Preface
Why write a book on healthcare analytics that focuses on quality and performance improvement? Why not focus instead on how healthcare information technology (HIT) and “big data” are revolutionizing healthcare, how quality improvement (QI) methodologies such as Lean and Six Sigma are transforming poorly performing healthcare organizations (HCOs) into best-in-class facilities, or how leadership and vision are the necessary driving factors behind innovation and excellence within HCOs?
The truth is, this book is about all these things. Or, more accurately, this book is about how healthcare organizations need to capitalize on HIT, data from source systems, proven QI methodologies, and a spirit of innovation to achieve the transformation they require. All of these factors are necessary to achieve quality and performance improvement within modern healthcare organizations. However, the professionals working in healthcare IT, quality improvement, management, and on the front lines all speak different languages and see the world from different perspectives—technology, data, leadership, and QI. This gap (a chasm, really) prevents these professionals from effectively working together and limits their capability to perform effective quality and performance improvement activities. This may in fact be lowering the quality of care and decreasing patient safety at a time when doing the opposite is critical.
This book demonstrates how the clinical, business, quality improvement, and technology professionals within HCOs can and must collaborate. After all, these diverse professional groups within healthcare are working together to achieve the same goal: safe, effective, and efficient patient care. Successful quality improvement requires collaboration between these different stakeholders and professional groups; this book provides the common ground of shared knowledge and resources necessary for QI, IT, leadership, and clinical staff to become better coordinated, more integrated, and to work together more effectively to leverage analytics for healthcare transformation.
In this book, I hope to demonstrate that analytics, above all, can and must be made accessible throughout the entire HCO in order for the insight and information possible through analytics to actually get used where it is needed. I attempt to dispel the myth that only a select few can be qualified to be working with the data of an HCO. Although the process of generating insight through analytics requires some statistics and mathematics, the output or result of analytics must make intuitive sense to all members of the healthcare team. In my experience, if the information and insight produced by business intelligence and analytics is too complex to understand for all but the team that generated it, then that information will contribute very little to healthcare improvement.
In keeping with the theme of accessibility, I have attempted to keep this book very accessible to readers with various backgrounds and experience. The book covers a wide range of topics spanning the information value chain, from information creation and management through to analysis, sharing, and use. As such, it cannot cover each of the topics completely and in depth. But it does cover the areas that I believe are vital in a quality improvement environment driven by analytics. If you work in the area of health IT, data management, or QI, I have attempted to connect the dots in how your professional discipline fits in with the others. I hope that this book can thereby enable technical, analytical, QI, executive, and clinical members of the healthcare team to communicate clearly, better understand one another’s needs, and jointly collaborate to improve the efficiency, effectiveness, and quality of healthcare.
I do admit my bias toward the acute-care setting, and emergency departments in particular. The vast majority of my career has been within acute care and emergency, and the writing and examples in this book definitely reflect that bias—although I have tried not to make every example an emergency department example! The basic concepts of quality, value, performance, and analytics will translate well to almost any setting, whether it is medicine, surgery, home care, or primary care.
In my opinion, the real value of analytics occurs when the insight generated through analytical tools and techniques can be used directly by quality improvement teams, frontline staff, and other healthcare professionals to improve the quality and efficiency of patient care. To some, this may not be the most glamorous application of analytics, but it is the most important.
Book Overview
After a discussion of the escalating inefficiencies and costs of healthcare (Chapter 1), a high-level overview of the various components of an effective analytics system within an HCO is covered in Chapter 2. Because of the need for strong alignment between the quality and process improvement goals of the organization, the various demands facing healthcare IT departments, and the balancing that analytics must do between these competing interests, Chapter 3 provides an overview of an effective analytics strategy framework that HCOs can use to keep their focus on efforts that achieve the desired improvement results of the organization. Chapter 4 is an overview of the concepts of quality and value, and how these are measured within an HCO. Three quality improvement methodologies (PDSA, Lean, and Six Sigma) are discussed in Chapter 4 as well, and how analytics can provide support to these various types of initiatives.
Chapters 5, 6, and 7 focus on data. Chapter 5 is an overview of data quality and data management, and how to ensure that analytics professionals and stakeholders have access to the high-quality data they need in order to provide information and insight to the organization. Chapter 6 discusses the different types of data, important methods of summarizing and understanding data, and how data type affects the kind of analysis that is possible. Chapter 7 provides tips on how to convert data into metrics and indicators that provide the HCO with a much clearer lens through which to monitor and evaluate performance and quality.
Chapter 8 is about how to meld analytics and quality improvement activities so that QI teams can benefit from the insight and information available throughout all phases of QI projects, regardless of the QI methodology that is chosen. Chapter 9 highlights several of the key statistical and graphical methods for monitoring performance and detecting when in fact a true change in performance or quality has occurred. Chapter 10 talks about usability of analytics from an access and presentation point of view. The advanced analytics discussed in Chapter 11 includes tools such as regression and machine-learning approaches that can be used to identify patterns in healthcare data and predict likely outcomes.
Finally, Chapter 12 discusses achieving analytics excellence within an HCO, including the types of leadership and management required within an HCO to ensure that data and privacy are held secure and that analytics is used appropriately and to its maximum effectiveness.