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Laralex Hospital Case Study quention, writting report (Operatios Management)

Read the case study document and case data, complete all questions below:Your case study analysis will prepare Blanche with explanations that she can present to her management at the next staff meeting. In preparation for this meeting, you will write a report as if you were Blanche.Blanche’s focus will be on highlight problems with the current system and convincing hospital management to allow her to explore the implementation of a new process-oriented approach. Throughout, she will need to use words that Hazel (and other similar-minded managers) would understand, and terms that make sense within a health care facility. That is, she should avoid generic phrasing (e.g., process, defect, or nonconformance) and focus on applications at Laralex Hospital. The report should address the following issues, using examples and/or analogies as illustrations. It should be concise, clear and complete. Because the reader of the report will not be aware of the questions below, she should not simply repeat the questions when developing your report (the report should flow nicely while addressing these issues).Case Question:1. Explain what is wrong with using percentiles to compare hospitals. Give an example (not the tomato example from the case) that illustrates why percentiles are ineffective.

Laralex Case Study Data
Hospital Acquired
Infections
Cesarean
Section
Procedures
Discrepant X-Rays
Unscheduled
Readmissions
Patients Who
Leave the ED Prior
to Treatment
Month
Patient-Days
No.
Births
No.
Patients
No.
Patients
No.
Patients
No.
1
5225
22
119
32
488
8
310
6
604
6
2
5515
20
111
27
573
3
294
5
575
10
3
5872
15
111
32
489
6
337
14
593
7
4
5398
22
125
28
420
4
253
10
641
6
5
5017
26
99
27
503
6
293
10
601
11
6
5273
17
127
27
580
7
300
4
649
9
7
4824
20
121
25
419
8
319
10
658
11
8
5340
21
117
32
442
4
199
7
552
11
9
5307
14
133
30
407
3
263
11
536
9
10
5507
20
106
23
553
9
259
5
554
11
11
4189
22
120
27
466
3
285
14
708
11
12
4378
17
123
33
551
4
275
11
547
12
13
4620
20
114
29
485
10
320
13
589
16
14
5869
27
128
19
427
7
329
12
596
12
15
4975
21
117
19
540
9
243
11
685
18
16
4969
19
115
21
568
3
278
8
640
15
17
5792
17
104
22
531
9
365
6
659
17
18
4939
22
128
20
558
5
348
11
609
16
19
5616
16
120
24
474
4
290
8
438
14
20
5061
11
121
25
594
9
321
7
522
13
21
5262
20
102
21
540
2
253
9
574
16
22
4808
26
107
18
553
9
266
10
539
18
23
5280
20
118
24
556
11
301
11
634
21
24
5491
24
116
22
541
7
348
9
610
22
BOSTON
METROPOLITAN COLLEGE
UNIVERSITY
DEPARTMENT OF ADMINISTRATIVE SCIENCES
LARALEX HOSPITAL1
FRIDAY, JUNE 2
Blanche Davis had just completed her third year as Director of Quality at Laralex Hospital, a medium-sized
not-for-profit facility located in a growing region of the Southeastern United States. Laralex Hospital, a member
of the Southeast Medical Care Group, offers a wide range of services to patients who typically belong to one of
the three major managed care providers in the area. The 260-bed facility includes numerous departments such as
maternity, emergency, cardiac care, diagnostic testing, and medical imaging. Blanche’s primary responsibility
was maintaining the Hospital’s accreditation status, which the Board of Directors considers critical to long range
viability. In order to maintain accreditation, the hospital must submit to audits (both through written
documentation and on-site visits) designed to evaluate its operations against recognized best practices. The
hospital must also provide the agency with periodic updates, including routine ongoing performance data.
Flexibility exists relative to the procedures used at individual hospitals to evaluate performance. Many hospitals
use external performance benchmarking systems. These systems are administered by independent organizations
that collect data from participating hospitals, then place each facility into a peer group of similar facilities, so that
a fair comparison may be performed. The organization used by Laralex Hospital charges $8,000 per year for their
service. In addition, Laralex employs other benchmarking organizations, such as an organization that analyzes
data from patient satisfaction surveys. Table 1 includes a sample of performance data collected at Laralex
Hospital.
