1 Introduction
1.1 Key Concepts
Quality improvement (QI) frameworks, Define–Measure–Analyze–Improve–Control (DMAIC), Plan–Do–Check–Act (PDCA), variation, data visualization, statistical tools.
1.2 Quality Improvement in Healthcare
Quality improvement (QI) is an integral component of the healthcare delivery landscape, necessitated by cost escalation and the drive to achieve better individual and population health outcomes. Government and nongovernment organizations at all levels provide resources, strategies, and mandates to achieve global, national, and local health goals. The United States has adopted a national strategy for healthcare quality improvement with three aims: better care, healthy people/healthy communities, and affordable care (Agency for Healthcare Research and Quality 2017). The World Health Organization articulates quality dimensions of effectiveness, efficiency, accessibility, patient‐centered, equity, and safety that are applicable to all countries for improving health systems (World Health Organization 2006).
Quality improvement can be defined as the use of a continuous and systematic approach to achieve measurable improvement in healthcare delivery and individual and population health outcomes. Adopting a culture of quality improvement in a healthcare organization can lead to increased efficiency and productivity, better patient and employee satisfaction, the ability to retain and attract high‐performing employees, better clinical outcomes, and reduced errors, risks, and costs. Quality improvement should be practiced continuously at all levels and functions of an organization. Additional information on quality improvement can be found at the websites of the American Society for Quality and the Institute for Healthcare Improvement.
There are a number of related activities found in healthcare delivery (and beyond) that differ from quality improvement. Quality assurance is a periodic, systematic review of a process to identify and correct errors and ascertain whether standards are being met. Quality improvement and quality assurance both focus on existing systems and processes, with quality improvement programs being driven from within the organization and quality assurance being driven by external organizations (e.g. government and accrediting agencies). Research activity can be found in healthcare organizations, but the emphasis is on attaining knowledge that supports the development of new interventions, products, systems, and processes. Quality improvement, quality assurance, and research share many methodologies and tools; the statistical tools presented in this book focus on quality improvement applications, but can also be used in research and quality assurance.
1.3 Understanding Variability: The Key to QI
Quality improvement can be realized by measurable reductions in cost, errors, or risk, improved health indicators for individuals and populations, and increased patient satisfaction. Healthcare systems and processes are subject to variation due to factors such as the inherent differences in patients, operational practices and procedures, clinician skill and training, and facilities and equipment. Improvements can be made by reducing variation. For example, hospitalized patient satisfaction can be raised and food waste reduced when meals are delivered as scheduled. Achieving improvement requires identifying and understanding the many sources of variation that can affect process performance. For example, timely hospital patient discharge can be affected by variations in staffing levels, pharmacy fulfillment times, demand for beds, etc.
A key step in quality improvement is to create a map or diagram that shows the sequence of the main process steps. Figure 1.1 shows a high‐level process map for the preoperative total joint replacement (TJR) process, which will be the subject of three chapters of this book that illustrate the lifecycle of a process improvement initiative.
Figure 1.1 Process map for the preoperative total joint replacement.
Such process maps are useful for bounding the scope of the project. Process maps with more detail are good for identifying sources of variability and whether or not these sources of variation are controllable by the organization. Understanding which variability sources are controllable and which are not helps in defining potential improvement actions that an organization can undertake. For example, in Figure 1.1, process step 3 (when the patient meets with the primary care provider), is mostly outside of the control of the hospital, whereas process step 2 (book preoperative appointment) can be changed and is more likely to yield process improvements because patients' preoperative appointment scheduling takes place at the orthopedic clinic.
Sources of variation are also classified as common‐cause or special‐cause. Common cause variation is inherent in the process and reducing this type of variation, requires a change in the process itself. For example, the variation in the time between process steps 2 (preoperative appointment) and 3 (the preoperative clearance) is between two and four days for knee or hip replacements and seems to be reasonable variation for this part of the process. As such, this would be considered common cause variation. However, the variation in the time between the preoperative appointment and the preoperative clearance can be as high as 40 business days. This unusual variation is attributed to special cause, which arises from unusual circumstances. The variability for the Conformis‐brand prosthetic knee replacement process is explained by additional preoperative steps, which not only require extra studies such an magnetic resonance imaging (MRI) but also the fabrication of a prosthetic by a vendor. If there is a problem with the prosthetic, the process takes longer than expected. This special circumstance leads to longer elapsed times than usual, and hence greater process variability that is outside the control of the clinic, adversely affecting the process performance. Identifying variation as either common‐cause or special‐cause can assist in developing and prioritizing potential improvement actions. In this casebook, we present tools for assessing variability both graphically and numerically. Data visualization and data slicing (or subgrouping) are powerful methods for identifying and quantifying process variation. Additional discussion on variability in the context of quality improvement can be found in Deming (1986) and Hoerl and Snee (2012).
1.4 Quality Improvement Frameworks
Establishing a quality culture requires an ongoing focus on quality throughout an organization, along with a framework and a set of tools for identifying, making, and maintaining improvements. These frameworks serve as a common approach within an organization, enabling project teams to undertake improvement initiatives in a systematic way using a well‐defined series of steps. A key component of quality improvement is data collection and analysis to assess process performance. Thus, statistical tools play a key role. Nonstatistical tools, such as brainstorming and process mapping, also have an important role in quality improvement initiatives, particularly in the early stages. There are a number of different frameworks that can be adopted; we will provide a brief overview of two of the most commonly used frameworks.
1.4.1 Define–Measure–Analyze–Improve–Control (DMAIC)
The DMAIC framework is a systematic approach to quality improvement applied in Six Sigma programs. The American Society for Quality (ASQ 2019), defines Six Sigma as “a method that provides organizations tools to improve the capability of their business processes.” The DMAIC cycle begins with the Define phase where a team is assembled to develop a project charter that describes the process to be improved and the objectives of the initiative. During this phase, requirements and improvement opportunities are elicited from stakeholders. A clear problem statement is a
