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- 1. Health Care Workflow Process Improvement Maintaining and Enhancing Improvements Lecture a This material (Comp 10 Unit 11) was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000024. This material was updated by Normandale Community College, funded under Award Number 90WT0003. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
- 2. Maintaining and Enhancing Improvements Learning Objectives ⢠Design control strategies to maintain performance of clinic processes ⢠Develop and present a sustainability and continuous improvement plan for a health care setting 2
- 3. Measurement Is the First Step âMeasurement is the first step that leads to control and eventually to improvement. If you canât measure something, you canât understand it. If you canât understand it, you canât control it. If you canât control it, you canât improve it.â - Dr. H. James Harrington Source: DeMarco, 1982 3
- 4. Quality Council ⢠Establish core quality standards and requirements ⢠Identify and defining quality metrics ⢠Identify and define quality requirements ⢠Clarify which performance measures are key to gauging actual quality improvement performance ⢠Collect and analyze data to understand key variables and process drivers ⢠Legitimize value of QI within the organization ⢠Analyze QI data and report quality trends ⢠Educate organization and train key staff 4
- 5. Maintaining Improvements Measurement ď¨ Understanding ď¨ Control ď¨ Improvement 5
- 6. Process Control Terminology ⢠Process control (PC) the method for keeping a process within boundaries; the act of minimizing the variation of a process ⢠In-control process: observed variability is due to natural random variation ⢠Out-of-control process: observed variability is due to special causes, i.e., those other than natural variation ⢠Statistical process control (SPC) is the application of statistical methods to control a process 6
- 7. Challenges to SPC in Health Care ⢠SPC was first used in manufacturing ⢠SPC is not frequently included in books on statistics for health care and medicine ⢠SPC is a tool, like any tool, it can be used incorrectly or for the wrong job ⢠Prior to EHRs data had to be manually collected 7
- 8. Statistical Process Control ⢠Uses special charts, called control charts ⢠Statistical Process Control activities â Understanding the process â Understanding the causes of variation â Elimination of the sources of special cause variation ⢠Monitored using control charts to identify variation due to special causes ⢠Causes for excessive variation must be determined Source: Shewhart, 1931 8
- 9. Control Charts and Process Control 11.1 Chart: Control Chart (Wikipedia, 2007) 9
- 10. Levels of Evidence Supporting Process Changes ⢠Statistical decision rules for making process changes based on Systematic process performance data collection and analysis ⢠Systematic decision rules for making process changes based on Systematic process performance data collection and analysis ⢠Systematic process performance data collection and analysis ⢠Provider or staff consensus only ⢠Individual perception or opinion 10
- 11. Continuous Quality Improvement (CQI) ⢠CQI: âA philosophy and attitude for analyzing capabilities and processes and improving them repeatedly to achieve customer satisfaction.â â Incremental â Emphasizes understanding the underlying work processes and systems ⢠Kaizen: âgradual unending improvement by doing little things better and setting and achieving increasingly higher standards.â 11
- 12. Plan-Do-Check-Act Cycle ⢠Plan: develop a way to effect improvement ⢠Do: carry out the plan in a small scale pilot ⢠Check: compare what was predicted by the plan to what was observed in the pilot ⢠Act: action is taken on the causal system to effect the desired change. 12
- 13. Maintaining and Improving Processes ⢠Process monitoring until special cause identified â Special cause investigated â Improvement devised â PDCA cycle initiated & repeated until process improved ⢠Process monitoring until next special cause arises 13
- 14. Tips for Promoting a Culture of Quality Improvement ⢠Educate providers and staff about QI ⢠Set a routine schedule for reviewing data ⢠Communicate results from improvement projects ⢠Display data where patients can see them ⢠Celebrate successes ⢠Articulate the values of QI in meetings ⢠Make QI part of everyoneâs job ⢠Acknowledge staff and provider QI contributions 14
- 15. EHR and Quality Improvement ⢠Data systems that automatically capture and track key clinical information, specifically the measures used for improvement, will make the QI process more efficient and less costly 15
- 16. Maintaining and Enhancing Improvements Summary â Lecture a ⢠Monitoring processes to maintain performance gains ⢠Continuing to improve process performance 16
