Posted: April 25th, 2025

case study

 Discussion Questions: Summarize the case study, in your own words, by answering the following questions.  

Abstract:

This teaching case study features characters, hospitals, and healthcare data that are all fictional. Upon use of the case study in classrooms or organizations, readers should be able to create a control chart and interpret its results, and identify situations that would be appropriate for control chart analysis. The case is best suited for MBA operations courses and modules, but it also could be used in a hospital setting at a facility that has embraced a continuous improvement philosophy.

Read the entire case study above and answer the questions below using the Case Study Report Template.

 

Do not copy and paste from the video or article.

Lean Six Sigma in the Age of Artificial Intelligence

Check out other references and  resources related to the case study. These should be included in the project template you submit.

 Discussion Questions:

a. Describe the organization where the problem exists

b. Summarize how the hospital assessed the current process performance.

c. Discuss the critical metrics that were monitored in the case study.

d. In your own words, what is the purpose of control charts in the case study.

e. List the different types of control charts used and the purpose of each.

Attached you will the files containing source information for this case study and a basic template for you to complete and submit your work.

Each case study involves current events related to quality improvement tools applied to processes.  I will provide you with the topic and articles that can be found on the internet.  This will be followed by discussion questions that I expect you to address in your paper.

The deadline to submit your paper is shown in the syllabus.

1. Download the Case Study and the template and rename it [Student Name]_Case_Study_4 x.

2. Review the related material provided and focus on how they relate to the discussion questions.

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Met the minimum page requirement (3 pages of case analysis); All pages numbered; Double spaced; 1 in. margins on all sides; Uses Arial font; 12 pt. font; Includes all required sections; No extra spacing between paragraphs; No indentations.  All text flush with left margin.; Student name single line in the header margin

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Student Name:

Case Study Title:
[What is the title of the case study article?]

1. Issue:
[What is the major issue that the case study is about? Tell us about the company, organization, or industry]

2

. Discussion Questions:
[Type in the discussion questions below and then provide your detailed analysis and observations for each question. The question must be typed in.]

a.

b.

c.

d.

e.

3. Opinions & Suggestions:
[Any other observations in your analysis?]

4. References/Sources:
[List the articles, videos, or other sources you reviewed in this case study. Use standard MLA format for references and sources.]

a.

b.

c.

Use the above format for your case study, including the section headers in bold. The main text (sections 1, 2, & 3) should fill a minimum of 3 full pages, excluding references/sources. Do not add extra spaces between paragraphs. Do not indent paragraphs. All text flush with left margin.

[
NOTE: Red italicized text in brackets is for your instructions only. Please delete this text as you complete your paper.]

2

ASQ www.asq.org Page 1 of

6

Making the Case for Quality

At a Glance . . .

December 201

5

• This teaching case study
features characters,
hospitals, and healthcare
data that are all fictional.

• Upon use of the case
study in classrooms or
organizations, readers
should be able to create
a control chart and
interpret its results, and
identify situations that
would be appropriate for
control chart analysis.

• The case is best suited
for MBA operations
courses and modules,
particularly those focused
on operations/process
improvement. It also could
be used in a hospital
setting at a facility that has
embraced a continuous
improvement philosophy.

Using Control Charts in a
Healthcare Setting

by Jack Boepple

After spending 10 years on the road as a healthcare operations improvement consultant, Isabella “Izzy”
Cvengros decided it was time to settle down. Although Cvengros loved what she was doing, she had
recently become engaged and wanted to spend more time with her future husband. However, with fam-
ily members spread throughout the country, there was really no “home” to go back to.

As a consultant, Cvengros had been assigned to a wide variety of healthcare projects over the years,
learning a great deal. She also enjoyed seeing so many different parts of the United States, with a par-
ticular fondness for New Hampshire, so she and her fiancé focused their job search there.

On October 10, 2014, Cvengros found herself with a good problem. She had just completed a series
of interviews with two Nashua, NH, hospitals: Farrell Memorial Hospital and Penner Mobley Health
Services, and both had gone very well. She interviewed for the same job at both facilities—director of
operations improvement—and leadership from both facilities indicated she was proceeding to the final
round of interviews, which entailed meeting each hospital’s executive team.

As a certified Lean Six Sigma Black Belt, Cvengros was thrilled to hear both hospitals’ progressive
views on continuous improvement. While she saw examples of many quality tools and analyses being
performed at each hospital, she did not notice any control charts being used.

