Cecilia Martinez - Improving Health Care Quality

A simple thanks is not adequate for our families and friends who supported us during this project. But please know that every word of encouragement or act of kindness helped us to keep going and made our work a pleasure.

Our appreciation for their guidance during this project goes to the editorial staff of Wiley, especially Mindy Okura‐Marszycki, Kathleen Santoloci, Blesy Regulas, Linda Christina, and Vishnu Priya. Thanks to everyone who provided feedback at any stage of this project. Your comments and suggestions greatly improved this book.

ASUAmbulatory Surgical UnitDMAICDefine–Measure–Analyze–Improve–ControlDRGDiagnosis Related GroupEBPEvidence‐Based PracticeEDEmergency DepartmentEMSEmergency Medical ServicesICDInternational Classification of DiseasesIRBInstitutional Review BoardMRMoving RangeOROperating RoomPCPPrimary Care ProviderPDCAPlan–Do–Check–ActPDSAPlan–Do–Study–ActQIQuality ImprovementSIPOCSuppliers–Inputs–Process–Output–CustomerSPARCSStatewide Planning and Research Cooperative SystemTJRTotal Joint ReplacementTRIZTheory of Inventive Problem SolvingVASVisual Analog ScaleVOCVoice of the consumerMeanaverageAlternative hypothesisResearch HypothesisSignificance levelAlpha, Probability of Type I ErrorBetaProbability of Type II ErrorPower1 – Probability of Type II Error

This book is accompanied by a companion website:

www.wiley.com/go/shifflet/improvinghealthcarequality1e

Scan this QR code to visit the companion website

The website includes:

Instructor's Site – Figures; Solution manual; Data sets with and without scripts for the Cases and the Exercises

Student's Site – Data sets for the Cases and for the Exercises; Chapter Summaries

Quality improvement (QI) frameworks, Define–Measure–Analyze–Improve–Control (DMAIC), Plan–Do–Check–Act (PDCA), variation, data visualization, statistical tools.

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.

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 1.1shows 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 1.1Process 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).