Publisher's Synopsis
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 and programs for improving clinical outcomes and to document and analyze change. This second edition has been expanded and updated to address major changes in our healthcare 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 toward value-based payment, the Affordable Care Act 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 through 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.
New to the Second Edition:
- Completely updated and expanded with 11 new chapters
- Includes an extensive data management toolkit with SPSS, Excel, and Tableau
- Describes value-based purchasing and NDNQI measurement programs
- Explains use of data sources to support the problem statement for the DNP project
- Guides selection of quality measures
- Provides best practices for collecting primary and secondary data
- Offers strategic guidelines for institutional review board submission
- Explains methods for risk adjustment in program and intervention monitoring
- Explores predictive a nalytics
- Illustrates applications of big data for the DNP
- Describes Magnet requirements for measuring quality improvement
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 supplemental materials, including sample data set case studies in SPSS and Excel formats, exercises, PowerPoint slides, and more
