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Clinical Analytics and Data Management for the DNP
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Title Details
3rd Ed.
2024 © Springer Publishing Company
Martha Sylvia, PhD, MBA, RN; Mary F. Terhaar DNSc, RN, ANEF, FAAN
ISBN-13: 978-0-8261-6323-3
eISBN-13: 978-0-8261-6324-0
Nursing
Additional Details
Description
Audience
Doody's 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
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