Lab Assignment 2 Introduction Applying statistic.docx

Lab Assignment 2
Introduction
Applying statistics is as much science as art, especially when
we are forced to make trade-offs between qualitative and
quantitative aspects of analysis. In this lab you will explore
some concepts that engineers grapple with in the field of data
analysis.
Objective
1. Construct confidence intervals suitable for design
engineers to use in developing a medical device.
2. Consider the trade-off between certainty (a higher
confidence) and the cost of data collection.
3. Develop an appreciation for the nuances of statistical
application.
Notes
Tips for full scoring: SHOW ALL WORK, USE CORRECT
NOTATION, COMPLETE ALL CALCULATIONS!
Lab Assignment
Part A. Variability in Flying Paper Planes

This part of assignment guides you through the steps of data
collection and analysis. All team members must participate in
the
data collection process (1)-(4) to qualify for full score. Note the
collected data will also be used for your individual final report
at
the end of the semester: the quality of your data impacts your
final report. In general, a good quality data refers to the one
with
minimal ‘unexpected’ variance. In experimentation, people
unwillingly and unknowingly invite multiple sources of
variability that
add to a variance. Having unexpected variance in your data
twists your conclusions, and lowers the power of your statistical
model. You will understand how this happens in Lecture 9
Regression Model. Thus, the primary goal of data collection is
to
minimize variance by identifying and eliminating unexpected
sources of variability.
In this regard, the current task is to measure air-time of three
paper planes, repeated at least 30 times for each plane. The
quality
of your work is evaluated by successfully minimizing variance,
and by reasonable efforts to reduce it. First, select three designs
of paper planes from Appendix I and make them. You can find
steps to make each design in the webpage address below
Appendix I.
(1) Before measurement, brainstorm with your team members to
identify a list of potential sources that may impact the variance
of your measurements. Construct a Fish-Bone diagram to
organize the list into proper categories.
(2) Discuss practical methods to prevent the sources from
impacting your measurement. Describe at least three actions you

will
take during your measurement to keep from the unexpected
variability.
Next, find a place for uninterrupted flight and measure air-time.
Repeat measurements at least 30 times for each design.
(3) Specify measurement environment, methods, devices, and
individual roles. Also, attach a photograph of measurement.
(4) Attach the recorded data from Excel. It will have at least 30
rows of data fields with three columns, one for each design.
Finally, provide some descriptive and inferential statistics for
your data. Show all steps if you manually get them. Show all
functions or screen-captured steps when you use Excel or other
statistical software.
(5) Provide descriptive statistics for all three designs including
mean, range, and standard deviation.
(6) Draw box plots for all three designs.
(7) Construct 95%-confidence intervals for means of all three
designs.
Part B
Read the case study in Appendix II, “Neonatal Device
Development: Engineering a Better Future One Baby at a
Time.” After
reading the case, answer the following questions, using the table
of collected data entitled, “Measurements taken from Neonatal
Ward.”

(1) Develop a 99% confidence interval for Biparietal distance
for neonatal infants based on the data available in the case for
babies who are 1-2kg, 2-3kg, and 3-4kg in weight. Clearly
indicate whether you are using Z or t table values. [Hints: One
interval for each group; therefore, three total intervals; is
population standard deviation known?]
(2) Suppose you wanted to predict the baby’s Biparietal
distance with more precision. The design team claims that it
must have
more precise and accurate estimates of this measure to ensure
that the device fits properly for each baby. In fact, they
would like to have the half-interval, h, be less than 2% of the
mean value (e.g., for the 1-2kg group, h= 1.4948). How many
babies would you need to sample in each group to have this be
the case? Maintain 99% confidence. [Hint: If your result is
n<30, by CLT, we would need to round n up to 30 to maintain
this confidence level. In other words, you would always have
sample size greater than 30. ]
Part C
Read the article in Appendix III entitled “Pocono Medical
Center: Faster Lab Results Using Six Sigma and Lean” by T.
Hayes, Carmine J. Cerra, and Mary Williams. Turn in answers
to the following questions:
(1) Describe the reasons why the Pocono Medical Center
decided that a quality program was needed in the laboratory.
What

was/were the goal(s) of the study?
(2) What was the “huge bottleneck”?
(3) A process map was used during the measuring phase to
determine variability in processing times. Which areas caused
the
most variability? Why is a large amount of variability
undesirable?
Submission Rules Submit in a PDF file. Make sure the team
Number is at the top of the document, as well as in the PDF
file name. Only one solution set per group. Your responses must
be typed and organized. Any illegible answers will not receive
points. Late labs will not be accepted. The entire group will
receive the same grade. Do not consult with anyone outside of
your
group, other than the Instructor for IE 424.
APPENDIX I
Paper Plane Designs
Figure 1 Arrow

