BOSTON METROPOLITAN COLLEGE UNIVERSITY DEPARTMENT OF ADMINISTRATIVE SCIENCES AD 605 Operations Management Laralex Hospital Case Study (Due November 25, 11:59 PM) Write your report as if you were Blanche. Do not assume that the reader of the report has read the case study document. Include a cover page with the course name, case study name, the date, and your team member names. Submit only one Word file (using Blackboard). In the report, Blanche’s will need to use words that Hazel (and other managers) would understand, and terms that make sense within a health care facility. That is, she should avoid generic phrasing (e.g., process, defect, etc.) and focus on processes and data at Laralex Hospital. The report should be concise, clear and complete. Include answers to the questions below in your report but do not list the questions followed by answers - the report should flow nicely while addressing these issues. Address the following in your report: 1. Explain what is wrong with using percentiles to compare hospitals. Give an example (not any examples from the case or in class – especially no coin flip examples) that illustrates why percentiles are ineffective. 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. 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: a. Discrepant X-rays - 1.11% b. Unscheduled Readmissions - 4.6% c. Hospital-Acquired Infections - 0.36% d. Cesarean Sections - 19.3% e. Patients who Leave the ED Prior to Treatment – 3.2% 4. Explain how a comprehensive process improvement program based on Lean Six Sigma will help with their accreditation. Use specific examples derived from the case study document. 5. 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). BOSTON UNIVERSITY METROPOLITAN COLLEGE DEPARTMENT OF ADMINISTRATIVE SCIENCES LARALEX HOSPITAL1 Blanche Davis just completed her third year as Director of Quality at Laralex Hospital, a medium-sized notfor-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 is maintaining the Hospital's accreditation status, which the Board of Directors considers critical to the Hospital’s long-term viability. In order to maintain accreditation, a hospital must submit to audits, both through written documentation and on-site visits, designed to evaluate its operations against recognized best practices. Hospitals 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 comparison may be made. The organization used by Laralex Hospital charges $12,500 per year for their service. In addition, Laralex employs another firm that analyzes data from patient satisfaction surveys. Table 1 includes some of the 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 has worked at Laralex Hospital for 24 years, ever since completing her education and becoming a registered nurse. Having held a variety of professional and administrative positions in the Hospital, she is 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 Laralex’s performance data to its peer group of hospitals. There was also a voice mail message from Hazel Wisely, Vice President of Quality Assurance and Risk Management. "Blanche, take a look at the latest benchmark report. Our results 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. © 2020 (Rev) All rights reserved. Laralex Hospital Case Study Page 1 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 a patient experiences that was not present at the time of admission) was 4.5 per 1000 patient-days and the corresponding percentile ranking (compared to the other hospitals in Laralex's peer group) was 86. This percentile means that the infection rate at Laralex was higher than 86% of the peer group hospitals. The hospital-acquired infection rate was highlighted because, in the previous quarter, the infection rate was only 2.9 per 1000 patient-days and the percentile ranking was 22. Blanche knew that these infections could be due to many causes within almost any department in the hospital, such as personnel not washing their hands according to the hospital’s protocol, not properly sterilizing treatment devices, or allowing patients to move unattended around the hospital, to name a few. The latest benchmarking report also showed similar results for x-ray discrepancies (a jump from 12 to 68 in percentile ranking) and unscheduled readmissions (an increase from 32 to 91 in percentile ranking). Blanche knew that x-ray discrepancies could be caused by patients not being instructed properly, improper use of the x-ray device, or device malfunctions, to name a few. She also knew that unscheduled readmissions had many potential causes. These types of requests were not new to Blanche. She generally received them whenever a quarterly report comparing Laralex with other hospitals was generated. In response to these requests, Blanche would make a few 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 interaction proceeded in a typical manner, Bill would 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 would be forgotten. Two aspects of the benchmarking system have continued to disturbed Blanche. First, the number of requests to track down reasons for performance problems consumed a significant portion of her time. The frequency of these requests seems to be unchanged over the last three years. Second, rarely was a definitive root cause identified. The long-term data appears to indicate no real improvements in the hospital's performance. On this day, she was too busy to worry about these issues, because she needed to meet with the managers responsible for the two other performance measures whose percentile rankings had slipped. Then, she was off to a one-week training program on Six Sigma and she needs to start packing. She also needs to arrange for a friend to feed her cat and water her garden plants. One Month Later… Ever since the Six Sigma training course ended, Blanche could not stop thinking about something said by the head trainer, Professor Robert Cavanaugh. When recommending procedures for interpreting the meaning of performance data, Professor Cavanaugh stressed that the outcomes of a process will change, even when the process remains the same. In fact, as she tended to her garden plants, Blanche had a strange feeling of déjà vu. The thought passed for a moment, and then she realized that she was thinking about the tomatoes on her 12 plants. In particular, she had 12 plants that were rooted in the same soil. They came from the same seed packet, they were planted by the same gardener, and they were maintained in the same manner. The plants are produced tomatoes in essentially equal amounts, both in size and quantity. In addition, the occasional "bad" tomatoes seemed to occur uniformly across the 12 plants. Yet, individual tomatoes picked from a plant would exhibit quite significant size variation. 