Although control charts are typically associated with manufacturing processes, Cvengros knew they
could be applied to any industry’s processes, including hospitals.

Because she had employed control charts with great success in several of her assignments, she incor-
porated this experience as part of her interview responses. Both hospitals were intrigued and asked
if she could provide an example during her next round of interviews. Cvengros agreed, but to make
the analysis more meaningful, she asked each hospital to provide her with data so the example con-
trol chart analysis would be more meaningful and relevant to them. Since one of the key discussion
points during her interviews at both facilities revolved around reducing the patient’s length of stay,
Cvengros asked for data on their estimated date of discharge (EDD) by week from January through
September 2014.

About Farrell Memorial Hospital

Farrell Memorial Hospital is a 400-bed general medical and surgical hospital located in Nashua, NH.
The hospital, which is part of a larger statewide healthcare system, has won numerous awards over
the years, including for patient safety, performance in its rehabilitation patient care unit, and critical
care excellence.

About Penner Mobley Health Services

Penner Mobley Health Services is a 500-
bed general medical and surgical hospital
in Nashua. Part of a regional health system,
Penner Mobley has also received numer-
ous awards, including being named a top-50
hospital in the country for the past six years,
recognized for its high-quality and innovative
nursing care, and recognized as a performance
improvement leader.

Assessing Hospital Performance

The Hospital Consumer Assessment of
Healthcare Providers and Systems (HCAHPS)
is a national patient satisfaction survey that
asks patients about their experiences during a
recent hospital stay.1 The responses are broken
down into the following categories:

• Survey of patients’ experiences
• Timely and effective care
• Readmission, complications, and deaths
• Use of medical imaging
• Medicare payme

nt

• Number of Medicare patients

Within some categories are sub-categories.
For example, in the “Timely and effective
care” category, there were 10 sub-categories,
including heart attack care, surgical care, and
pregnancy and delivery care.

The results are maintained by Centers for
Medicare and Medicaid Services, CMS.gov,
(Medicare) and anyone can compare one
hospital vs. another on Medicare’s Hospital
Compare website2. Knowing this, Cvengros
ran a report of patients’ experiences category
results for both Farrell Memorial and Penner
Mobley as compared to the state and national
averages (see Table 1).

Both hospitals ranked below the state and
national averages in many of the categories.
Cvengros was surprised by the ranking of
Penner Mobley Health Services as it is a
Magnet®-recognized organization. Recognized
by the American Nurses Credentialing Center,
a Magnet® designation recognizes hospitals
with high-quality, innovative nursing and
best practices for patient care, particularly
in the areas of nurse communication (Q1),

availability of help (Q3), and receipt of discharge information (Q8). Cvengros
found it interesting their performance was rated lower than the state and national
averages on nursing communication (Q1), the availability of help (Q3), pain con-
trol (Q4), and explanation for medications (Q5).

Estimated Date of Discharge

One of the cost reduction approaches employed by hospitals is to reduce the
patient’s length of stay (LoS). One of the strategies embedded within this
approach is to actively plan the patient’s discharge. Like anything else, devel-
oping a plan and setting a target date for completion of a task increases the
likelihood the task will be completed on time (vs. no planning and/or coordina-
tion of resources). While task planning is a project management fundamental
(similar to work breakdown structure), it is still a relatively new concept in the
healthcare field.

In hospitals, the “project teams” are composed of the patient’s physician, nurses,
and ancillary professionals (such as pharmacists, physical therapy, occupational
therapy, and social workers). The composition of the team was dependent upon
the specific patient’s condition. So, there could literally be as many project