Neonatal Mortality Rate
Hospital-Acquired Infections Rate
Surgical Wound Infections Rate
Inpatient Mortality Rate
Diagnostic Testing False Positive Rate
Patient Satisfaction Rate (Based on Surveys)
Cesarean Section Birth Rate
Rate of Patients Who Leave Emergency Department Prior to Service
Rate of Unscheduled Readmissions to the Hospital
Rate of Positive/Negative HIV, Hepatitis and Other Laboratory Results
Biopsy Results (Positive/Negative)
Medication Error Rate
Discrepant X-Ray Report Rate
Rate of Pap Smear Results by Category
Table 1: Selected Performance Measures at Laralex Hospital
Blanche Davis had worked at Laralex Hospital for 24 years, ever since completing her education and
becoming a registered nurse. She had held a variety of professional and administrative positions in the hospital
and was well respected for her understanding of all internal operations. One morning as Blanche arrived for
work, she found the most recent quarterly benchmark analysis which compared the performance data generated
by Laralex with those of competitors. There was also a voice mail message from Hazel Wisely, Vice President of
This case was developed by John Maleyeff and F.C. Kaminsky based on their work in applying quality management
principles in healthcare settings. All references to people and organizations are fictional. © 2018 (Rev) All rights reserved.
1
Laralex Hospital Case Study
Page 1
Quality Assurance and Risk Management. “Blanche, take a look at the latest benchmark report. Our results for
hospital acquired infections, x-ray report discrepancies, and unscheduled readmissions are way up. I am
especially concerned about the increase in hospital acquired infections. What’s going on?” Blanche opened the
report and found that the rate for hospital acquired infections (an infection that occurred during a patient’s stay in
the hospital that was not present when the patient arrived) was 4.5 per 1000 patient-days and the corresponding
percentile ranking (compared to the other hospitals in Laralex’s peer group) was 86 (meaning that the infection
rate at Laralex was higher than 86% of the peer group hospitals). This measure was highlighted since in the
previous quarter, the infection rate was only 2.9 per 1000 patient-days and the percentile ranking was 22. Similar
results were found for x-ray report discrepancies (a jump from 12 to 68 in percentile ranking) and unscheduled
readmissions (an increase from 32 to 91 in percentile ranking).
These types of requests were not new to Blanche. She generally received them whenever a report comparing
Laralex with other hospitals was generated. As a result of such a request, Blanche would make some calls and
visit the departments responsible for each performance measure. Typically, the department manager’s first
response would be similar to that of Bill Karinsky who managed the x-ray department and was Blanche’s first
stop. “As far as I know, we haven’t made any changes that would impact discrepancies, but I’ll take a look.” If the
meeting proceeded in a typical manner, Bill would then talk to his technicians and get back to Blanche with his
best guess as to the reason for the increase. In most cases, the data from the next quarterly performance
benchmark report would show an improvement, and the issue will be forgotten.
Two things, however, had always disturbed Blanche. The first was that the number of requests to track down
reasons for performance problems consumed a significant portion of her time and the frequency of these requests
seemed to be unchanged over the last three years. The second was that rarely was there a definitive root cause
identified and the long-term data appeared to indicate no real improvements in the hospital’s performance.
But, she was too busy to worry about those issues, since she needed to meet with the managers responsible
for the two other performance measures whose percentile rankings also had slipped.
SATURDAY, JUNE 3
Blanche’s main form of relaxation was tending to her vegetable garden, which was admired by her neighbors
both for its impeccable organization and for the vegetables themselves, which the neighbors often found
unexpectedly on their side porches. On this morning, while tending to her 12 tomato plants, Blanche had a
strange feeling of déjà vu. The thought passed for a moment, then she realized that she was thinking about the
sizes of tomatoes on her 12 plants. In particular, she had 12 plants that were planted in the same soil from the
same seed packet by the same gardener, and maintained in the same manner. The plants are produced tomatoes
in essentially equal amounts, both in size and quantity. Also, the occurrence of “bad” tomatoes seemed to happen
uniformly across the 12 plants. Yet, individual tomatoes picked from a plant would exhibit quite significant size
variation. And, as she periodically picked the bad tomatoes from the plants each Saturday, the number of both
good and bad tomatoes picked from an individual plant varied from Saturday-to-Saturday.
But why these thoughts involving the field of statistics on a Saturday morning? After all, it had been about 25
years since her last statistics class, and Blanche recalled very little about what went on in that class other than
some confusing probability calculations involving urns and playing cards. Then it hit her. She was somehow
relating the plants to hospitals and also relating tomatoes to performance measures. That is, the plants could
represent 12 hospitals in a peer group that were all designed and managed identically and served similar
populations. So, even though 12 hospitals could be essentially the same, the occurrence of discrepant x-ray
reports is subject to random variations. Hence, performance measures for identical hospitals must vary both
over time within each hospital and also from hospital-to-hospital for the same time period (just like the
occurrence of bad tomatoes on the 12 plants). If this analogy were accurate, then one hospital’s performance
within a group of identical peers could just as likely be the minimum for the peer group, or the maximum for the
peer group, or any place in the middle. Could this mean that the percentile ranking could vary from 0-100 with
Laralex Hospital Case Study
Page 2
equal likelihood? If so, then a percentile ranking change from 22 to 86 (that occurred for hospital acquired
infections) may mean nothing at all!
