Improving Health Care Quality. Cecilia Fernanda Martinez
deliver. Numerous frameworks and programs such as Lean Six Sigma, High Reliability Organization, and the Institute of Medicine's six domains of health care quality (among others) exist to help identify opportunities and implement meaningful change. These evidence‐based approaches provide the necessary structure and process for successfully driving quality improvement results throughout a provider organization and help focus the attention of everyone involved toward the critical work required to attain them.
However, while each of these frameworks can be extremely useful as a guide, it is only through the review and analysis of relevant data that the most impactful opportunities for improvement can be appropriately recognized and addressed, and the success of improvement efforts ultimately measured – which is exactly what this book is designed to help you accomplish. Data literacy is now every healthcare leader's responsibility, and this text will help you learn how to apply the concepts, processes, techniques, and tools required for extracting meaningful and actionable information from your data. And do not worry if you have never before studied statistical analysis (or have but are still intimidated by it!), as everything here is presented in a very easy‐to‐follow, easy‐to‐understand manner. The use of JMP® software, which itself is quite easy to use, also facilitates the learning process.
I welcome and encourage every reader to use the information in this book to help make the quality of the care your (or your future) organization delivers the absolute best it can be, and I congratulate each of the authors on creating a fantastic resource for all of us in healthcare who strive every day to achieve this important goal. And who knows… maybe I'll get to join in for the Second Edition!
Eric Stephens, MBA, CAP
Chief Analytics Officer
Nashville General Hospital
Nashville, TN
January 11, 2020
Preface
Electronic health records, medical imaging, cell phones/wearable devices, and all‐payer claims databases are a few of the technological advances that supply vast amounts of data to the healthcare industry. This data supports the development of new medical devices and pharmaceuticals, enables healthcare systems to contain costs and improve the delivery of services, and informs the medical decision‐making of clinicians and patients. The healthcare industry offers a diversity of opportunities for careers in clinical practice, administration, research and analytics. Increasingly, the healthcare workforce must be able to make use of data to tackle a broad range of problems.
As educators, our job is to prepare students with the skills to enter the workforce and be successful contributors in healthcare and other fields. While the four of us have a diversity of experience and backgrounds, we share a desire to teach statistics and quality improvement in a very practical way. Each of us has made use of case studies in our classrooms and found them an effective way for our students to learn practical analytic skills that are easily transferred to the workplace.
As we began to plan what this book would look like we considered several questions. One, what would we find helpful in teaching our students? Two, what would our students find helpful? Three, what would people who are trying to teach themselves about quality improvement find helpful? Those questions led us to the overarching goal of creating practical, real‐life case studies for statistics and quality improvement courses that are targeted toward current and future healthcare professionals. These courses can be part of traditional higher education programs with a healthcare focus at either the undergraduate or graduate level or part of continuing education programs for working professionals. Our goal is to offer a set of cases that would provide instruction on most of the statistical tools needed in healthcare quality improvement. A secondary goal was to make the book broad enough to be useful outside the healthcare arena.
We have made a concerted effort to make these cases user friendly for classroom and online instructors, students being assigned cases for learning or assessment, and the self‐directed learner seeking to solve practical problems in the workplace. We intentionally created cases of different lengths and levels of difficulty to meet as many needs as possible.
The use of software is now a part of nearly all statistics and quality improvement classes, but integrating technology into a course is always a tricky proposition. In addition to learning the content, there is the added burden for the students to quickly develop facility with a software tool. A frequent debate among statistics educators is the selection of the appropriate software for a particular course with concern about the need to impart software skills that will be applicable in the workplace. Given the number and diversity of available software packages, we are less concerned with specifically which software is used but rather that students at all levels use SOME software to analyze data. The basic concepts associated with any particular software are readily transferred to another. Our software of choice is JMP®. Together, we have many years of experience using JMP in our classrooms, including in online courses. The book is the outgrowth of a project initiated by JMP to fill a need for more healthcare case studies. The focus of the book is on the quality improvement tools and how they are applied to practical problems. While step‐by‐step JMP navigation is provided, the material will be useful for those preferring other software tools.
The JMP instructions provided refer to JMP version 14; however, most instructions will be appropriate for previous versions. As new versions of JMP are released, there is always excellent backward compatibility. Although the print book is provided in monocolor, the instructions will have color references that refer to items particular to the JMP user interface.
It is our hope that these cases will be engaging for students and instructors and be a valuable resource for the self‐directed learner seeking to solve practical problems.
Jane Oppenlander, Schenectady, NY
9 March 2020
Acknowledgments
We would like to thank Mia Stephens of JMP® for bringing a group of strangers together to talk about health care case studies for JMP users. Along with three of the authors, the group included Amy Cohen, Susan Madden, Pat Schaffer, and Eric Stephens. This group brainstormed ideas about how to best meet the needs of health care instructors and professionals who were developing data analysis and JMP skills. The original case structure was the result of these discussions.
A special thanks to Ruth Hummel and Eric Stephens for initial work on the data used in Chapters 3 and 4. Eric also provided invaluable assistance with the data for Chapter 10.
Thanks to Loretta Driskel, Clarkson University, for ideas for graphics.
Thanks to Marilyn Stapleton for her insights into the practice of nursing research along with the nurses of Ellis Medicine for the opportunity to participate in organizational and clinical studies that served as the basis for Chapters 7 and 8.
We would like to give a special thanks to all of our students who helped us improve our teaching over the years, and inspired us to do better every time. We would especially like to thank the students that worked on the ambulatory surgeries and the TJR cases presented here.
We are very appreciative of the two rural hospitals that allowed us to work with them on their process improvement journeys.
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