Only parts b and c

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-ring0F 1 1 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 caregivers 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 endotracheal 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 resultsfor 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 ■ Page 1 of 4 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 providing 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, Massachusettsbased 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 scheduled 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 Solutions 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 collect 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 The American Society for Quality ■ Page 2 of 4 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 Start Draw to Comp Comp LT Start Draw to Comp Comp Williams’ team returned to the process flow and learned that one tube—or even four tubes—of blood could be drawn in 71 ⁄2 minutes. Completing an assessment of the total number of draws per floor, and of the lab’s processing, analyzing, and delivery capacity, 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 minutes. “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 percentage 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 helping the medical center discharge patients in a timely manner is its use of hospitalists, medical doctors whose specialty is caring 160 140 120 100 80 60 40 20 0 160 140 120 100 80 60 40 20 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 hospitalists who care for patients who have been admitted by another physician. Although both the lab process and the use of hospitalists 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). The American Society for Quality ■ Page 3 of 4 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 measurements 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 and the Pocono Medical Center Laboratory at • Access more case studies, how-to articles, and other information about using Six Sigma in healthcare by visiting 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 pathologist 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, facilitators, 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. The American Society for Quality ■ Page 4 of 4

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