- 17. Maintaining and Enhancing Improvements References â Lecture a References Continuous quality improvement. 2012. In American Society for Quality Glossary. Retrieved from http://asq.org/glossary/c.html. Harrington, J. H. (1982). You canât control what you can't measure. In T. DeMarco, Controlling software projects: management, measurement and estimation (p. 3). New York: Yourdon Press. In-control process. 2011. In American Society for Quality Glossary. Retrieved from http://asq.org/glossary/i.html Institute of Medicine; Committee on Quality of Health Care in America. (2001). Crossing the Quality Chasm: A New Health System for the 21st Century 2001 . Washington: NATIONAL ACADEMY PRESS. Institute on Medicine, Committee on Quality of Health Care in America. (2000). To Err is Human: Building a Safer Health System. (L. T. Kohn, J. M. Corrigan, & M. S. Donaldson, Eds.) Washington, DC: NATIONAL ACADEMY PRESS. Kaizen. 2012. In American Society for Quality Glossary. Retrieved from http://asq.org/glossary/k.html. Out-of-control process. 2011. In American Society for Quality Glossary. Retrieved from http://asq.org/glossary/o.html. Process control. 2011. In American Society for Quality Glossary. Retrieved from http://asq.org/glossary/p.html. Shewhart, W. A. (1931). Economic Control of Quality of Manufactured Product. ASQ Quality Press. 17
- 18. Maintaining and Enhancing Improvements References â Lecture a, continued References Shortell, S. M., Bennett, C. L. and Byck, G. R. (1998), Assessing the Impact of Continuous Quality Improvement on Clinical Practice: What It Will Take to Accelerate Progress. Milbank Quarterly, 76: 593â624. doi: 10.1111/1468-0009.00107 Statistical process control. 2011. In American Society for Quality Glossary. Retrieved December 31, 2011, from http://asq.org/glossary/s.html. Thomson, W. (1883). Electrical Units of Measurement. Popular Lectures , 73. Charts, Tables, Figures 11.1 Chart: Penfield, Daniel. 2007. Control Chart. [Public domain] Retrieved 2011 from http://en.wikipedia.org/wiki/File:ControlChart.svg 18
- 19. Maintaining and Enhancing Improvements Lecture a This material was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000024. This material was updated by Normandale Community College, funded under Award Number 90WT0003. 19
Hinweis der Redaktion
- Welcome to Health Care Workflow Process Improvement, Maintaining and Enhancing Improvements. This is lecture a.
- Objectives for this lecture are to: Design control strategies to maintain performance of clinic processes, and Develop and present a sustainability and continuous improvement plan for a health care setting.
- The primary concept applied in quality improvement is the simple act of deciding what to measure, measuring it, deciding what to do to improve it, implementing the improvement, and finally evaluating the improvement. This last step could also be called âmeasuring againâ. Measurement is really the critical part of quality improvement; measurements tell you where you are and how far you have to go, like the number of miles to your final destination on a road trip. Doctor James Harrington, a long-time quality improvement expert summed it up best when he paraphrased a well known quote, stating that, âMeasurement is the first step that leads to control and eventually to improvement. If you canât measure something, you canât understand it. If you canât understand it, you canât control it. If you canât control it, you canât improve it.â As an aside, as far as the developers of this module have been able to tell, there isnât consensus regarding the actual source of the quote âYou canât manage what you canât measure.â Quite possibly, it is an adaptation of a statement in an 1883 work by Lord Kelvin, William Thomson, âwhen you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kindâ (Thomson, 1883). All others may stem from this earliest recording of the sentiment, or may indeed have arisen independently.
- Even before you can measure, you have to decide what to measure, and maybe even how to define it. A best practice in quality improvement is to form a quality council. The quality council is usually a group of individuals who already have job responsibility for quality improvement, or in health care, often for registries or performance measurement reporting or facility accreditation. The quality council is charged with tasks such as: Establishing or recommending (depending on the level of authority invested in the quality council) core quality standards and requirements, Identifying and defining quality metrics, Clarifying which performance measures are key to gauging actual quality improvement performance, Collecting and analyzing data to understand key variables and process drivers, Legitimizing value of QI to ensure best use of resources and measure improvement associated with these activities, Analyzing QI data and reporting quality metrics and trends, and Educating organization and training key staff. For a small practice, the quality council may consist on the practice leader and the individual responsible for performance measure reporting. Creating a quality council formalizes the responsibility and accountability for the decision-making regarding quality, often including process, improvement and performance measurement. A key function of the quality council is deciding what to measure, i.e., what data will best inform decision-making. For example, if a practice is trying to increase access to care, they may decide to measure the percent of same day visit requests that they are able to accommodate. The quality council may be the group that recommends this measure or makes the decision that the percent of same day visit requests that they are able to accommodate will be used as the or a measure of access to care.