ASQ www.asq.org Page 2 of 6

Table 1 — HCAHPS Survey of Patients’ Experiences, October 201

4

# Question Farrell
Memorial

Penner
Mobley

State
Average

National
Average

1 Patients who reported
that their nurses “Always”
communicated well

73% 77% 79% 79%

2 Patients who reported that
their doctors “Always”
communicated well

76% 81% 81% 82%

3 Patients who reported that
they “Always” received help
as soon as they wanted

62% 63% 70% 68%

4 Patients who reported that
their pain was “Always”
well controlled

66% 68% 72% 71%

5 Patients who reported that
staff “Always” explained
about medicines before
giving it to them

61% 62% 67% 64%

6 Patients who reported that
their room and bathroom
were “Always” clean

65% 66% 74% 73%

7 Patients who reported that
the area around their room
was “Always” quiet at night

52% 63% 64% 61%

8 Patients who reported “Yes,”
they were given information
about what to do during
their recovery at home

84% 87% 87% 85%

9 Patients who gave their hospital
a rating of 9 or 10 on a scale
of 0 (lowest) to 10 (highest)

64% 73% 75% 71%

10 Patients who reported
“Yes,” they would definitely
recommend the hospital

68% 77% 76% 71%

As noted by Jackie Birmingham, vice president
of regulatory monitoring and clinical leader-
ship at Curaspan Health Group,4 correctly
estimating the date of discharge has several
positive benefits/effects:

1. Improves care transition. According to Health
Affairs (a journal of health policy thought and
research): “The term care transition describes
a continuous process in which a patient’s care
shifts from being provided in one setting of care
to another, such as from a hospital to a patient’s
home or to a skilled nursing facility (SNF) and
sometimes back to the hospital. Poorly managed
transitions can diminish health and increase
costs.”5 Estimating the date of discharge helped
improve the communication/coordination with
the patient’s post-hospital destination.

2. Improves expectations setting with patients
and their families. Part of setting an estimated
discharge date is communicating it with
the patients and their families. While the
estimate is just that—a target—the net effect
is to improve the communications between
all parties. Most people cope better with the
known (vs. the unknown). By communicating
the EDD to the patients and their families,
some of the “mystery” is removed, and it
brings them into the conversation.

3. Enables reduced unnecessary clinical
variation in treatment. Standardized care
plans for specific-case types (e.g., sepsis) lists a
sequence of services needed by patients, based
on an anticipated LoS. Criteria sets are used to
monitor patients’ clinical progress to determine
whether a continued stay is medically
necessary. Embedded in both standardized care
plans and criteria sets is a timing component,
and the EDD helps quantify it.

4. Enables more efficient hospital operations.
Capacity management, bed management, and
patient throughput are all dependent on EDD.

5. Prepares for the future. Given the evolution
of healthcare in recent years—with increased
responsibilities for utilization reviewers, use of
recovery audit contractors, and focus on denial
management—justifying and documenting LoS
has grown ever-more important. As such, EDD
seems destined to be measured and monitored
as some reimbursement metric.

ASQ www.asq.org Page 3 of 6

“teams” as there are patients. Depending upon the hospital, these project
teams might be called different names, such as a multidisciplinary team or
an inter-disciplinary team.

Whereas the review/update of a typical project plan might be done on a
weekly basis, the “tasks” (i.e., patients) must be reviewed/updated on a
daily basis. And with a large number of patients and various demands upon
each caregiver specialty, coordinating a hospital project team can be a
daunting task. Many hospitals have tackled this task by creating multidisci-
plinary rounds.

According to the Institute for Healthcare Improvement: “With multidis-
ciplinary rounds, disciplines come together, informed by their clinical
expertise, to coordinate patient care, determine care priorities, establish
daily goals, and plan for potential transfer or discharge. This patient-
centered model of care has proven to be a valuable tool in improving the
quality, safety, and patient experience of care.”3

One barometer to assess the effectiveness of multidisciplinary rounds was
to measure EDD, which is one of the primary outcomes for each patient
discussed during multidisciplinary rounds. Setting an EDD prompted active
discussion on the barriers preventing a patient’s release. A natural byprod-
uct of these discussions is to streamline the transition of care for patients
(i.e., it helped reduce/minimize unnecessary clinical variation in treatment).
It also fosters a team, rather than an individual, approach to patient care.

Mathematically:

EDD
Accuracy =

Number of Estimated Discharges Actually Discharged

Number of Potential Discharges

And:

Number of
Potential
Discharges

=
Number of Estimated Discharges Actually Discharged
+ Number of Estimated Discharges Actually Not Discharged
+ Number of Discharges Not Estimated

Although there is no ideal goal for EDD accuracy, higher is better than
lower. A lower rate and/or a “stuck” rate, is symptomatic of a problem.
It requires analyses to determine the cause of the problem. In general, a
low rate is indicative that (a) the staff is not communicating effectively, or
(b) not taking the estimation of discharge dates seriously.

Control Charts

All processes have variation. The challenge is to determine whether or not
the variation is “common cause” (or random or “noise”) or “special cause”
(or nonrandom variation).