Figure 2 Delta
Figure 3 Classic-Dart
Figure 4 Condor
Figure 5 Dragonfly
Figure 6 Canard
Figure 7 Bullet
Figure 8 Raptor
Figure 9 Spade
Figure 10 Interceptor
Figure 11 Bulldog
Figure 12 Trap-glider
Figure 13 Stealth-wing

Figure 14 Helicopter
Figure 15 Flying-ring0F1
1 http://www.funpaperairplanes.com/index.html
http://www.funpaperairplanes.com/pdf/Arrow_Sample.pdf
http://www.funpaperairplanes.com/pdf/Delta_sample.pdf
http://www.funpaperairplanes.com/pdf/Classic_Dart_sample.pdf
http://www.funpaperairplanes.com/pdf/Condor_sample.pdf
http://www.funpaperairplanes.com/pdf/Dragonfly_sample.pdf
http://www.funpaperairplanes.com/pdf/Canard_Sample.pdf
http://www.funpaperairplanes.com/pdf/Bullet_Sample.pdf
http://www.funpaperairplanes.com/pdf/Raptor_Sample.pdf
http://www.funpaperairplanes.com/pdf/Spade_sample.pdf
http://www.funpaperairplanes.com/pdf/Interceptor_sample.pdf
http://www.funpaperairplanes.com/pdf/Bulldog_Sample.pdf
http://www.funpaperairplanes.com/pdf/Trap_Glider_sample.pdf
http://www.funpaperairplanes.com/pdf/Stealth_Wing_sample.pd
f
http://www.funpaperairplanes.com/pdf/Helicopter_sample.pdf
http://www.funpaperairplanes.com/pdf/Flying_Ring_sample.pdf
APPENDIX II
Neonatal Device Development: Engineering a Better Future
One Baby at a Time
Dr. Irene J. Petrick, Ph.D., Industrial & Manufacturing
Engineering, with the assistance of Dr. Charles Palmer, M.B.,

Ch.B.,
F.C.P., Hershey Medical Center, October 2002.
The Milton S. Hershey Medical Center is both an academic
institution and a medical service provider. As such, professors
of
medicine are also doctors giving care to patients. This synergy
has incredible benefits, but there are also ethical issues in
studying
a patient while providing care. The team of the neonatal ward
includes doctors, residents, nurses, clinicians, and other care-
givers who interact with the babies and their parents.
Dr. Charles Palmer, M.B., Ch.B., F.C.P., is Professor of
Pediatrics in the Division of Newborn Medicine at Hershey
Medical
Center. His patients are the premature babies born into this
world with multiple complications, the least of which include
diminished lung function and the need for specialized feeding
approaches. These babies can spend weeks to months under Dr.
Palmer’s care, and many live in very protected incubator
environments. Tubes frequently are needed to help maintain
positive
pressure in the chest cavity and to feed the babies. These tubes
are currently held in place with tape. The tape secures the tubes
snuggly against the baby’s nose and mouth. See Figure 1.
Figure 1: Newborn baby with taped endotracheal tube. The
endo-
tracheal tube is pushed into the chest cavity. A feeding tube is
also
inserted. It is very difficult to prevent the tubes from moving
when

secured only with tape.
Unfortunately, the tape is a breeding ground for bacteria, does
not always hold the tubes in the correct orientation, can move
and stretch over time, must be changed when it becomes less
functional, and can irritate the baby’s skin. Moreover, parents
are
distressed by so many tubes and the tape which covers much of
the baby’s face.
Often babies in the neonatal ward can spend weeks to months,
with the very young ones needing endotrachael and feeding
tubes
for sustained periods. Peeling tape from the infant’s face, in
addition to causing discomfort or dislodging the tubes, can
damage
the very fragile skin.
For the past couple of years, Dr. Palmer and his team have been
working on NORI [Nasal Oral Respiratory Interface], a clear
plastic stirrup-shaped device that will hold the tubes in place
and that can be secured to the baby’s face using hydrogel. See
Figure 2. Hydrogel can be used in place of tape to reduce the
likelihood of infection and to reduce the damage to skin with
removal and/or changing of the tubes or device. The team is
still determining whether or not a Velcro or other type of strap
will
be needed to further secure the device to the baby’s face.
Figure 2: NORI device is stirrup-shaped plastic holder that can
be fitted
snuggly to the baby’s face and attached with hydrogel. Once