- The next several slides will address maintaining the performance achieved through process redesign. The process that Dr. Harrington lays out in his adaptation, âIf you canât measure something, you canât understand it. If you canât understand it, you canât control it. If you canât control it, you canât improve it.â starts with measurement, and from that, the measurer gains understanding, i.e., real data on what is actually happening. By understanding, we mean, is the performance consistent or are there spurious unexplained variations? For example, if we are trying to improve clinic wait times, we would look at actual measured wait times to see if they varied widely from patient to patient, i.e., 5 minutes for some and 2 hours for others. We would also want to see if the average patient wait time is acceptable, or if improvements are needed. By control, we mean consistent performance of the process, in other words, maintaining the performance we achieved through process redesign. For example, if we redesigned a clinic process and reduced the average wait time from 45 minutes to 20 minutes, process control would mean that the average wait time remained close to 20 minutes, and varied within what we would expect from natural random variation. We would use QI tools to show whether or not this was indeed the case. Then we would continue to use the tools over time so that we could tell if the average wait time or the variability drifted too far, and we would investigate and possibly intervene if it did to bring the process back into control. Monitoring process performance over time can be done with by simply calculating measures of central tendency, i.e., mean (average), median, mode, and measures of dispersion, e.g., range, variance, standard deviation, and by monitoring them over time. However, without the tools of statistics, it is difficult to know whether or not the differences from measurement to measurement are due to natural random variation, or due to the process drifting or due to some other cause. Statistical process control charts can be used to help make this decision, or the aid of a statistician can be sought for an equivalent analysis and in cases where a custom analysis is required. The next few slides will cover statistical process control charts and how to use them to control clinical processes.
- First, weâll go over some terms and concepts important to statistical process control. The term process control has a specific meaning in statistics and quality engineering. The American Society for Quality, ASQ, defines process control as, âthe method for keeping a process within boundaries; the act of minimizing the variation of a processâ. ASQ defines an in-control process: âA process in which the statistical measure being evaluated is in a state of statistical control; in other words, the variations among the observed sampling results can be attributed to a constant system of chance causesâ. The American Society for Quality defines an out-of-control process as âa process in which the statistical measure being evaluated is not in a state of statistical control. In other words, the variations among the observed sampling results can be attributed to a constant system of chance causes.â Often, the causes of variability in an out-of-control process are referred to as âspecial causesâ to denote that they are due to causes OTHER than natural variation. The American Society for Quality defines Statistical process control (SPC) as, âThe application of statistical techniques to control a process.â The term SPC is often used interchangeably with the term âstatistical quality controlâ (SQC).
- SPC is increasingly being applied in health care. SPC was developed and first used in manufacturing. In health care we have medical statistics and unfortunately, SPC is not frequently included in books on statistics in health care and medicine, thus, statisticians working in health care are less likely to have received training in SPC techniques than those working in industrial sectors. The SPC is a way of thinking, i.e., managing based on numbers, can be seen as management by objectives, increasing inspection costs, or risking local optimization (at the expense of global optimization), i.e., as contrary to best quality or management practices. These, however, are characteristics of how SPC tools are used by an organization. Using SPC in a way that increases overall production cost or using measures that incentivize locally optimal and globally detrimental behavior would be a poor use of the tools. Lastly, successful application of SPC requires that appropriate data be available. Until the adoption of electronic health records, data required for quality improvement often had to be manually collected in addition to regular care activities, thus, prior to EHRs, using tools such as SPC required too much additional effort for health care facilities.
- Statistical Process Control is accomplished using special data displays, i.e., graphs, called control charts, originally developed by Walter A. Shewhart while working for Bell Labs in the 1920s. Dr. Walter Shewhart was later named the Father of Statistical Quality Control by the American Society for Quality. Statistical process control may be broadly broken down into three sets of activities: understanding the process; understanding the causes of variation; and elimination of the sources of special cause variation. Using SPC, a process is monitored using control charts to identify detrimental variation, often called variation due to special causes, and to free the user from concern over naturally occurring variation, often called variation due to common causes. This is a continuous, ongoing activity where only special causes are addressed. When special causes are identified by the control chart detection rules, additional effort is exerted to determine causes of that variance.