Blanche couldn’t wait to get to work on Monday.
MONDAY, JUNE 5
Blanche arrived early for work. The first thing she did was make coffee, since she arrived before any of her
support staff and was anxious to get to work exploring the performance data. Using her limited database
management skill, she accessed the raw data for hospital acquired infections over the past two years. For
reporting purposes, the data had been recorded using monthly time intervals and then summarized by quarter.
To save time, Blanche used the monthly performance values. She quickly downloaded the data (number of
infections and number of patient-days by month, typically around 5,000) and calculated the infection rate over the
2-year period. The moment after clicking the “finish” button in her spreadsheet’s graph wizard, she knew her
hunch was confirmed. The graph she looked at is shown in Figure 1.
Hospital Acquired Infections
0.0055
0.0050
Proportion
0.0045
0.0040
0.0035
0.0030
0.0025
0.0020
2
4
6
8
10
12
14
16
18
20
22
24
Month
Figure 1: Run Chart for Hospital Acquired Infections
By this time, her assistant Victor Minkfield was at his desk. Blanche called him in and asked for his
interpretation of the graph, stating only that the data showed proportions, by month, over two years. Victor, with
no experience in statistics but with some training as an engineering technician, immediately said “noise.” After
Blanche explained the source of the data, Victor interpreted the graph as showing an average infection rate of
about 0.4% (4 infections per 1000 patient-days) with monthly values bouncing around this average due to
randomness. In fact, for a given month, it looked as though one could anticipate the rate to vary from about 2 per
1000 to about 6 per 1000. But Blanche was skeptical. Could this pattern be simply “noise” (a natural fluctuation
you would expect in data of this type), or could the rate really be changing frequently due to inconsistent hospital
procedures?
To get help answering this question, Blanche decided to call her long time friend, Dr. Ralph Connors, a
professor of statistics at the local college. After explaining her problem, Ralph said “That’s easy, Blanche, what
you have is a sequence of random variables that are independent and identically distributed and which follow a
binomial distribution. The value of sigma for this random variable is equal to the square root of the product p
times one minus p divided by the square root of n. Blanche, I have to get to class. Give me a call later if you need
more help.”
After making a note on her Rolodex not to call Ralph about statistics again, Blanche tried another friend, Dan
Marley, a quality manager for Duran Plastics, who she knew dealt with quality data resulting from
manufacturing operations. After listening to Blanche’s description of the data, Dan replied,
Laralex Hospital Case Study
Page 3
“This is easy. What you did was create a run chart for a quality outcome that we call an attribute. A run chart
displays an outcome, over time, and is used to determine whether or not the system is operating in a stable,
predictable manner. In this case, for any patient, the outcome is either ‘infection’ or ‘OK,’ and you plotted the
proportion of discrepant outcomes. We usually plot these types of data on a similar chart called a P Chart,
which is a statistical control chart designed for proportion data (data with a numerator and a denominator). The
only difference between a P Chart and a run chart is that the P Chart contains what we call ‘control limits.’
Assuming that your hospital is operating in a consistent way, these control limits would give us a guide as to the
range of values expected to contain the monthly infection rate. Let me get my calculator…okay, in your case if
the average infection rate is 4 per 1000, and the number of patient days in a given month averages about 5000,
the control limits will extend from 1.3 per 1000 on the low side to 6.7 per 1000 on the high side. The formula to
get these numbers is not too difficult, but does require some training. In the meantime, a run chart may be all
you need to get a good handle on how your processes are behaving.”
Blanche was a little confused about what Dan meant by “how your processes are behaving,” but she was due to
attend a meeting and they agreed to have lunch the next day to continue the discussion.
TUESDAY, JUNE 6
Lunch with Dan consisted of a two-hour long tutorial on the philosophy behind statistical process control
(SPC). Dan summarized the philosophy underlying SPC as follows:
“Years ago, we in manufacturing treated quality assurance as the filtering out of substandard products through a
comprehensive inspection process. In other words, we tried to separate good products from bad products. We
sent the good products to our customers and reworked or scrapped bad products. Since inspections were time
consuming and costly, we used statistical sampling theory to inspect a small portion of a larger batch, then either
rejected or accepted the entire batch based on the status of the sample. Sampling was usually done at the front
end of the manufacturing system to judge the quality of incoming materials, and at the back end to judge the
quality of final products. This was the standard mode of operation until the late 70’s, when we began to notice
that other countries, led by the Japanese, were becoming more and more competitive, due mainly to the quality
of their products. We asked ‘why.’
