1.
Healthcare Six Sigma
Project
ED Wait Times and Service Quality
Michael J Floriani
12/2/2010

2.
Project Overview
A recent report from the Centers for Disease Control and Prevention indicates that over the past
decade, trips to the emergency department (ED) increased twenty percent, while the number of
available emergency centers fell by fifteen percent. Another study from the American Hospital
Association (AHA) indicated that sixty-two percent of hospitals feel they are at, or over operating
capacity. That number jumps to ninety percent when considering Level 1 Trauma Centers and larger
(300+ beds) hospitals.
These statistics are frighteningly familiar to many hospitals and patients. The pressures are mounting
and a faltering economy has swelled the ranks of the uninsured – those who often rely on the local ED
for primary care. Countless emergency departments are literally on life support as they try to cope with
the following:
Capacity issues
Workforce shortages
Preparing for, or responding to emergency threats such as bioterrorism and SARS, only increases the
strain on the system. In hospitals across the U.S., emergency departments face a similar story of delays
and dissatisfaction from both patients and clinicians.
Some hospitals, however, are finding new ways to overcome the challenges and are creating safer and
more efficient environments. Through a combination of Six Sigma and Lean, hospitals are targeting
critical aspects of patient flow, patient access, service-cycle time, and admission/discharge processes. A
growing number of hospitals are taking steps to identify and remove bottlenecks or inefficiencies in the
system. As a result, they are seeing a positive impact on patients, staff, and the bottom line.
By using the principles in the Villanova Six Sigma Black Belt course, the objectives of the project are:
Decrease door to doctor time
Decrease the patient’s total length of stay (LOS)
Decrease the number of patients who leave the ED without being seen as a result of being tired
of waiting.
Last year, the hypothetical hospital received 43,800 patients into its emergency department with 6.3%
leaving without treatment – essentially because of their dissatisfaction with the wait time.
The nation’s emergency care network must be strong – not only to maintain its ability to serve basic
community needs, but also to ensure it will have the necessary capacity and processes in place to
respond quickly during a crisis.

3.
Project Charter (Define)
The project charter of this Six Sigma/Lean healthcare project establishes the first phase of the DMAIC
process by defining the problem and other key elements to motivate the team and ensure the project
meets the stakeholders’ needs. Additionally, it establishes “buy-in” of the project.
The project charter is composed of various elements; however, the key elements include:
Business Case
o The sponsor must know what the project is about and how it impacts the strategic
objectives of the organization. This business case statement should be limited to one to
two sentences.
Problem Statement
o The sponsor must be sold on why we need to do this project and needs a short, to-thepoint compelling reason why we need to do this. It is in the problem statement, we
'sell' the need for the project with specific and measurable data.
Goal Statement
o This includes the target improvement for this project and target date.
o Six Sigma projects should target the project for an initial 50% improvement as a best
practice.
Project Scope
o The project scope identifies the boundaries of the project to include what is and is not
included as part of the project. Assumptions and constraints may also be included in
the scope statement which affect the budget or project team.
The following project charter deliverable has been established for this healthcare case:
Business Case
o Paoli Hospital’s emergency department is facing increased patient volumes, constrained
capacity and employee shortages as it moves towards a Level 1 Trauma Center.
o Excessive delays and length of stays negatively impact patient outcomes and satisfaction
requiring us to initiate this project to improve key ED metrics.
Problem Statement
o Since 2009, patients who left the emergency room without waiting due to delays,
accounted for 6.3% of a total 43,800 ED visits. This 50% higher than desired increase
resulted in 2,759 “balked visits”, lost hospital revenue, negative hospital reputation and
poor emergency room preparedness.
Goal Statement
o The project will commence June 1, 2010 and meet all objectives six months prior to the
hospital becoming a Level 1 Trauma Center, currently planned for June 2011 and result
in increased patient satisfaction and improved financial performance.
o Goals for the project include 1) Improving “door to doctor time” by 50%, 2) Decreasing
total LOS by 20% and 3) Reduce “unseen” patients by 75%.

