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GET WRITING HELP!Assignment: Interpreting Control Charts Activity ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Assignment: Interpreting Control Charts Activity In a Word document, answer the following questions Assignment: Interpreting Control Charts ActivityWhat are the two major categories of control charts? What are the most common charts in the each of the two major categories (Name and briefly describe them)? Download and complete the Control Chart Exercises Control Chart Exercises Alternative Formats . Upload all of the documents to the dropbox below for your instructor to review. control_chart_exercises_1_ PART A: Control Chart Exercises Background: All processes have variation. The challenge is to determine whether or not the variation is common cause (or random or noise) or special cause (or nonrandom variation). This assignment deals with run charts and control charts. These are tools that you can use to plot data points and interpret whether a process shows a common cause or a special cause. Run charts display observed data in a time sequence. There are four rules that can be applied to a run chart to help determine whether or not a special cause can be indicated. Review Run Chart Rules for Interpretation: http://www.qihub.scot.nhs.uk/media/529936/run%20chart%20rules.pdf . Keep in mind that run charts include a median which represents the center line. According to iSixSigma, an online clearinghouse for process improvement, common cause variation is fluctuation caused by unknown factors resulting in a steady, but random, distribution of output around the average of the data. Special cause variation is the inverse: variation caused by factors that result in a nonrandom distribution of output. It is also referred to as exceptional or assignable variation.6 Determining the cause of special cause variation typically requires further analysis/investigation. Control charts show what type of variation is occurring in a process. Synonymous with statistical process control, control charts are a graphical view of a process. Special tests are conducted against the data to determine (a) the normal limits/ variation of the process and (b) whether or not these limits have been violated. Control charts can also be considered a run chart on steroids.. Control charts take the simple run chart and apply some statistical rigor to them. Basically, the mean (average) is calculated and drawn on a graph. The individual data points then are plotted on the same graph. Then, the control limits are also drawn as +/- three standard deviations from the mean. Review the Nelson Rule Set for Determining Special Causes on page 181 in Lighter (2015). In short, the types of tests that can be run to determine whether a process is out of control varies by data type. Continuous data has more tests, but both attribute and continuous data have the same core four tests: 1 point > 3 standard deviations from center line 9 points in a row on same side of center line 6 points in a row, all decreasing or increasing 14 points in a row, alternating up and down When a test is violated, it is up to the user to determine the special cause. Test violations are not necessarily negative in nature as they may indicate either a favorable or adverse shift in a process. Or they just might indicate an abnormal event that the process failed to handle. Problem #1 Reducing Delays from Abnormal Mammogram to Biopsy The medical director of the Worldbest Health Plan was concerned with what he felt were inordinate days in the time between reading an abnormal mammogram and obtaining a definitive biopsy. He collected the average delay time per week for a period of 20 weeks, presented them to a continuous quality improvement (CQI) team, and instructed them to remedy the problem. He felt an average waiting time of 31 days was unacceptable. He also felt that the process was continuing to deteriorate. He pointed to the fact that the delay time reached 40 days during week 14, In addition, during the last six weeks he felt there was a negative trend from 26 days to 35 days of wait time. Using Excel, calculate the median delay time per week and construct a run chart using the data below. Compare the delay times per week to the median. Display your chart. Question: Does this process show common or specia l cause variation? Is this something that the medical director should be concerned with? Would it be productive to investigate and eliminate the cause (whether it is common or special)? Why or