“Then, little by little, we began to hear about the philosophies of Dr. W. Edwards Deming. Deming’s basic
premise was that to be competitive, quality must be the number one concern. Superior quality drives down costs
since so much of a manufacturing operation involved correcting problems. Also, in a competitive world, quality
must be continuously improved. Traditional sampling methods were not doing the job, mainly because they
were not designed to diagnose causes of quality problems. He pushed the idea that the only way to improve
quality was to understand how processes were behaving and then to improve the processes that needed
improvement. This approach has become known as a ‘process-oriented’ approach to quality, replacing the old
‘product-oriented’ approach. So, in a nutshell, the traditional sampling methods weren’t doing the job; a
different kind of statistical philosophy was needed.”
Blanche interrupted, “That’s fine for manufacturing, but what’s that got to do with a hospital? We generally use
all of the data throughout our system to judge quality. And, except for patient surveys, there’s not a lot of
sampling.” Dan countered, “Yes, but we aren’t using your data to understand your processes, and anyway, all of
your data are really samples. Take hospital acquired infections. The patients you serve over one month, or one
quarter, or for that matter one year represent only a portion of all the patients who ultimately will use your
hospital.”
“You’re getting a little abstract on me, Dan,” said Blanche, “What I really want to know is the connection
between this process-oriented philosophy and the run chart I developed yesterday. I also need to know how I
can use these charts to improve quality.” Dan was prepared to answer this question using a simple card game he
developed for the training sessions he delivered to employees in his facility. He proceeded to take a deck of
ordinary playing cards from his briefcase and stated his premise. “I’ll use these cards to make my point. Let’s say
that the deck of cards represents the hospital. Now, if I draw a card from the deck, I have generated one patient
who stays in the hospital for one day. If the card turns out to be a club, the patient acquired an infection during
his or her stay; otherwise, the patient did not acquire an infection. Okay so far?”
Laralex Hospital Case Study
Page 4
Blanche responded by explaining that the cards are not representative of a hospital since we have perfect
knowledge of the deck. Dan responded, “No you don’t. I never said that the deck was fair; in fact, I have loaded
the deck with a certain configuration that only I know. Now, let’s generate the arrival of 10 patients, which will
represent one month’s worth of business for the hospital.” Dan drew one card, which turned out to be a diamond,
then another (a spade), then a third (a club), and so on, replacing each card and shuffling quickly after each card
was drawn. The ten patients produced 2 clubs. Dan summarized, “So, for month #1, 10 patients stayed in the
hospital for one day each, and two of those patients acquired an infection.” Dan took another deck of cards from
his briefcase and used the same procedure to generate another “month” of 10 patients. This time, 3 clubs were
generated, representing 3 patients acquiring infections. This process continued until 20 decks were used to
simulate a total of 200 patients over 20 months (10 patients per month). Table 2 shows the data and Figure 2
shows the corresponding run chart for the 20 months.
Month
1
2
3
4
5
Clubs
2
3
4
2
3
Month
6
7
8
9
10
Clubs
5
2
4
3
5
Month
11
12
13
14
15
Clubs
1
5
2
6
3
Month
16
17
18
19
20
Clubs
3
2
3
2
5
Table 2: Data For Card Game
Occurrence of Clubs
6
Proportion
5
4
3
2
1
2
4
6
8
10
12
14
16
18
20
Month
Figure 2: Run Chart for Card Game
Dan grabbed his calculator and found that the total number of clubs drawn was 65 which, after dividing by
200, yielded a proportion of 32.5%. He then sketched a horizontal line on the chart corresponding to the 32.5%
value. He continued, “Now, if you knew nothing about this hospital and were given this run chart, what would
you conclude about the occurrence of hospital acquired infections? And, as a manager, how would you respond
to the last data point, which was a fairly large increase over the previous month?” Blanche studied the charts.
“First of all, it looks like the infection rate varies from month to month, but follows a steady average rate of 32.5%.
The last month is an increase from the month before, but well within the random variation seen in the prior
months, so I would take no action.”
Dan responded, “Great. You have just about perfect knowledge of this process. The decks are identical. Each
deck contains 50 cards and 17 clubs, or 34%. In other words, you have a hospital whose operation is unchanged
over time, wher…

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