Praise for the First Edition: “DNP students may struggle with data management, since their projects are not research, but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects." Score: 98, 5 Stars --Doody's Medical Reviews This is the only text to deliver the strong data management knowledge and skills that are required competencies for all DNP students. It enables readers to design data tracking and clinical analytics in order to rigorously evaluate clinical innovations/programs for improving clinical outcomes, and to document and analyze change. The second edition is greatly expanded and updated to address major changes in our health care environment. Incorporating faculty and student input, it now includes modalities such as SPSS, Excel, and Tableau to address diverse data management tasks. Eleven new chapters cover the use of big data analytics, ongoing progress towards value-based payment, the ACA and its future, shifting of risk and accountability to hospitals and clinicians, advancement of nursing quality indicators, and new requirements for Magnet certification. The text takes the DNP student step by step through the complete process of data management from planning to presentation, and encompasses the scope of skills required for students to apply relevant analytics to systematically and confidently tackle the clinical interventions data obtained as part of the DNP student project. Of particular value is a progressive case study illustrating multiple techniques and methods throughout the chapters. Sample data sets and exercises, along with objectives, references, and examples in each chapter, reinforce information. Key Features: Provides extensive content for rigorously evaluating DNP innovations/projects Takes DNP students through the complete process of data management from planning through presentation Includes a progressive case study illustrating multiple techniques and methods Offers very specific examples of application and utility of techniques Delivers sample data sets, exercises, PowerPoint slides and more, compiled in Supplemental Materials and an Instructor Manual

Praise for the first edition: "DNP students may struggle with data management, since their projects are not research but quality improvement, and this book covers the subject well. I recommend it for DNP students for use during their capstone projects." Score: 98, 5 Stars -- Doody's Medical Reviews This unique text and reference—the only book to address the full spectrum of clinical data management for the DNP student—instills a fundamental understanding of how clinical data is gathered, used, and analyzed, and how to incorporate this data into a quality DNP project. The new third edition is updated to reflect changes in national health policy such as quality measurements, bundled payments for specialty care, and Advances to the Affordable Care Act (ACA) and evolving programs through the Centers for Medicare and Medicaid Services (CMS). The third edition reflects the revision of 2021 AACN Essentials and provides data sets and other examples in Excel and SPSS format, along with several new chapters. This resource takes the DNP student step-by-step through the complete process of data management, from planning through presentation, clinical applications of data management that are discipline-specific, and customization of statistical techniques to address clinical data management goals. Chapters are brimming with descriptions, resources, and exemplars that are helpful to both faculty and students. Topics spotlight requisite competencies for DNP clinicians and leaders such as phases of clinical data management, statistics and analytics, assessment of clinical and economic outcomes, value-based care, quality improvement, benchmarking, and data visualization. A progressive case study highlights multiple techniques and methods throughout the text. New to the Third Edition: New Chapter: Using EMR Data for the DNP Project New chapter solidifies link between EBP and Analytics for the DNP project New chapter highlights use of workflow mapping to transition between current and future state, while simultaneously visualizing process measures needed to ensure success of the DNP project Includes more examples to provide practical application exercises for students Key Features: Disseminates robust strategies for using available data from everyday practice to support trustworthy evaluation of outcomes Uses multiple tools to meet data management objectives [SPSS, Excel®, Tableau] Presents case studies to illustrate multiple techniques and methods throughout chapters Includes specific examples of the application and utility of these techniques using software that is familiar to graduate nursing students Offers real world examples of completed DNP projects Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan

Designed as a textbook for the DNP curriculum and as a practical resource for more seasoned health professionals, this acclaimed text encompasses an interprofessional approach to translating evidence into nursing and health care practice that is useful for both clinical and nonclinical environments. The second edition presents new chapters, three of which feature additional approaches for translating evidence into practice, new methods of information technology for translation, and interprofessional collaboration and practice for translation and three that offer 19 exemplars that illustrate actual translation work within the areas of population health and specialty practice, and in the health care system. Consistently woven throughout are the themes of integration and application of knowledge into practice, leadership and evaluating change, leadership strategies for translation, and interprofessional applications across settings. Also included is new information about outcomes management for improvement of direct and indirect care. The second edition continues to deliver applicable theory and strategies to achieve improved outcomes, and meets the DNP core competency requirements. It features a variety of models for change as they relate to translation of research into practice. The text underscores the importance of translating evidence for use in practice to improve health care and health care delivery, and presents strategies to achieve this. It addresses the use of evidence to improve nursing education, discusses how to reduce the divide between researchers and policy makers, and presents expedients for overcoming resistance to change. Extensive lists of references, web links, and other resources enhance learning and support the development of the DNP core competencies. NEW TO THE SECOND EDITION: Addition of an esteemed co-editor Reorganized and revised for enhanced comprehension New chapters: Methods for Translation, Information Technology and Decision Support, Interprofessional Collaboration and Practice for Translation, and Data Management and Evaluation of Translation Three new exemplar chapters: Population Health Exemplars, Specialty Practice Exemplars, and Health Care System Exemplars Updated information on integration and application of knowledge into practice, leading and evaluating change, leadership strategies for translation, and interprofessional application across settings New coverage of outcomes management for improvement of direct and indirect care KEY FEATURES: Offers an in-depth guide for planning, implementing, and translating evidence Includes extensive references necessary for doctoral study Provides the perfect supplement for evidence-based practice materials that often have limited information or value for translation/implementation activities