fitted,
endotracheal and feeding tubes can be inserted. The device
holds the
tubes in a stable orientation and reduces the likelihood of
spontaneous
exturbation.
Data Collection for Design Development
During development the team had to determine the dimensions
that might best accommodate a range of baby’s faces. Baby
head
and facial measurements are related to their stage of
development and to their weight. Typically, babies in the
neonatal ward
range in weight from about 1 kilogram to near 4 kilograms. The
neonatal ward includes both babies who are born prematurely
and those requiring surgery and recovery. As part of their
development effort, the team used a digital picture of a baby’s
face and
developed line drawings of the contours and features. See
Figure 3. The team used this line drawing to conceive of shapes
and
features of the device.
Figure 3. Early device development relied on line drawings of
baby features taken from digital pictures. Here the device is
shown as it might fit the infant’s face.

In planning its device the team sought measurements that
already existed in the wide knowledge base of
anthropometric data that has been gathered over the years to
assist product developers. The team discovered that
anthropometric measures were, in fact, available for newborn
infants, but that these were at the upper end of their
preliminary measurements. For example, the 5th-percentile of
head breadth (similar to Biparietal distance) is estimated
to be 100 mm and the 50th percentile, the median is 110 mm
[1]. These numbers are above the preliminary measures
for all but the 3-4 kg baby group. Thus the team was forced to
undertake extensive measurements of babies in the
neonatal ward.
To actually measure the baby’s facial contours, the team
identified key measures that would be needed to make sure
that the device would fit properly. Several of these measures
included head circumference, maximum cranial length,
widest diameter of the head (the biparietal distance), the lip
radius, the cheek radius, and other critical features. For
some of these measures, the team could use measuring tape,
specially coated to reduce potential introduction of
infection.
Each time the team took measurements, the baby’s comfort and
safety had to be considered. The team had to develop
calipers that could be used on the babies, eventually modifying

traditional digital calipers with a plastic extension
that would be suitable for sterilizing and then using on the
babies in the neonatal ward. See Figure 4. The team also
used aluminum wire encased in plastic to make face radius
measurements. Measures were made for three groups of
neonatal babies, 1-2 kilogram, 2-3 kilogram and 3-4 kilogram.
Since there is not a stable population of babies in the
neonatal ward at any one time, these measures were made over a
6 month period.
Figure 4: Specialized calipers fitted with plastic to reduce
infection and equipped with digital readouts.
Table 1 summarizes the results for one of the more important
measures to ensure a snug device fit, the Biparietal distance
(widest
diameter of the head measured between lateral sides of the
parietal bones). These measures were made with the calipers. It
can
be assumed that these measurements come from a parent
population that is normally distributed; the values in Table 1 are
based
on the sample.
Table 1: Measurements taken from Neonatal Ward
1-2 kg Baby 2-3 kg 3-4 kg Baby

Number of
observations
10 babies
16 babies
9 babies
Average
Weight
1684 g
2306 g
3478 g
Average
Biparietal
Distance
74.74 mm
84.41 mm
95.58 mm

Standard
Deviation for
Biparietal
Distrance
7.468 mm
6.922 mm
3.361 mm
During development, the team considered several design criteria
in its many iterations:
Design criteria for NORI
• Stability for tracheal tube – since this device is targeted to
the special group of neonatal patients needing a breathing
tube, this device would be used only for those babies on a
ventilator.
• Orientation of the tube – palatal grooving is a major problem
with the baby’s mouth health and longer term development. The
palate, the top inside portion of the mouth known as the roof, is
particularly sensitive to a tube positioned against it for long
periods of time. This is exacerbated by the baby’s natural