why not? Week Average Delay Time (in days) 1 34 2 30 3 35 4 32 5 28 6 26 7 29 8 28 9 35 10 26 11 34 12 31 13 28 14 40 15 26 16 32 17 31 18 30 19 33 20 35 Problem #2 Next, using the same data from problem #1, calculate the upper and lower control limits and then construct a control chart. Display your chart. Question: Using the control chart rules for detecting special causes, can you detect a special cause? Explain why or why not. Week Average Delay Time (in days) 1 34 2 30 3 35 4 32 5 28 6 26 7 29 8 28 9 35 10 26 11 34 12 31 13 28 14 40 15 26 16 32 17 31 18 30 19 33 20 35 Problem #3 Suppose the same medical director from the first two problems wanted to look at 36 weeks instead of 20 weeks. His quality improvement team provided him with the data below after an intervention was introduced in week 20. Calculate the upper and lower control limits and then construct a control chart. Display your chart. Question: Using the rules for detecting special causes, can you detect any special causes in this case? If so, what special causes can you detect? What does this mean? Was the intervention successful in reducing the delays? Week Average Delay Time (in days) 1 34 2 30 3 35 4 32 5 28 6 26 7 29 8 28 9 35 10 26 11 34 12 31 13 28 14 40 15 26 16 32 17 31 18 30 19 33 20 35 21 26 22 19 23 21 24 23 25 25 26 17 27 21 28 22 29 21 30 17 31 23 32 22 33 24 34 19 35 20 36 21 Problem #4 Reducing C-Section Rates The largest purchaser of care of the Worldbest Healthcare System complained that the average C-section rate at Worldbest had increased from 2015 to 2016 and was now over 21 percent. The average at other area hospitals was between 17 percent and 19 percent. The purchaser told Worldbest that it expected them to lower the C-section rate to below 19 percent if they wished to remain on their provider list. Worldbest responded by making a P-chart on the combined C-section rates for all eight hospitals in the system. Calculate the percent of deliveries that were c-sections (to one decimal place). Review the types of control charts on page 182-197 in (Lighter, 2011). Which type of control chat would you use in this situation? Why? Construct a control chart. Display your chart. The percent that you calculate should go on the y-axis, the months should go on the x-axis. Question: Did your control chart show any special causes? Would you recommend that an intervention is needed in order to bring the average to less than 19 percent? Month Year Deliveries All C-Sections Percent Jan 2015 1483 286 Feb 1436 285 March 1601 316 Apr 1595 305 May 1725 336 Jun 1614 311 Jul 1715 343 Aug 1707 353 Sep 1623 298 Oct 1590 330 Nov 1409 294 Dec 1491 298 Jan 2016 1482 308 Feb 1407 303 Mar 1626 339 Apr 1569 324 May 1649 333 Jun 1552 326 July 1638 343 Aug 1487 291 Sept 1563 298 Oct 1606 349 Nov 1442 299 Dec 1479 306 Assignment: Interpreting Control Charts Activity Problem #5 Introduction Farrell Memorial Hospital is a 400-bed general medical and surgical hospital. The Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) is a national patient satisfaction survey that asks patients about their experiences during a recent hospital stay. The responses are broken down into the following categories: Survey of patients experiences Timely and effective care Readmission, complications, and deaths Use of medical imaging Medicare payment Number of Medicare patients Within some categories are sub-categories. For example, in the Timely and effective care category, there were 10 sub-categories, including heart attack care, surgical care, and pregnancy and delivery care. The results are maintained by Centers for Medicare and Medicaid Services, CMS.gov, (Medicare) and anyone can compare one hospital vs. another on Medicares Hospital Compare website. Assume that you ran a report of patients experiences at Farrell Memorial and compared those numbers to the state and national averages. Farrell Memorial ranked below the state and national averages in many of the categories. Estimated Date of Discharge One of the cost reduction approaches employed by hospitals is to reduce the patients length of stay (LoS). One of the strategies embedded within this approach is to actively plan the patients discharge. In hospitals, the project teams are composed of the patients physician, nurses, and ancillary professionals (such as pharmacists, physical therapy, occupational therapy, and social workers). The composition of the team was dependent upon the specific patients condition. So, there could literally be as many project According to the Institute for Healthcare