- A control chart has several important visual features. The first are three usually horizontal and parallel lines. The first of the three is known as the center line (CL) and is used to mark the average of the plotted points. The second and third lines are called the Upper and Lower Control Limits (UCL) and (LCL); these are equidistant above and below the center line. The measure of interest is plotted over time, or for multiple samples, with each time point or sample being shown on the x, or horizontal axis. The vertical, or y, axis represents the measure of interest. For example, if the measure, or quality characteristic of interest was clinic wait time, and our clinic measured the average clinic wait time every day for 15 days and plotted it on a control chart, it might look like the chart on the slide, where each daily average corresponds to a plotted point. There are entire books and courses on how to create and use control charts. Comprehensive training on the creation and use of control charts in health care quality improvement would be equal or greater in length to this course, and thus can not be provided within this course. Our goal in this course is to provide Practice Workflow and Information Management Redesign Specialists with enough information to recognize the salient features of control charts and understand how they are used to maintain process performance and to further improve processes. In more extensive introductory courses in statistical process control, practitioners first learn how to choose the chart that is appropriate for the type of data being measured and graphed. Second, practitioners learn how to use the sets of formulas corresponding to each type of chart to calculate the center line and upper and lower control limits. Next, practitioners learn how to use rules to interpret the charts. An example chart interpretation rule is, âa point that appears above the upper control limit or below the lower control limit is a special causeâ, i.e., it is due to something other than natural random variation, and should be investigated. And lastly, practitioners learn how to use common quality improvement methods and tools such as those introduced in Component 10, Unit 8 in addition to other methods to investigate special causes. Control charts are special tools, and different from other graphs like scatter plots, bar and pie charts, in that the formulas used to create the center line, upper and lower control limits are statistically developed. In this way, they guide practitionerâs decision-making by visually and statistically distinguishing special causes which require action, from common causes, which should not be acted upon. Thus, practitioners are prompted to act on special causes and to leave the process alone in the absence of special causes. Acting on special causes and leaving a process alone in their absence is a major concept of quality improvement best illustrated by W. Edwards Deming. Dr. Deming devised an experiment to demonstrate that tampering with a process subject to only natural random variation degrades process performance by introducing more variation into the process than would occur from natural random causes if the process were just left alone. Dr. Deming demonstrates these principles by simulating adjustments in a random cause process. In the simulation, beads are dropped through a funnel, first without adjusting the funnelâs position over the target, and later using three different methods of funnel position adjustments. The experiment demonstrates that the beads fall in a pattern closer to the target without funnel position adjustments than in any of the scenarios where the funnel position is adjusted. Points on a control chart that according to control chart interpretation rules that are NOT special causes mean that the observed variation is due only to natural, i.e., common causes and no adjustments should be made to the process. The chart on the slide is an example of such a process, would be called an in-control process, and should not be adjusted.
- Similar to evidence-based medicine, there are different levels of evidence upon which process changes can be based. Here, we describe five different levels of evidence upon which practices base process changes. Statistical process control, or application of equivalent statistical-based decision-making is the most robust way to maintain a process in that it controls both for making adjustments when they need to be made (through interpretation rules that guide practitioners in identifying special causes) as well as controls against making adjustments when they shouldnât be made. Thus, the time devoted to it in this lecture. Many practitioners use lesser levels of evidence, for example, data and graphs to maintain processes, but without the aid of statistics to support decisions about when and when not to make process changes. Consider the scenario where a practice may be measuring patient satisfaction using a patient satisfaction survey and may decide to make changes based on the monthly survey results for patients seen that month until patient satisfaction remains over 80%. Using this method, any month in which the results were below 80% would result in process changes. These changes would be made in the absence of knowledge of what the natural random variation was, and what consistency the process was actually capable of providing, thus, some changes would be made in response to random variation, rather than real process problems. This however, is better than not measuring satisfaction at all, and certainly better than making process changes in the absence of any real information about how the process is actually performing. These same concepts about when to make process changes, and levels of evidence that can support process changes apply to not just maintaining processes as we have been discussing, but equally as well to improving processes. Next, we will cover Continuous Quality Improvement.