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. Did this mean the outcomes changed (e.g., the size and number of bad tomatoes) while the process remained the same? Blanche came to realize that plants were like hospitals and tomatoes were like patients. The 12 plants would be analogous to 12 hospitals in a peer group that were all managed identically and served similar populations. Even though the 12 hospitals could be essentially the same, the occurrence of outcomes (such as hospital-acquired infections) would differ across hospitals over a period (such as one quarter). Professor Cavanaugh referred to these differences as random variations. Performance data for identical hospitals will Laralex Hospital Case Study Page 2 vary both over time within each hospital and from hospital-to-hospital for the same 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 peers could just as likely be the minimum of the peer group, or the maximum of the peer group, or any place in the middle. Could this mean that the percentile ranking could vary from zero to 100 with equal likelihood? If so, then a percentile ranking change from 22 to 86 (which occurred for hospitalacquired infections) may mean nothing at all! Blanche arrived early for work on the following Monday. 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 each month’s infection rate over the 2-year period. After choosing the time series graph option and clicking the OK button, she knew her hunch was confirmed. The “run chart” she created is shown in Figure 1. Hospital Acquired Infections 0.0055 Proportion 0.0050 0.0045 0.0040 0.0035 0.0030 0.0025 0.0020 2 4 6 8 10 12 14 Month 16 18 20 22 24 Figure 1: Run Chart for Hospital-Acquired Infections Blanche recognized the pattern shown on the run chart for hospital-acquired infections. The graph was similar to many examples she saw during her Six Sigma training – it appeared to be a display of random variation. She noticed that the run chart showed an average infection rate of about 0.4% (4 infections per 1000 patient-days) with monthly values varying around this constant average. 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. Could she be looking at a process that is unchanged, with the data changing merely due to random variation? Or, was the likelihood of an infection actually changing in the hospital over that period? Blanche left for a midmorning staff meeting. On Tuesday, Blanche accessed the database containing performance data over the last two years. She created run charts for five performance outcomes, including the three outcomes that she was asked about last month. Figures 2-5 contain the run charts for the proportion of births having a Cesarean Section (CSection) procedure, the proportion of discrepant x-ray reports, the proportion of unscheduled readmissions to the hospital, and the proportion of patients who left the emergency department (ED) prior to treatment. It seemed to Blanche that random patterns of variation existed for all but two of the performance outcomes. In the case of C-Section births (Figure 2), there appeared to be a change in the process (the proportions seemed to drop suddenly, then maintain a consistent level). In the case of patients who left the ED prior to treatment (Figure 5), there appeared to be a steady increase over time. Neither of these outcomes had previously been highlighted using the peer group comparisons based on percentiles. The other outcomes, including hospitalacquired infections, seem to show random variation with no process changes. Laralex Hospital Case Study Page 3 C-Section Births 0.300 0.275 Proportion 0.250 0.225 0.200 0.175 0.150 2 4 6 8 10 12 Month 14 16 18 20 22 24 20 22 24 22 24 Figure 2: Run Chart for C-Sections Discrepant X-rays 0.0225 0.0200 Proportion 0.0175 0.0150 0.0125 0.0100 0.0075 0.0050 2 4 6 8 10 12 14 Month 16 18 Figure 3: Run Chart for Discrepant X-rays Unscheduled Readmissions 0.05 Proportion 0.04 0.03 0.02 0.01 2 4 6 8 10 12 14 Month 16 18 20 Figure 4: Run Chart for Unscheduled Readmissions Laralex Hospital Case Study Page 4 Left ED Prior to Treatment 0.035 Proportion 0.030 0.025 0.020 0.015 0.010 2 4 6 8 10 12 Month 14 16 18 20 22 24 Figure 5: Run Chart for Patients Leaving the ED Prior to Treatment Blanche took a walk. Her first destination was the maternity department. Before she could say a word, the head nurse, Robin Gallagher, who was never happy to see Blanche, began to provide a long list of reasons why her C-Section numbers were bad. They included physicians scheduling the procedures in order to take vacations, improper pre-natal care, or flu outbreaks potentially affecting pregnancies, among others. However, this visit was different. After Blanche showed her the run chart for C-Sections, the Robin immediately stated, "Sure, that's when Dr. Forster left. Some of us thought that he was using the C-Section procedure even in cases where the other doctors would not." Blanche was thrilled because, finally, she received a seemingly valid reason from the usually uncooperative Robin. Blanche also visited the emergency department. After viewing the run chart of patients who left the ED prior to treatment, the