According to iSixSigma, an online clearinghouse for process improvement,
“common cause variation is fluctuation caused by unknown factors result-
ing in a steady, but random, distribution of output around the average of
the data.” Special cause variation is the inverse: variation caused by factors
that result in a nonrandom distribution of output. It is also referred to as
“exceptional” or “assignable” variation.6 Determining the cause of special
cause variation typically requires further analysis/investigation.

For example, think of
weighing yourself every
morning. One day a six-foot
man might weigh 201.2
pounds. The next day he
is 200.4 pounds. The fol-
lowing day he is 200.8
pounds. Over time, he is
probably hovering around
201 pounds, +/- two pounds.
His weight is demonstrat-
ing normal (common cause)
variation. Come Christmas
holidays/vacation, however,
his weight might balloon
to 205 pounds. In this case,
the “special cause” varia-
tion is rather apparent: He
consumed far more calories
than he expended during
the holiday. Unfortunately,
identifying the special cause
is rarely as straightforward.

Control charts show what
type of variation is occurring
in a process. Synonymous
with statistical process
control, control charts are a
graphical view of a process.
Special tests are conducted
against the data to deter-
mine (a) the normal limits/
variation of the process and
(b) whether or not these
limits have been “violated.”7
Control charts can also be
considered a run chart “on
steroids.” Run charts dis-
play observed data in a time
sequence.8 Control charts
take the simple run chart and
apply some statistical rigor
to them. Basically, the mean
(average) is calculated and
drawn on a graph. The individual data points then are plotted on
the same graph. Then, the control limits are also drawn as +/-
three standard deviations from the mean.

Figure 1 is an example control chart (with no rules “viola-
tions”). The middle (green) line is the mean. The upper and
lower (red) lines are the upper control limit (UCL) and lower
control limit (LCL).

When introduced in the 1920s, control charts were drawn on
graph paper. More recently, specialized computer programs
(such as Minitab) can create these automatically.

The type of control chart used depends upon the type of data—
variable (continuous) or discrete (attribute). Figure 2 provides
a decision tree on how to select the appropriate control chart.
Figure 1, for example, is an NP chart.

ASQ www.asq.org Page 4 of 6

Sa
m

pl
e

C
ou

nt

Sample

NP Chart of 3T

UCL = 25.9

0

LCL = 3.2

1

NP = 14.56

0

5

10

15

20

25

1

5 9 13 17 21 25 41 45373329

Chart of individuals

Moving average—
moving range chart

_
X and R chart or _
X and s chart

p chart

np chart

u chart

c chart

Are the data measured
on a continuous scale?

(e.g., time, weight,
temperature)

Variable Data

The data are counted
(e.g., defective items

or complaints)
Attribute Data

Is each data point a
natural subgroup?
(such as one batch)

or
Are data gathered

infrequently?

The number of
defects are counted
(and an item can

have many defects)

Are defective
items counted?

Are the data
normally

distributed?

Can sample
size vary?

Can sample
size vary?

No

No

No

No

No

No

Yes

Yes Yes

Yes Yes

Yes
Source: Nancy R. Tague’s
The Quality Toolbox, Second Edition,
ASQ Quality Press, 2005.

Figure 1: Example of a Control Chart

Figure 2: Control Chart Decision Tree

ASQ www.asq.org Page 5 of 6

The types of tests that
can be run to determine
whether a process is out of
control varies by data type.
Continuous data has more
tests, but both attribute and
continuous data have the
same core four tests:

1. 1 point > 3 standard
deviations from
center line

2. 9 points in a row on
same side of center line

3. 6 points in a row, all
decreasing or increasing

4. 14 points in a row,
alternating up and down

The first test is the one
typically associated with
control charts (see Figure 3
for an example)—a point
outside the control limits.

Sa
m

pl
e

C
ou

nt

Sample

NP Chart of 4s

UCL = 9.35

LCL = 0

NP = 3.64

0

2

4

6

8

10

12

14

1

1

1

5 9 13 17 21 25 41 45373329

Figure 3: Example of a NP Control Chart With Test No. 1 Violated

ASQ www.asq.org Page 6 of 6

The number of points associated with each test above is essen-
tially an industry standard, although software programs (such
as Minitab) allow you to configure/modify those values to suit
your specific need/application.

