Clinical Analytics and Data Management for the DNP
by Martha L. Sylvia, Mary F. Terhaar
3rd Edition
Publisher: Springer Publishing Company
Book Details
| Print ISBN | 9780826163233 |
| eText ISBN | 9780826163240 |
| Publisher | Springer Publishing Company |
| Publishing Year | 2023 |
| Edition | 3rd Edition |
| Language | English |
| Pages | 496 |
Clinical Analytics and Data Management for the DNP, 3rd Edition is a textbook structured to assist Doctor of Nursing Practice students with clinical data management during their doctoral projects. The volume offers guidance for graduate nursing students, DNP clinicians, and executive leaders who need to organize healthcare information to support evidence-based practice and quality improvement initiatives.
This updated text aligns with the 2021 AACN Essentials revision while exploring key developments in national health policy. Core topics encompass quality measurements, value-based care, benchmarking, and bundled payments for specialty care. The material also incorporates policy changes regarding the Affordable Care Act and evolving initiatives from the Centers for Medicare and Medicaid Services.
By connecting policy structures with analytical methods, the text helps students structure clinical data to evaluate practice outcomes. Nursing faculty can adopt this resource for graduate-level courses focused on advanced analytics, quality measurement, and project leadership across diverse clinical environments.
Table of Contents
Chapter 1: Introduction to Clinical Data Management
Chapter 2: Analytics and Evidence-Based Practice
Chapter 3: Using Data to Support the Problem Statement
Chapter 4: Preparing for Data Collection
Chapter 5: Secondary Data Collection
Chapter 6: Primary Data Collection
Chapter 7: Using EHR Data for the DNP Project
Chapter 8: Determining the Project Measures
Chapter 9: Using Statistical Techniques to Plan the DNP Project
Chapter 10: Using Workflow Mapping to Plan the DNP Project Implementation
Chapter 11: Developing the Analysis Plan
Chapter 12: Best Practices for Submission to the Institutional Review Board
Chapter 13: Creating the Analysis Data Set
Chapter 14: Exploratory Data Analysis
Chapter 15: Outcomes Data Analysis
Chapter 16: Summarizing the Results of the Project
Chapter 17: Ongoing Monitoring
Chapter 18: Data Governance and Stewardship
Chapter 19: Value-Based Care
Chapter 20: Nursing Excellence Recognition and Benchmark Programs
Chapter 21: Data Visualization
Chapter 22: Risk Adjustment
Chapter 23: Big Data, Data Science, and Analytics
Chapter 24: Predictive Modeling
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