sucking motion which also pushes the tube against the roof of
the
mouth.
• No tape on the baby’s face –When combined with the baby’s
saliva, tape is a natural incubator for bacteria. Since the
tape might remain on the baby for several days or more, this
breeding ground tends to increase the likelihood of skin
problems and pneumonia.
• Aesthetics – often babies have so many tubes and lines going
into their tiny bodies that tape almost obscures the face. This
is a difficult sight for parents and can interfere with bonding
between parent and child in the early stages of development.
The goal of the neonatal team is to reduce this as much as
possible.
• No spontaneous exturbation – though not very common, even
neonatal babies have been known to spontaneously rid
themselves of the tube. This might occur through excessive
movement or through smaller movements over a period of time.
Once the tube is freed, it must be reinserted causing increased
risk and discomfort to the baby.
• Stable platform for multiple tubes – once tubes are placed
into the esophagus and/or stomach, doctors prefer that the tubes
remain in the installed position. A stable platform for multiple
tubes increases the likelihood that tubes will remain in
desired positions. This also helps the caregivers to maintain
constant positions for these tubes.

In the future, the team believes that the basic device could be
modified to include a palate guard, a pacifier, and eventually
pathways for direct feeding of oxygen. Though the team is
developing the apparatus for high risk neonatal babies needing a
ventilator, it is not inconceivable that a similar device could be
scaled to accommodate similar needs in an adult population.
The team went through several iterations with the NORI,
working on the design, a prototype, incorporating various
manufacturing needs, and then developing multiple prototypes.
Figure 5 demonstrates an early prototype of NORI
attached to a clay model of a baby’s face.
Figure 5: Early NORI prototype attached to a clay model of
a baby’s face. ET stands for endotracheal tube.
Dr. Palmer has been leading this device development, learning
SolidWorks in the process to engineer his vision. He has
worked
with several other professionals, including Penn State
Behrend’s Plastics Center and faculty in Industrial and
Manufacturing
Engineering, to identify proper plastics and materials for use in
the system and to rapid prototype emerging designs. Students
have helped Dr. Palmer’s team make measurements of the
baby’s faces to determine approximate size ranges.
The team has multiple stakeholders: the baby [who as a minor
cannot really contribute input, but around whom all else
focuses];

the parent(s); nurses; doctors; students; Hershey Medical Center
administrators; engineers; industry executives, designers, and
engineers. Dr. Palmer’s primary goal in all of this is to develop
safer, more comfortable, more effective devices to assist babies
under his care. The NORI project is just one of many projects
Dr. Palmer has undertaken to improve the quality of care for his
patients.
The NORI device has not been commercialized yet, but is in
prototype testing at Hershey Medical Center. Dr. Palmer is in
discussions with various companies considering developing a
commercial product. He is also considering ways to test the
efficacy of this device on patients in the neonatal ward. Once
again, his concerns include balancing risk to the individual baby
with the need to test the device to develop a better endotracheal
and feeding tube interface for neonatal care. Ultimately, Dr.
Palmer’s primary objective is to create a safer environment for
his patients, one that gives them a head start against the
multiple
complications many of them face as premature newborns. He is
engineering a better future for each of these babies since studies
show that reduced complications during neonatal care reduce
the risks to the infant as it matures and grows to adulthood.
[1] Source: Pheasant, Stephan (1998) Bodyspace:
Anthropometry, Ergonomics and the Design of Work, Taylor
and Francis, Table 10.1

APPENDIX III
Pocono Medical Center:
Faster Lab Results Using Six Sigma and Lean
Contributed by Walter T. Hayes and Carmine J. Cerra, with
Mary Williams
For years, the Pocono Medical Center’s laboratory battled to
provide test results to doctors in time for
their early-morning patient rounds. Time crunches would occur,
both in drawing patients’ blood and in
processing it. Physicians would begin their rounds at 6 a.m., but
blood test results generally were not
ready until 9 a.m.
Walter Hayes, the hospital’s director of laboratory services, and
Dr. Carmine Cerra, chief of pathology,
were seriously considering automating the lab in an effort to fix
the bottlenecks. Both had learned
about Six Sigma and Lean quality improvement processes
during a recent automation conference, and
they wanted to make lab automation the hospital’s first Six
Sigma process.
Before beginning such a huge project, however, senior
management requested that a satisfaction survey
be sent to the hospital’s 160 doctors. Based on the responses
received, the executive management team
decided against jumping into an automation effort. “The doctors
essentially said ‘just give us the test

results by 7 a.m. and we don’t care how you do it,’” Hayes
notes.
At a Glance . . .
• The Pocono Medical Center
initiated a Six Sigma/Lean
project to deliver blood test
results to doctors earlier in
the workday.
• Within about six weeks, the
project team implemented a
solution. Doctors began to
receive blood test results
by 6 a.m. for critical care
patients and by 7 a.m. for
all other patients.
• Project results also extend
outside of the laboratory,
contributing to a decrease
in overall patient length of
stay for the medical center.
Management asked Hayes and Cerra to focus instead on a pilot
project using Six Sigma/Lean. Their
project goal was to find a way to get blood test results to
doctors earlier. With board approval, Pocono
Medical Center launched the project in mid-May 2005 and
finished it less than three months later. Today,
doctors have blood test results for critical care patients by 6
a.m. and for all other patients by 7 a.m.

