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E-grāmata: Clinical Analytics and Data Management for the DNP

  • Formāts: 396 pages
  • Izdošanas datums: 28-Mar-2018
  • Izdevniecība: Springer Publishing Co Inc
  • Valoda: eng
  • ISBN-13: 9780826142788
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  • Formāts: 396 pages
  • Izdošanas datums: 28-Mar-2018
  • Izdevniecība: Springer Publishing Co Inc
  • Valoda: eng
  • ISBN-13: 9780826142788
Citas grāmatas par šo tēmu:

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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

Recenzijas

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

Contributors ix
Foreword xi
Preface xv
1 Introduction to Clinical Data Management 1(10)
Mary F. Terhaar
2 Basic Statistical Concepts and Power Analysis 11(16)
Martha L. Sylvia
3 Value-Based Purchasing 27(10)
Mary F. Terhaar
4 Using Data to Support the Problem Statement 37(8)
Martha L. Sylvia
5 Selecting Quality Measures 45(10)
Martha L. Sylvia
6 Preparing for Data Collection 55(6)
Martha L. Sylvia
Mary F. Terhaar
7 Secondary Data Collection 61(26)
Emily Johnson
Martha L. Sylvia
8 Primary Data Collection 87(10)
Martha L. Sylvia
9 Developing the Analysis Plan 97(38)
Martha L. Sylvia
Mary F. Terhaar
10 Data Governance and Stewardship 135(8)
Martha L. Sylvia
Mary F. Terhaar
11 Best Practices for Submission to the Institutional Review Board 143(14)
Mary F. Terhaar
Laura A. Taylor
12 Creating the Analysis Data Set 157(40)
Martha L. Sylvia
13 Exploratory Data Analysis 197(32)
Martha L. Sylvia
Shannon Murphy
14 Outcomes Data Analysis 229(26)
Martha L. Sylvia
Shannon Murphy
15 Summarizing the Results of the Project Evaluation 255(26)
Martha L. Sylvia
16 Ongoing Monitoring 281(28)
Melissa Sherry
Martha L. Sylvia
17 Data Visualization 309(16)
Erik Sederstrom
18 Nursing Excellence Recognition and Benchmarking Programs 325(12)
Heather Craven
19 Risk Adjustment 337(12)
Martha L. Sylvia
20 Big Data, Data Science, and Analytics 349(12)
Marisa L. Wilson
21 Predictive Modeling 361(12)
Martha L. Sylvia
Index 373