ED staff manager explained that the chart confirmed his impression that service in the ED had gradually declined due to both an increase in patient demand as well as a decrease in the ED budget. He had previously attempted to have his budget increased but was told that the performance benchmark analysis never indicated a problem. He asked Blanche for a copy of the run chart for his use in justifying future requests. At this point, Blanche realized how much time she had likely wasted over the last three years tracking down problems that did not exist, and the lost opportunity to highlight real problems that the current quality system was not able to identify. Back in her office, Blanche reviewed the materials presented in the Six Sigma training program. Although run charts can be effective at determining if a process has changed, statistical control charts provide a more statistically sophisticated method. Although the details were not fresh in her mind, she did understand the use of control charts during the training course. She plans to review the coverage of control charts and the method presented for using statistical confidence intervals to determine if the outcomes of a process are consistent with external benchmarks. In the meantime, she had some internal challenges to overcome. Six Months Later… Blanche Davis was able to convince Hazel Wisely and the leadership of Laralex that implementing a processoriented approach to analyzing quality-related data was consistent with accreditation requirements and positioned the Hospital well with insurers, government regulators, and other key stakeholders. In fact, one large insurer recently expressed concern to Laralex President Ingrid Carney about the similarity in quality between accredited and non-accredited hospitals. This insurer may be instituting internal standards against which to evaluate hospitals based specifically on their ability to improve over time. Ingrid knows that, by creating an effective system now, these standards may in fact be based on the system implemented at Laralex. This result would be a huge competitive advantage for the Hospital. Blanche is given responsibility for organizing the new quality system, which will include the use of P Charts and other statistical process control methods. It would monitor performance over time and provide a statistically valid mechanism for comparison with peer hospitals. The new system would satisfy Joint Laralex Hospital Case Study Page 5 Commission requirements that performance metrics be evaluated both internally (against prior performance) and externally (against suitable benchmarks). Its forward-looking approach that reacts quickly to unstable processes also satisfies the requirement that performance data is used to initiate action to find root causes of problems. Blanche will also oversee the implementation of a process improvement system based on the aspects of Lean Six Sigma that make sense within the Hospital’s environment. Laralex is unique because of its mix of technically sophisticated medical staff and non-technical support staff. However, its technically oriented staff (physicians, physiatrists, registered nurses, and medical imaging technicians) do not possess a strong background in statistics. The hospital’s support functions (accounting, human relations, information technology, purchasing, etc.) includes intelligent employees but many of whom are not mathematically oriented. Blanche’s main worry is that the culture within the Hospital may be inconsistent with the culture needed to implement an effective process improvement program. At a recent management seminar, she heard the phrase culture eats strategy for breakfast. She would be prepared to speak with leadership about necessary modifications to their organizational infrastructure, but she does not know how to approach this aspect of the implementation. She is especially concerned about the nurses, who are represented by a regional nurses union, the Amalgamated Nurse and Midwife Union (ANMU), which recently had contentious relationship with the Hospital during the recent round of contract negotiations. In the past, the ANMU did cooperate with hospital leadership regarding working rules, but only if it was confident that the changes also benefitted the nursing staff. Blanche remembers a recent study performed by her staff showing that the rate of prescription errors was 2.8 per 100 prescriptions. Although most of these errors were minor (e.g., the date was not entered), this error rate was a surprise to the Hospital’s leadership. As a result, the pharmacy manager was fired. Blanche knows that, with a healthy process improvement program, all workers will need to report mistakes and close calls, even if they personally made or almost made the mistake. For example, she wonders if nurses will report these situations because they are often the last step in a complicated medication administration process, and the last step is often blamed for problems. She is also concerned that outside stakeholders may not understand that reporting problems in no way implies that the Hospital’s operations are problematic. In fact, the U.S. Federal Aviation Administration operates a voluntary system for tabulating and reporting mistakes and close calls based on information provided by pilots, air traffic controllers, flight attendants, and other airline workers and contractors. Those reporting incidents can do so anonymously and the reporter is immune from punishment. The success of this system is evidenced by the impressive safety record of major U.S. airlines. Other industries, especially healthcare, have taken strives to create similar systems. But, other leaders within the healthcare community believe that the culture of litigation in the U.S. will prevent hospitals from willingly exposing its problems. Laralex Hospital Case Study Page 6 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 
Purchase answer to see full attachment