This book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.

Introducing Statistics & Data Analytics for Health Data Management by Nadinia Davis and Betsy Shiland, an engaging new text that emphasizes the easy-to-learn, practical use of statistics and manipulation of data in the health care setting. With its unique hands-on approach and friendly writing style, this vivid text uses real-world examples to show you how to identify the problem, find the right data, generate the statistics, and present the information to other users. Brief Case scenarios ask you to apply information to situations Health Information Management professionals encounter every day, and review questions are tied to learning objectives and Bloom’s taxonomy to reinforce core content. From planning budgets to explaining accounting methodologies, Statistics & Data Analytics addresses the key HIM Associate Degree-Entry Level competencies required by CAHIIM and covered in the RHIT exam. Meets key HIM Associate Degree-Entry Level competencies, as required by CAHIIM and covered on the RHIT registry exam, so you get the most accurate and timely content, plus in-depth knowledge of statistics as used on the job. Friendly, engaging writing style offers a student-centered approach to the often daunting subject of statistics. Four-color design with ample visuals makes this the only textbook of its kind to approach bland statistical concepts and unfamiliar health care settings with vivid illustrations and photos. Math review chapter brings you up-to-speed on the math skills you need to complete the text. Brief Case scenarios strengthen the text’s hands-on, practical approach by taking the information presented and asking you to apply it to situations HIM professionals encounter every day. Takeaway boxes highlight key points and important concepts. Math Review boxes remind you of basic arithmetic, often while providing additional practice. Stat Tip boxes explain trickier calculations, often with Excel formulas, and warn of pitfalls in tabulation. Review questions are tied to learning objectives and Bloom’s taxonomy to reinforce core content and let you check your understanding of all aspects of a topic. Integrated exercises give you time to pause, reflect, and retain what you have learned. Answers to integrated exercises, Brief Case scenarios, and review questions in the back of the book offer an opportunity for self-study. Appendix of commonly used formulas provides easy reference to every formula used in the textbook. A comprehensive glossary gives you one central location to look up the meaning of new terminology. Instructor resources include TEACH lesson plans, PowerPoint slides, classroom handouts, and a 500-question Test Bank in ExamView that help prepare instructors for classroom lectures.

As the focus of the health care delivery system continues to move toward a coordinated and accountable system, there is an increasing need for a single resource that focuses on analytics for population health. Population Health Analytics addresses that need by providing detailed information and a “how to” guide for achieving population health analytics. Comprehensive, current, and practical, this logically organized text builds from understanding data sources, to contextualizing data, modeling data, and gleaning insights from that data, which is a natural progression for organizations in progressing to higher levels of analytic capabilities. Furthermore, these frameworks for the population health process and analytics are grounded in an evidence base that is also aligned with theories and processes used in healthcare disciplines. This first of its kind text will prepare students to improve health outcomes, understand patterns of health behavior and more.

Healthcare Data Analytics and Management help readers disseminate cutting-edge research that delivers insights into the analytic tools, opportunities, novel strategies, techniques and challenges for handling big data, data analytics and management in healthcare. As the rapidly expanding and heterogeneous nature of healthcare data poses challenges for big data analytics, this book targets researchers and bioengineers from areas of machine learning, data mining, data management, and healthcare providers, along with clinical researchers and physicians who are interested in the management and analysis of healthcare data. Covers data analysis, management and security concepts and tools in the healthcare domain Highlights electronic medical health records and patient information records Discusses the different techniques to integrate Big data and Internet-of-Things in healthcare, including machine learning and data mining Includes multidisciplinary contributions in relation to healthcare applications and challenges