- American Society for Quality (ASQ) defines continuous quality improvement (CQI) as âA philosophy and attitude for analyzing capabilities and processes and improving them repeatedly to achieve customer satisfaction.â CQI is an incremental approach to improving a process that emphasizes understanding the underlying process, i.e., improving outcomes or results by improving the process itself. CQI is probably best practiced by the Japanese who embody CQI into the work culture and into everyoneâs job, i.e., improving processes is not the work of a special projects team, it is part of everyoneâs job. In Japan, they have a special word for it, Kaizen. The ASQ defines Kaizen as âgradual unending improvement by doing little things better and setting and achieving increasingly higher standardsâ. The term became widely used in the US as a result of Masaaki Imaiâs book, Kaizen: The Key to Japanâs Competitive Success. According to Shortell, et. al, âCQI had come to be widely used in other sectors of the American economy and throughout the world (Deming 1986, Juran 1988) before it was introduced into health care by Berwick (1989) and Laffel and Blumenthal (1989), who wrote seminal articles on the topic,...â. Throughout the last two decades, there has been a large amount of CQI work in health care; most health care facilities undertake quality improvement efforts of some kind. Most commonly, facilities report performance measures or participate in registries and make process changes based on the analyzed data. The two landmark Institute of Medicine reports mentioned elsewhere in this component, and no doubt elsewhere in the Health IT curriculum, To Err is Human, and Crossing the Quality Chasm published in 1999 and 2001 respectively documented major quality gaps that existed in health care that still exist today. Thus CQI will remain a part of health care culture.
- Deming and others have suggested that just âdoing our bestâ is not good enough, and in fact, that âdoing our bestâ is responsible for inferior quality of many American products. (for more information, watch the YouTube videos Deming Parts 1-3 referenced in the Instructor manual) As the antidote to poor quality, Deming taught the Plan-Do-Check-Act (PDCA) cycle, a four-step process for continuous quality improvement. The American Society for Quality (ASQ) describes the PDCA cycle in the following way: Step1 (plan), a way to effect improvement is devised, and the improvement is predicted. Step 2 (do), the plan is carried out, on a small scale, i.e., tested or piloted. Step 3 (check), a comparison is made between what was predicted by the plan and what was observed in the pilot. Step 4 (act), if the comparison was favorable, the change is made for real, i.e., action is taken to effect the desired change. This cycle is carried out repeatedly for multiple improvements. At this point, you have the major pieces for continuous quality improvement, we just need to put them together. The Plan-Do-Check-Act cycle is sometimes referred to as the Shewhart cycle, because Walter A. Shewhart discussed the concept in his book Statistical Method From the Viewpoint of Quality Control, and as the Deming cycle, because W. Edwards Deming introduced the concept in Japan. The Japanese subsequently called it the Deming cycle. Also called the plan-do-study-act (PDSA) cycle.
- In practice, putting all of the pieces together, weâll talk through what a best practice organization might look like. A best practice clinic would have processes and measures most important to patient safety, clinical outcomes, and patient satisfaction identified. The processes would be monitored by obtaining data from practice data systems on a regular basis. This data would be collected as a by-product of care rather than as an additional data collection effort, with the exception perhaps of a patient satisfaction survey that is collected simultaneously with patient follow-up. The data for the measures is analyzed and plotted on control charts and reviewed monthly by the quality council and practice leadership. The charts are also posted for clinic staff and providers. Special causes, when they occur, are discussed at staff meetings. Relevant staff and the quality council determine if any additional data are needed to determine the process problem and work together to devise a process change to effect the improvement, setting the PDCA cycle in motion. The plan is piloted and checked. If favorable it is implemented, if not, a new plan is devised. The process change is monitored using the same measures unless adjusted measures were necessitated by the change. Monitoring continues until the next special cause arises.
- It is important not to underestimate the people factors, such as culture, in selecting a quality improvement approach. Any improvement (change) takes time to implement, gain acceptance and stabilize as accepted practice. Improvement must allow pauses between implementing new changes so that the change is stabilized and assessed as a real improvement, before the next improvement is made. Some tips for promoting a culture of quality improvement are: Educate staff about QI and provide them with the skills to participate in QI. Set a routine schedule for monitoring and reviewing data. Communicate results from improvement projects throughout the clinic and the Community. Display data where patients can see them. Celebrate successes. Articulate the values of QI in meetings. QI should be part of everyoneâs job, and finally Acknowledge staff and provider QI contributions.
- More seamless data systems that automatically capture and track key clinical information would make the QI process more efficient and potentially less costly. The challenge is that these systems typically require significant initial financial and social investment. Thus, while implementing a successful EHR is seen as quality improvement in the health care setting; the EHR itself can promote and support additional quality improvement in the clinic.
- This concludes Lecture a of Maintaining and Enhancing Improvements. In this lecture, we learned about maintaining the performance gains achieved with the redesigned process, i.e., Process Control, and continuing to improve the redesigned process and other practice processes, i.e., Continuous Quality Improvement (CQI).
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