Improvement: With multidisciplinary rounds, disciplines come together, informed by their clinical expertise, to coordinate patient care, determine care priorities, establish daily goals, and plan for potential transfer or discharge. This patient centered model of care has proven to be a valuable tool in improving the quality, safety, and patient experience of care. One barometer to assess the effectiveness of multidisciplinary rounds was to measure Estimated Date of Discharge (EDD), which is one of the primary outcomes for each patient discussed during multidisciplinary rounds. Setting an EDD prompted active discussion on the barriers preventing a patients release. A natural byproduct of these discussions is to streamline the transition of care for patients (i.e., it helped reduce/minimize unnecessary clinical variation in treatment). It also fosters a team, rather than an individual, approach to patient care. Calculate the EDD Accuracy rate. Next calculate the mean of the EDD accuracy rate and use this mean to construct a control chart. Display your chart. Assume that Farrell Memorial implemented a process change or intervention in week 20. Assignment: Interpreting Control Charts Activity Farrell Memorial Week Estimated Discharged that were Actually Discharged (EDAD) Potential Discharges (PD) EDD Accuracy (EDAD/PD) 1 80 374 2 109 530 3 160 577 4 169 569 5 153 583 6 162 563 7 165 564 8 163 564 9 180 602 10 153 624 11 141 623 12 166 601 13 167 570 14 155 525 15 185 602 16 179 613 17 173 613 18 160 515 19 198 571 20 202 621 21 191 583 22 195 569 23 222 619 24 204 623 25 210 651 26 210 567 27 182 550 28 192 565 29 207 578 30 222 532 31 235 614 32 272 645 33 267 618 34 248 571 35 290 657 36 248 577 37 286 646 38 262 578 39 271 600 Question: Is the process in control or out of control? In this particular case is this good or bad? Does the implemented process change appear to be effective or not? What recommendations (if any) would you provide to the hospital based on the control chart analyses? After comparing control charts from Penner Memorial Hospital in the SCORM and Farrell Memorial Hospital in this problem set, what take home messages do you think are important for anyone studying control charts to remember? Part B: Interpreting Control Charts Background: Control charts are a valuable tool for monitoring process performance. However, you have to be able to interpret the control chart for it to be of any value to you. Read through this AHRQ website which explains how healthcare organizations can measure fall rates and fall prevention practices: https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/fallpxtk5.html Question #1: Read Case Study #4 on pages 268-271in Lloyd (2019): Tracking Patient Falls. Would you say that the new program that was implemented have the desired impact? In your own words explain why or why not. Assignment: Interpreting Control Charts Activity The National Database of Nursing Quality Indicators (NDNQI) collects data about nursing quality. One problem NDNQI is addressing through statistical process control is patient falls. As seen in the figures below, measures such as falls per 1,000 patient days can be plotted using X-MR charts or u-charts. Your job is to interpret the control charts below. What can be learned from these particular control charts? Question #2 : The average is 5.5 falls per 1,000 days. With an upper control limit (UCL) of 9.1 and lower control limit (LCL) of 1.8. What is the chart below telling you? Do special causes occure. If so, in what months and year do they occur? In your opinion is there enough evidence to implement a process change? Why or why not? Do special causes occure. If so, in what months and year do they occure? Question #3 : The average range between each month is 1.4 falls per 1,000 days. What is this chart telling you? If so, in what months and year do they occur? In your opinion is there enough evidence to implement a process change? Why or why not References Carey, Raymond G.. Improving Healthcare with Control Charts: Basic and Advanced SPC Methods and Case Studies . ASQ Quality Press. Kindle Edition. http://www.qihub.scot.nhs.uk/media/529936/run%20chart%20rules.pdf Lloyd & Provost (2011): The Health Care Data Guide Learning from Data for Improvement, Chapter 3 and from: http://www.knowledge.scot.nhs.uk/media/CLT/ResourceUploads/1006891/Good_Practice_Guide_Data_Man agement_run_chart_rules.pdf http://asq.org/2015/12/quality-tools/control-charts-healthcare-setting.pdf Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10