4.
o
A project plan will be provided to management by June 15 outlining tasks involved, risk
plan and communication plan. Weekly status reports will be distributed and a midphase and final phase implementation plan will be presented to executive management.
Project Scope
o Registration process, ED flow process, lab process and discharge process will be included
in this project.
o Included in this scope is the time from patient entry to the ED, either by foot or by
emergency transport, and ends when the patient is officially discharged by the
physician.
o Outside of the scope is the admission process for patients admitted due to severity of
illness.
o Not included are delays attributed to patients, patient families or other members
outside of hospital personnel.
Baseline Sigma (Measure)
The baseline sigma establishes the “original state” sigma before any process improvement initiative
is implemented.
Because the defect, the number of potential patients leaving the hospital’s ED, is attribute data
(only one possible defect per opportunity – a two state condition in which the patient either stays or
leaves ), the opportunity to calculate uses the value “1” for opportunity; hence:
Units:
Hospital ED visits which, according to the case, are 43,800 visits per year.
Defects:
6.3% or 2,759 people leaving the hospital ED without being seen by a doctor.
DPMO or DPMU result in same because of the 1 opportunity and therefore the formula and result is
2759/(1*43,800) = .062991 or 63,000 DPMO. This equates to 3.03 Sigma with 1.5 shift.
SIPOC (Define)
The following SIPOC represents a high-level identification of the “current state” process to observe
the major process elements. This includes the 1) Suppliers 2) Inputs 3) Processes 4) Outputs and 5)
Customers associated within this project.
The SIPOC begins by identifying the key steps within the process by listing five to seven process
elements. Once identified, other areas are listed associated with the project’s SIPOC.
Further breakdown of sub-processes can be achieved later within this project; however, the purpose
of the SIPOC. The SIPOC diagram helps to identify the process outputs and the customers of those
outputs so that the voice of the customer can be captured.

5.
Suppliers
Inputs
Patient
Medical Records
Triage Nurse
Patient Symptoms
Registration Clerk
Nurse
Rx Information
Insurance Data
ED
Doctor/Hospitalist
ED Activity Log
Room Data
Emergency Room SIPOC
Process
Outputs
Customer
Patient Arrival to
ED
Triage patient
Discharge
Documents
Prescriptions
Patient
Register Patient
Assign Patient to
Room
Assign Physician
Physician Notes
ED Activity Log
ED
Doctor/Hospitalist
ED Manager
Orderly/Nurse/Aid
Empty ED Room
Lab Personnel
Physician Examines
Patient
Physician Orders
Tests
Physician Treats
Patient
Physician
Discharges Patient
Pareto Diagram (Analyze)
The principle is based on the unequal distribution of things in the universe. It is the law of the
"significant few versus the trivial many." The significant few things will generally make up 80% of the
whole, while the trivial many will make up about 20%.
The purpose of a Pareto diagram is to separate the significant aspects of a problem from the trivial
ones. By graphically separating the aspects of a problem, a team will know where to direct its
improvement efforts. Reducing the largest bars identified in the diagram will do more for overall
improvement than reducing the smaller ones.
Based on information provided by those who entered the hospital, the following reasons were
stratified for leaving:
Got tired:
Not necessary:
People Waiting:
Doctor Treatment:
Staff Treatment:
Environment:
Went Elsewhere:
Ignored Me:
Too Expensive:
Had to Leave:
6
4
4
3
2
2
1
1
1
1
From the above data, the project team should focus, at most, on the first six reasons while accepting
others as the “useful many”. This information is substantiated from the Pareto Chart below.

6.
Expected Variation (Analyze)
During the past month, the patient wait times were logged and are noted within this document. All
figures are in minutes with a wait time operational definition of the patient entering the ED facility
until brought into an ED room. All values are rounded to the nearest minute.
24
17
18
28
27
28
27
18
24
22
11
17
27
22
8
17
27
21
40
17
26
22
23
23
18
17
17
17
From the thirty-one observations, the following results are provided:
Average wait time:
21.1935484 minutes
Standard Deviation:
6.9013011
Range of Expected Variation
o Lowest Point:
0.4896451
o High Point:
41.8974517
Histogram to determine Normal Distribution or Assignable-cause variation is normally
distributed as shown on the following histogram by the bell curve.
5
31
18

7.
Patient Wait Times
12
10
Frequency
8
6
4
2
0
5
12
19
26
33
More
Minutes
Stem and Leaf Diagram (Analyze)
The Stem and Leaf diagram preserve the actual data values compared to the histogram which
categorizes values into bins. To get a visualization of the variation in wait times over the past
seventy days, it can be determined by this diagram, if assignable cause variation exists.
The values over the past seventy days are indicated within this document as shown below:
16
16
17
37
47
32
48
9:::
8:::
7:::
6:::
5:::
4:::
3:::
2:::
1:::
0:::
5
0
1
3
0
4
2
0
0
6
2
5
4
0
4
5
0
1
5
0
5
7
0
3
1
6
8
0
4
21
18
75
15
17
13
47
11
47
38
17
20
49
19
16
26
17
65
15
17
48
16
44
48
45
50
49
63
17
22
10
18
51
14
80
6
49
48
47
48
52
46
48
47
20
71
47
50
95
47
20
50
35
21
46
48
20
64
16
44
82
51
58
1
6
2
7
8
7
7
7
7
7
7
8
8
8
8
8
8
8
9
1
5
1
5
2
6
6
6
6
6
6
7
7
7
7
7
7
8
8
9
9
9