About Pocono Medical Center
Pocono Medical Center is a 196-bed not-for-profit community
hospital, fully accredited by the Joint
Commission on Accreditation of Healthcare Organizations.
Located in the Pocono Mountains in East
Stroudsburg, Pennsylvania, the center employs more than 1,400
people and offers emergency and
acute-care services.
Pocono Medical Center Laboratory provides clinical diagnostic
services to physician offices and
nursing homes in the area. The laboratory performs more than
300 different procedures, including
blood, tissue, and cell analysis, and preparation of blood for
transfusion.
Timely delivery and accuracy of results are chief quality
indicators for the laboratory, as well as
key inputs for overall quality of patient care. Therefore, the
laboratory workflow, including
pre-analytical, analytical, and post-analytical processes, has
always been central to the laboratory’s
quality management program.
The American Society for Quality ■ www.asq.org Page 1 of 4
http://www.asq.org/
A Scheduling Bottleneck
The Six Sigma methodology—define, measure, analyze,
improve,

and control (DMAIC)—supplied the framework for Hayes and
Cerra’s project. The physician satisfaction survey had already
underscored the need for delivering lab results earlier, thus pro-
viding a project focus and objective to begin the define stage:
• Because most physicians indicated a need for test results by
7 a.m., delivering all results by that time was the primary
focus of the project.
• Physicians with patients in the intensive care unit (ICU),
critical care unit (CCU), and progressive care unit (PCU)
expressed a need for results by 6 a.m. The team resolved to
meet the needs of these three units and find a way to deliver
their results even earlier.
Hayes and Cerra lobbied the hospital’s management and board
for approval to proceed, and the hospital selected Mary
Williams,
a vice president at Rath & Strong, a Lexington, Massachusetts-
based firm, to provide consulting services.
Defining the problem within the lab was easy. Lab technicians
could not forecast how many blood draws would need to be
done
the next morning until the middle of the night. “Unit secretaries
transcribe orders all day long for the morning,” Cerra says. “On
top of that, there are many situations in the ICU that have
sched-
uled draw times. Sometimes the patient has to fast for 12 hours
before drawing, which dictates that we draw in the morning.”

The phlebotomist would wait for eight to 10 blood draws to be
done before taking them down to the lab. That, in turn, caused a
backup in processing, late results, and delayed discharges for
some patients.
The Measuring Blitz
It was in the measuring phase that Williams and her team helped
the hospital the most. “They analyzed where every tube was,
through every stage of the process,” Cerra recalls, adding that
the consultants literally followed hospital personnel and tracked
the test tubes’ paths.
“If you just do a process map, you don’t see that tube of blood
• Collection of patient samples
• Delivery of tubes to the lab
• Front-end processing
• Actual running of the tests
Analyzing Flow, Implementing
Solution
s
Williams’ team ran the data it collected from following test
tubes
around the medical center through a number of statistical

analyses.
For a full list of the tools used during the measure and analyze
phases of the project, see “Statistical Tools Used.”
Statistical Tools Used
• Value stream maps (current and future)
• Detailed process map
• Time series plots
• Control charts
• Stratified frequency plots
• Cause and effect diagram
• Hypothesis tests (ANOVA, Moods Median test)
• Multiple regression
• Matrix plot
• Pareto charts
• Binary logistic regression
• Process capability
As the regression fitted line plots in Figure 2 show, the two
biggest drags on the process were the actual delivery of the test
tubes to the lab and their analysis. “The phlebotomist would
col-
lect 10 to 15 patient samples and return to the lab with a basket
of tubes all at once. This created a huge bottleneck,” says Cerra.

