?
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60. Red Yellow and Green
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61. Circle Reports
 “Unemployment rates drop as Americans stop
looking for work”
 Dallas Morning News
 “Unemployment falls to 7.5%”
 NPR
 “Unemployment stable at 7.5%”
 WSJ
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62. Jul 2013Jun 2013May 2013Apr 2013Mar 2013Feb 2013Jan 2013
9
8
7
6
5
Month
Rate
_
X=7.514
UCL=7.825
LCL=7.204
Monthly Unemployment Rate
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63. Jul 2013Jun 2013May 2013Apr 2013Mar 2013Feb 2013Jan 2013
9
8
7
6
5
Month
Rate
_
X=7.514
UCL=7.825
LCL=7.204
Monthly Unemployment Rate
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64. Control Chart Fun
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65. Questions?
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66. Inpatient Unit, Inter-professional QI
AKA: stuff you can improve
in your clinical area

67. Low Hanging Fruit
Just Do It
“logic and intuition”
Low Hanging Fruit
“incremental improvement of processes”
Strategy level decisions
“System redesign”

68. Count 45 42 37 35 32 31 24 22 41179135122112 81 63 48 45
Percent 4 4 3 3 3 3 2 2 416 12 11 10 7 6 4 4
Cum % 76 80 83 86 89 92 94 96 10016 29 40 50 57 63 68 72
Diagnoses
Other
SDH
LTR
RSV
Closed
Head
Injury
Spina
Bifida
M
enigiom
yelocele
Cardiac
Arrest
AM
S
Brain
Tum
or
Crainis
DKA
Septic
Shock
Status
Asthm
aticus
Respiratory
Distress
Respiratory
Failure
PSFSz
1200
1000
800
600
400
200
0
100
80
60
40
20
0
Count
Percent
Pareto Chart of Primary Diagnoses
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69. Count 45 42 37 35 32 31 24 22 41179135122112 81 63 48 45
Percent 4 4 3 3 3 3 2 2 416 12 11 10 7 6 4 4
Cum % 76 80 83 86 89 92 94 96 10016 29 40 50 57 63 68 72
Diagnoses
Other
SDH
LTR
RSV
Closed
Head
Injury
Spina
Bifida
M
enigiom
yelocele
Cardiac
Arrest
AM
S
Brain
Tum
or
Crainis
DKA
Septic
Shock
Status
Asthm
aticus
Respiratory
Distress
Respiratory
Failure
PSFSz
1200
1000
800
600
400
200
0
100
80
60
40
20
0
Count
Percent
Pareto Chart of Primary Diagnoses
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70. Count 57 54 18 5
Percent 42.5 40.3 13.4 3.7
Cum % 42.5 82.8 96.3 100.0
IVF Category OtherD5 1/2 NSD5 NSD5 LR
140
120
100
80
60
40
20
0
100
80
60
40
20
0
Count
Percent
Overall IVF
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71. 131118105927966534027141
60
50
40
30
20
10
0
Observation
Timeinhours
_
X=10.82
UCL=38.89
1
11
1
1
1
Time between Decision to transfer and actual transfer
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72. 131118105927966534027141
60
50
40
30
20
10
0
Case
Days
_
X=5.99
UCL=16.58
1
1
1
Hospital Length of Stay
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73. worse
After
Quality
worse Quality
better
Improvement and
standardization in
processes reduces
variation (narrows the
curve) and raises quality
of care for all (shifts
entire curve toward
better care).
Defining
Quality Improvement
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74. 19117215313411596775839201
25
20
15
10
5
0
Patients
Days
_
X=3.35
UCL=5.20
LCL=1.51
Baseline Data (Aug 2012-Sep 2014) New Process (Oct 2014 -current)
Hospital LOS before and After Quality Improvement Implementation
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X = 6.39

75. Pulling Define and Measure Together
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76. Aim Statement
 To reduce GI Clinic patient encounter time to a
mean of 70 minutes by September, 2009.
 Perception of the Problem: Registration takes
too long
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77. Team Members
Shehzad Saeed MD, Team Leader
Leslie Hayes MD, Facilitator, Statistical
Support
Tracy Cartwright RN, Clinic Team Leader
Tracy Williamson RN, GI Clinician
Velda Hill, Registration Coordinator
Lauren Rice CA
Suzanne Hammett CRNP
Kenneka Caitlin, Order Entry Staff
Beverly Barrett RN, VP Outpatient Services
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78. D M A I C

79. Please write down clipboard
clock time for each of the
following
1. Sign in _________________________
2. Registration done_________________
3. Called back & weighed_____________
4. Placed in room to wait______________
5. Nurse in room to see you____________
6. Doctor in room to see you____________
7. Discharge instructions done__________
8. Leave Clinic_______________________
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80. GI Clinic Encounter Time -Baseline
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
1 16 31 46 61 76 91 106 121 136 151 166 181 196 211 226 241 256 271 286 301 316
Patients
Time(minutes)
Visits Mean LCL UCL
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81. Normal Distribution Total Time
0.0000
0.0500
0.1000
0.1500
0.2000
0.2500
23
51
79
107
135
163
191
Value
Proportion
Survey
Expected
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82. Clinic Encounter Time Pareto
0
5
10
15
20
25
30
Discharge MD RN Register Triage
Time(minutes)
Discharge MD RN Register Triage
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83. Practitioner C Baseline Data
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
180.00
200.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Patients
Time(minutes)
Visits Mean LCL UCL
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84. D M A I C

85. Leverage Points
 Time until RN encounter
 Time until MD encounter
 Time until Discharge
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86. Calculating Process Sigma : Method 1
1
319
117
0.36677
63.323%
1.84
Current Process Sigma
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87. D M A I C