8.
The above Stem and Leaf diagram shows that this data is not normally distributed. As a
recommendation, the project team should focus on wait times of forty minutes and more. The
number of wait times is fairly evenly distributed at the point of >= 40 minutes and <40 minutes.
Design of Experiment (Improve)
The Design of Experiments (DOE) approach has been recommended to hopefully realize an
improvement considering variables that impact wait times. The project team brainstormed five
possible reasons for the delay to include the following:
1.
2.
3.
4.
5.
Staff size
Order of treatment
Treatment method
Tracking software
Waiting room temperature
Using a statistical software package for Fractional Factorial Designs, the effects of the five factors can be
achieved in as little as eight experiments with no interactions of these factors.
The factors or each experiment were:
A
B
C
D
E
Level (-)
8
FIFO
Iterative
Product A
68 Degrees
Staff Size
Order of Treatment
Treatment Method
Tracking Software
Waiting Room Temp
Level (+)
16
By Priority
All at Once
Product B
75 Degrees
The breakdown of the experiments is as follows:
Trial #
1
2
3
4
5
6
7
8
Staff
1
-1
1
-1
-1
1
1
-1
Order
1
-1
1
-1
1
-1
-1
1
Method
-1
-1
1
1
1
-1
1
-1
Software
-1
1
1
1
-1
1
-1
-1
Temp
-1
1
-1
-1
1
1
-1
1
(Y) Wait Time
9
7
25
28
26
8
28
6
To determine the correlation, average wait times must be taken for various conditions. For the first
experiment for staff size, the average wait time for a staff size of 8 and 16 was as follows:

9.
Staff Size of 8: (7+28+26+6)/4 = 16.75
Staff Size of 16: (9+25+8+28)/4 = 17.50
Charting these results in the following:
30
25
Staff Size
30
25
20
20
15
15
10
10
5
5
0
Treatment
0
30
25
Method
30
25
20
20
15
15
10
10
5
5
0
Software
0
30
25
Temperature
20
15
10
5
0
Since the objective is “Less is Better” relative to wait times, the following should be achieved:
1. Immediately make an adjustment to room temperature from the 68 degree setting to achieve a
more comfortable setting. This has the most significant impact, based on the DOE data, for a
reduction in wait times.
2. Begin prioritizing patients as this has reduced the average wait time.
3. While the average wait time has decreased slightly using product B, this should be used as the
other software may contribute to not effectively tracking patients; thus, increasing wait times.
This software should be evaluated to ensure it prioritizes patients.

10.
Scatter Diagram (Improve)
The following data and Scatter Diagram are used to determine the correlation between the volume
of patients and the impact on the number of patients that leave without treatment (LWT). This was
believed by the project team, and as such, the data identifies the correlation.
Number in for
treatment per
specific day
Leave without
treatment
incidents
172
4
132
130
6
2
206
199
223
201
4
6
4
8
169
135
7
5
200
189
110
203
189
224
197
188
125
199
194
207
3
7
8
6
5
8
4
8
2
6
8
7
9
8
7
6
5
Series1
4
Linear (Series1)
3
2
1
0
0
50
100
150
200
250

11.
Correlations of 1.0 to -0.7 indicate a strong negative association while a correlation of -0.7 to -0.3
indicates a weak negative association. The correlation of -0.3 to +0.3 indicates little or no association
and +0.3 to +0.7 weak positive association. A +0.7 to +1.0 indicates strong positive association.
The Scatter Diagram does not indicate any correlation with a correlation coefficient of .2256432
(very weak) and is positive.
XmR Chart (Control)
The earlier Stem and Leaf diagram indicated a bi-modal condition; therefore the project team
identified the source of this condition and eliminated one of the sources and optimized accordingly.
The two sources were 1) some patients were first-time visitors and 2)some of the patients were
return visitors.
Order
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Order
10
17
29
39
55
64
28
6
5
3
39
46
35
30
6
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
32
33
11
20
13
9
14
12
30
56
62
73
54
10
9
The project team changed the process so that first-time visitors were processed ahead of time; thus,
reducing their wait time in the waiting room. This dramatically decreased the overall average wait
time. Part of the control strategy is to employ the use of an on-going capability study; however, one
must first determine the process is in statistical control. To do this, an XmR Chart of new wait times
has been established in order of occurrence. This data is shown above.
The Upper Control Limit (UCL) and Lower Control Limit (LCL) are indicated which shows that
continuous improvement is necessary.

12.
20
15
Series1
10
Mean
UCL
5
LCL
0
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 30