The issue quickly became how to avoid the batch collections on
the hospital floor and batch deliveries to the lab. “It became
obvious that we needed to have continuous flow into the lab.
We
shouldn’t be collecting from 16 patients and then taking those
tubes to the lab,” Hayes says.
Figure 1 Pocono Medical Center Laboratory
Process Map
sitting there or the other things that are not adding any value,”
Williams explains. “Walt (Hayes) walked us through the
process.
We walked through the hospital and the emergency room. We
talked to stakeholders and went to the front and back of the
lab.”
Figure 1 shows the process steps Williams mapped based on her
walk-through, including the drawing of a sample of blood,
delivery
to the lab, entry into the computer, putting the tube into a
centrifuge,

performing analysis work, and feeding results into the computer
for
physician access. Waiting times between process steps are also
represented to help identify delays that can be addressed.
Process mapping revealed that four basic areas were causing
great variability in test processing times:
Start
Wait
Median 2
Range 0-28
Wait
Median 3

Range 0-72
Draw
Median 2
Range 1-45
Enter to
Computer
Median 2
Range 2-6
Analyze
Median 16
Range 0-271
Wait
Median 34
Range 0-184

Centrifuge
Median 7
Range 0-20
Wait
Median 2
Range 0.5-280
Deliver to Lab
Median 5
Range 0-23
Give to
Analyst

Median 0
Range 0-7
Complete in
Computer
Median 0.5
Range 0.5-1
End
http://www.asq.org/
10 Days Before
Done by 6 a.m.

No 251
18%
Yes
Yes 56
Total 307
Done by 7 a.m.
No 57
81%
Yes
Yes 250
Total 307
10 Days After
Done by 6 a.m.
No 48

92%
Yes
Yes 534
Total 582
Done by 7 a.m.
No 0
100%
Yes
Yes 582
Total 582
LT
S
ta
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D

ra
w
to
C
om
p
C
om
p
LT
S
ta
rt
D
ra
w
to

C
om
p
C
om
p
Williams’ team returned to the process flow and learned that
one
tube—or even four tubes—of blood could be drawn in 71⁄2
min-
utes. Completing an assessment of the total number of draws per
floor, and of the lab’s processing, analyzing, and delivery
capac-
ity, led to a surprisingly simple solution: Designate a “runner”
to
bring test tubes from the floor to the lab every 15 minutes. A
lab
person is assigned to pick up tubes at key points on the hospital
floor. The phlebotomist on the floor places a flasher light
outside
the patient room so the runner can quickly find the

phlebotomist.
Under the new model, work can keep advancing every 15 min-
utes. “The model worked surprisingly well and we didn’t have
to
increase our staff,” Hayes says, although one lab technologist
was promoted to senior technologist to oversee the new process.
Fast Payback
Figure 3 shows a comparison of results from before and after
the
Six Sigma/Lean redesign of the lab collection process. For a
sample
of 920 blood results delivered before the redesign, 68% reached
the
appropriate doctors by 7 a.m. For a sample of 1,020 results
using the
new process, the percentage delivered by 7 a.m. increased to
98%.

Figure 2 Key Drivers of Total Lead Time
Fitted Line Plot
LT Start Draw to Comp Comp = 44.24 + 1.076 Time to Lab
Even more dramatic improvements occurred with tests delivered
to
the ICU, CCU, and PCU, the three units requesting results by 6
a.m.
Figure 4 provides a closer look at results for these units for 10
days
before the redesign and 10 days afterward, showing that the per-
centage of results delivered by 6 a.m. increased from 18 to 92.
“We did this really, really fast and we got our payback really,
really
fast,” Hayes says. Cerra agrees that the rewards of the project
have
been “tremendous” and notes, “The physicians are really
happy.”
According to Williams, the Pocono Medical Center’s Six
Sigma/Lean project was unique in that it was completed so
quickly: “We did this in about six weeks. Frequently, these

types
of projects take nine to 12 months.” She jokingly adds, “We
really beat everyone over the head.”
Hayes says the project “was designed as an accelerated project.
No one was really sure that we could get it done this quickly.”
But employees and management worked hard to keep the
momentum strong. Having the buy-in of the medical center staff
was key to fast progress. Williams calls the staff involved with
the project “top notch,” commenting, “They were right with us
all the way and they did a bang-up job.”
Project results also extend outside of the laboratory. Hayes has
observed a decrease in overall length of stay, an improvement
he
attributes in part to the faster lab process. Another factor in
help-
ing the medical center discharge patients in a timely manner is
its use of hospitalists, medical doctors whose specialty is caring
160
140