When a test is violated, it is up to the user to determine the spe-
cial cause. Test violations are not necessarily negative in nature
as they may indicate either a favorable or adverse shift in a
process. Or they just might indicate an abnormal event that the
process failed to handle.

Results

What is the variation trying to tell us about
a process, about the people in the process?

—W. Edwards Deming

During their interviews with Cvengros, leaders from both hos-
pitals (rather proudly) claimed improvements in the accuracy of
the EDD—providing examples to Cvengros (see Table 2).

Cvengros knew her control chart analysis would show
whether or not they were reacting to normal variation or
whether their actions had resulted in a statistically significant
favorable change.

Even though it was only one data point, Cvengros felt her analy-
sis could be a proxy on the hospitals’ willingness to change and
adapt. In other words, she would have a quantitative way to
assess whether the actions of the healthcare leaders back their
words on continuous improvement.

For More Information

• To contact the author of this case study, email Jack Boepple
at j-boepple@kellogg.northwestern.edu.

• To view this and other case studies, visit the ASQ
Knowledge Center’s Case Studies landing page at asq.org/
knowledge-center/case-studies.

References

1. HCAHPS, http://hcahpsonline.org/home.aspx.
2. Medicare Hospital Compare, http://www.medicare.gov/

hospitalcompare/.
3. Institute for Healthcare Improvement. “How-to Guide:

Multidisciplinary Rounds,” http://www.ihi.org/resources/
Pages/Tools/HowtoGuideMultidisciplinaryRounds.aspx.

4. Curaspan. “Estimating the Date of Discharge: Five Reasons
to Do It,” http://connect.curaspan.com/blog/estimating-date-
discharge-five-reasons-do-it.

5. Health Affairs. “Improving Care Transitions,” http://www.
healthaffairs.org/healthpolicybriefs/brief.php?brief_id=76.

6. iSixSigma. “Common Cause Variation,” http://www.
isixsigma.com/dictionary/common-cause-variation;
“Variation (Special Cause),” http://www.isixsigma.com/
dictionary/variation-special-cause/.

7. Wikipedia. “Statistical process control,” http://en.wikipedia.
org/wiki/Statistical_process_control.

8. Wikipedia. “Run Chart,” http://en.wikipedia.org/wiki/
Run_chart.

About the Author

Jack Boepple is a process improvement professional and adjunct
professor at the Kellogg School of Management. He received
his Project Management Professional (PMP) certification in
1999, his ASQ Six Sigma Black Belt certification (CSSBB) in
2007, his ASQ Manager of Quality/Organizational Excellence
certification (CMQ/OE) in 2007, and his Professional in
Healthcare Quality (CPHQ) certification in 2015. He served as
an examiner for the Baldrige Performance Excellence Program
from 2008–2010 and 2012.

Table 2 — EDD Accuracy Rate for Past 4 Weeks

Week Ending Penner Memorial

09/07/2014 35.5% 43.0%

09/14/2014 43.2% 44.3%

09/21/2014 40.2% 45.3%

09/28/2014 48.0% 45.2%

mailto:j-boepple%40kellogg.northwestern.edu?subject=ASQ%20Case%20Study

http://asq.org/knowledge-center/case-studies

http://asq.org/knowledge-center/case-studies

http://hcahpsonline.org/home.aspx

http://www.medicare.gov/hospitalcompare/

http://www.medicare.gov/hospitalcompare/

http://www.ihi.org/resources/Pages/Tools/HowtoGuideMultidisciplinaryRounds.aspx

http://www.ihi.org/resources/Pages/Tools/HowtoGuideMultidisciplinaryRounds.aspx

http://connect.curaspan.com/blog/estimating-date-discharge-five-reasons-do-it

http://connect.curaspan.com/blog/estimating-date-discharge-five-reasons-do-it

http://www.healthaffairs.org/healthpolicybriefs/brief.php?brief_id=76

http://www.healthaffairs.org/healthpolicybriefs/brief.php?brief_id=76

Achieving Process Stability with Common Cause Variation: Strategies for Success

Achieving Process Stability with Common Cause Variation: Strategies for Success

Identifying and Managing Special Cause Variations: Dealing with the Unexpected

Identifying and Managing Special Cause Variations: Dealing with the Unexpected

http://en.wikipedia.org/wiki/Statistical_process_control

http://en.wikipedia.org/wiki/Statistical_process_control

http://en.wikipedia.org/wiki/Run_chart

http://en.wikipedia.org/wiki/Run_chart