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
1. Introduction to Clinical Data Management - Mary F. Terhaar 2. Basic Statistical Concepts and Power Analysis - Martha L. Sylvia 3. Value Based Purchasing - Mary F. Terhaar 4. Using Data to Support the Problem Statement - Martha L. Sylvia 5. Selecting Quality Measures - Martha L. Sylvia 6. Preparing for Data Collection - Martha L. Sylvia, Mary F. Terhaar 7. Secondary Data Collection - Emily Johnson, Martha L. Sylvia 8. Primary Data Collection - Martha L. Sylvia 9. Developing the Analysis Plan - Martha L. Sylvia, Mary F. Terhaar 10. Data Governance and Stewardship - Martha L. Sylvia, Mary F. Terhaar 11. Best Practices for Submission to the Institutional Review Board - Mary F. Terhaar, Laura A. Taylor 12. Creating the Analysis Data Set - Martha L. Sylvia 13. Exploratory Data Analysis - Martha L. Sylvia, Shannon Murphy 14. Outcomes Data Analysis - Martha L. Sylvia, Shannon Murphy 15. Summarizing the Results of the Project Evaluation - Martha L. Sylvia 16. Ongoing Monitoring - Melissa Sherry, Martha Sylvia 17. Data Visualization - Erik Sederstrom 18. Nursing Excellence Recognition and Benchmarking Programs - Heather Craven 19. Risk Adjustment - Martha L. Sylvia 20. Big Data, Data Science and Analytics - Marisa L. Wilson 21. Predictive Modeling - Martha L. Sylvia