0
0 10 20 30 40 50 60 70 80 90
Time to Lab
Fitted Line Plot
LT Start Draw to Comp Comp = 44.48 + 1.112 Chem
0 10 20 30 40 50 60 70 80 90
Chem

S 13.9088
R-Sq 66.9%
R-Sq (adj) 66.9%
S 20.4845
R-Sq 29.0%
R-Sq (adj) 28.9%
for hospitalized patients. The medical center has seven hospital-
ists who care for patients who have been admitted by another
physician. Although both the lab process and the use of

hospital-
ists ultimately help improve overall length of stay, the precise
impact of each has not yet been officially measured.
Figure 3 Before and After: Lab Results Delivered
by 7 a.m.
Delivered
after 7 a.m.
Delivered
by 7 a.m.
Total
% Delivered by
Target Time
Process
Sigma

Before 294 626 920 68% 1.97
After 21 999 1020 98% 3.54
Figure 4 10 Days Before and After Redesign Results
for ICU, CCU, PCU
While each step drives total lead time, we see a strong
relationship with
time to lab, and a moderate relationship with Chemistry (time
analyst
receives to complete in computer).
http://www.asq.org/
Maintaining the Gains—Every Day

Pocono Medical Center recognizes that the improved laboratory
process will only continue to be effective as long as a number
of
factors remain in place. A well-respected staff member must be
present to manage the process during the crucial hours of 3 a.m.
to 7 a.m., enough phlebotomists must be on staff, and runners
must consistently make their regularly scheduled rounds.
Above all, the process must continually be measured, and meas-
urements must be reported in time to take corrective action
when
needed. To ensure that the right measurement activity occurs, a
control plan prescribes daily review of the following measures:
• Percent of results delivered on time—troubleshooting occurs
if the outcome ever falls below 95%
• Number of late tests and reason for delay—troubleshooting
occurs for all late results
• The variance of actual versus expected results for cumulative

tubes every 15 minutes—troubleshooting occurs if the
variance exceeds 10%
Because daily measurement is necessary, maintaining results
remains an ongoing effort. The laboratory process project team
made progress quickly using Six Sigma and Lean, but holding
the gains requires effort from the entire Pocono Medical Center
staff every day.
For More Information
• Learn more about the Pocono Medical Center at
http://www.poconohealthsystem.org and the Pocono Medical
Center Laboratory at http://www.pmclab.org/.
• Access more case studies, how-to articles, and other informa-
tion about using Six Sigma in healthcare by visiting
http://www.asq.org/healthcaresixsigma/.
Article Contributors

Walter T. Hayes is currently the administrative director of
Laboratory Services at the Pocono Medical Center in East
Stroudsburg, Pennsylvania. He holds a bachelor’s degree in
chemical engineering from the University of Pittsburgh and a
master’s degree in public administration from Cleveland State
University. Having started his healthcare career at the
Cleveland
Clinic as the manager of Laboratory Computer Systems and then
manager of the Primary Laboratory Center, Hayes has held
administrative director positions at several other hospitals and
healthcare systems in Ohio and Virginia. He has also served as
chair of the North East Ohio Red Cross Health Care
Administration Advisory Board and vice chair of the Greater
Cleveland Hospital Association’s Regional Reference
Laboratory
Alliance Project.
Carmine J. Cerra is a practicing anatomic and clinical patholo-
gist with 24 years of experience in the laboratory environment.
He has been past chief of the medical staff and is the current
department chair in pathology and the medical director of the
lab
at Pocono Medical Center. A teaching assistant at nearby East
Stroudsburg University, Cerra has published through the

Pennsylvania Academy of Sciences. He is the chair of the
Medical Staff Performance Improvement Committee.
Mary Williams, a vice president at Rath & Strong, Aon
Consulting, assists senior-level executives, training teams,
facili-
tators, and middle managers with Six Sigma, process
improvement, and redesign. Previously, she was a vice president
with the Juran Institute, where she focused on the healthcare
industry. She received her B.S. from Columbia University, her
MBA from the University of the Virgin Islands, and her R.N.
from St. John’s Hospital, New York City, and has completed
advanced studies in operations management in the service
industry at the Massachusetts Institute of Technology.
http://www.asq.org/
http://www.poconohealthsystem.org/
http://www.poconohealthsystem.org/
http://www.pmclab.org/
http://www.asq.org/healthcaresixsigma/
http://www.asq.org/healthcaresixsigma/

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