You will see the questions in case study questions file. I also attached the classmate work. You can refer it but don’t copy it.?? Due in 12 hoursLaralexHospitalCaseStudyData.xl

Laralex

BOSTON UNIVERSITY

METROPOLITAN COLLEGE DEPARTMENT OF ADMINISTRATIVE SCIENCES

AD 605 Operations Management

Laralex Hospital Case Study

Study the case study document. Then, answer the following questions.

1. Explain what is wrong with using percentiles to compare hospitals.

It is wrong to use percentiles when comparing hospitals because even though we can have the same process and 12 hospitals could essentially be the same, the occurrence of outcomes would differ across hospitals over a period. Performance data for identical hospitals will vary both over time within each hospital and from hospital-to-hospital for the same period due to random variation. One hospital’s performance within a group of peers can be just as likely placed in the minimum, middle or maximum. In summary, the changes from a lower ranking to a larger one could meat nothing at all and it just is random variation over time.

2. Create and interpret a P Chart for each of the outcomes analyzed in the case (an Excel file with the data is provided). Interpret each P chart based on the Shewhart interpretation rules. Use the Excel Control Chart template.

Infections: This process seems to be stable according to Shewhart rules as no points are below or above the LCL or UCL lines. In addition, no significant number of points in a row between 1 and 3 sigma limits or below or above the center line.

Cesarean Procedures: This process violates the Shewhart rule of 8 points in a row below or above the center line and 10 of 11 points in a row below or above the center line.

This study source was downloaded by 100000849136748 from CourseHero.com on 06-22-2022 22:07:44 GMT -05:00

https://www.coursehero.com/file/88046253/metad605-Laralex-Case-Study-docx/

Discrepant X-Rays: This process seems to be stable as it does not violate any of Shewhart’s rules.

Unscheduled Readmissions: This process seems to be stable as it does not violate any of Shewhart’s rules.

This study source was downloaded by 100000849136748 from CourseHero.com on 06-22-2022 22:07:44 GMT -05:00

https://www.coursehero.com/file/88046253/metad605-Laralex-Case-Study-docx/

Patients Leave ED Prior to Treatment: This process is unstable as it violates the rule of 8 points in a row above or below the center line. In addition, 10 of 11 points in a row below or above the center line.

3. For processes that are stable, compare Laralex’s performance with external benchmarks. Use the Excel Control Chart template. Assume that a very large group of peer hospitals had the following average proportions:

This study source was downloaded by 100000849136748 from CourseHero.com on 06-22-2022 22:07:44 GMT -05:00

https://www.coursehero.com/file/88046253/metad605-Laralex-Case-Study-docx/

a. Discrepant X-rays – 1.11%

i. Performance is consistent with this benchmark of 1.11% since it falls within our confidence interval of 1.03% and 1.42%.

b. Unscheduled Readmissions – 4.6%

i. Performance is lower than our benchmark of 4.6% and this is higher than our entire confidence interval of 2.7% and 3.6%.

c. Hospital-Acquired Infections – 0.30%

i. Performance exceeds benchmark since our entire confidence interval of .35% and .42% is above this benchmark of 0.30%.

d. Cesarean Sections – 19.2%

e. Patients who Leave the ED Prior to Treatment – 3.3%

4. List and discuss the three most important challenges faced by Blanche when implementing a process improvement program based on Lean Six Sigma at Laralex Hospital (justify them with specific examples from the case study document).

Changes to organizational infrastructure may not be understood by internal staff members especially by the nurses who are represented by a Union and has past actions of not cooperating with hospital leadership working rules unless it directly benefited them. This can pose in an issue if they feel that this new system would paint them in a bad light. A healthy process improvement program requires workers to communicate mistakes or close calls even if they committed them to track performance of the process. Nurses may not want to communicate these mistakes due to fear of being fired. The final challenge in improving the reporting process is the interpretation of reporting problems by external stakeholders. With all improvements there is a learning curve as employees learn the new methods and this may be seen as problematic with the culture of litigation associated with healthcare.

Turn in one Word file (on Blackboard). Make sure you put the course name, case study title, and date on top of the first page. Put all analysis in detail in the Word file (i.e., do not attach an Excel file). You do not need to write a case report – providing answers to each of the questions above will suffice.

This study source was downloaded by 100000849136748 from CourseHero.com on 06-22-2022 22:07:44 GMT -05:00

https://www.coursehero.com/file/88046253/metad605-Laralex-Case-Study-docx/ Powered by TCPDF (www.tcpdf.org)

Laralex Hospital Case Study Page 1

BOSTON UNIVERSITY

METROPOLITAN COLLEGE

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

1 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.

Laralex Hospital Case Study Page 2

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 3

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.

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,

24222018161412108642

0.0055

0.0050

0.0045

0.0040

0.0035

0.0030

0.0025

0.0020

Month

P ro

p o

rt io

n

Hospital Acquired Infections

Laralex Hospital Case Study Page 4

"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 5

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 Clubs Month Clubs Month Clubs Month Clubs

1 2 6 5 11 1 16 3

2 3 7 2 12 5 17 2

3 4 8 4 13 2 18 3

4 2 9 3 14 6 19 2

5 3 10 5 15 3 20 5

Table 2: Data For Card Game

<p

Collepals.com Plagiarism Free Papers

Are you looking for custom essay writing service or even dissertation writing services? Just request for our write my paper service, and we'll match you with the best essay writer in your subject! With an exceptional team of professional academic experts in a wide range of subjects, we can guarantee you an unrivaled quality of custom-written papers.

Get ZERO PLAGIARISM, HUMAN WRITTEN ESSAYS

Why Hire Collepals.com writers to do your paper?

Quality- We are experienced and have access to ample research materials.

We write plagiarism Free Content

Confidential- We never share or sell your personal information to third parties.

Support-Chat with us today! We are always waiting to answer all your questions.