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Clinical Analytics and Data Management for the DNP

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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 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. Purchase includes online access via most mobile devices or computers.

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
  • Provides Instructor’s Manual, PowerPoint slides, data sets in SPSS and Excel, and forms for completion of data management and evaluation plan

Author(s): Martha L. Sylvia, Mary F. Terhaar
Edition: 3
Publisher: Springer Publishing Company
Year: 2023

Language: English
Pages: 494
City: New York

Cover
Half Title: CLINICAL ANALYTICS AND DATA MANAGEMENT FOR THE DNP
Author Bio
Book Title: CLINICAL ANALYTICS AND DATA MANAGEMENT FOR THE DNP
Copyright
Dedication
CONTENTS
CONTRIBUTORS
FOREWORD FOR THE THIRD EDITION
PREFACE
INSTRUCTOR RESOURCES
Part 1: Introduction
Chapter 1: Introduction to Clinical Data Management
PROBLEM SOLVING
TRANSLATION
THE DOCTOR OF NURSING PRACTICE AS PROBLEM SOLVER, TRANSLATOR, AND ANALYST
THE CONTEXT OF DISCOVERY AND INNOVATION
CLINICAL DATA MANAGEMENT
SECTION II: data PLANNING and preparation‌‌
SECTION III: preparing for project implementation
Section IV: Implementing and evaluating project results
SECTION V: Key competencies for dnp practice
Section VI: Advanced analytic techniques
CONCLUSION
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 2: Analytics and Evidence- Based Practice‌‌
LEARNING OBJECTIVES
Ev
idence-based practice and the DNP project
A
nalytic methods in support of evidence-based practice
SUMMARY
END-OF-CHAPTER RESOURCES
References
Part 2: Data Planning and Preparation
Chapter 3: Using Data to Support the Problem Statement
LEARNING OBJECTIVES
PROBLEM STATEMENTS IN DNP PROJECTS
WHY USE DATA TO SUPPORT THE PROBLEM STATEMENT?
WHERE TO FIND DATA TO SUPPORT THE PROBLEM STATEMENT
SUMMARY
END-OF-CHAPTER RESOURCES
PROBLEM STATEMENT EXEMPLAR
REFERENCES
Chapter 4: Preparing for Data Collection
LEARNING OBJECTIVES
PRIMARY AND SECONDARY DATA
BENEFITS AND LIMITATIONS
THE DECISION TO USE PRIMARY OR SECONDARY DATA
Summary
END-OF-CHAPTER RESOURCES
PROBLEM STATEMENT EXEMPLAR
Reference
Chapter 5: Secondary Data Collection
LEARNING OBJECTIVES
SECONDARY DATA
SOURCES FOR SECONDARY DATA
RESEARCH DATABASES
METHODS FOR OBTAINING SECONDARY DATA
REQUESTING SECONDARY DATA FROM ORGANIZATIONS
EXAMPLES OF SECONDARY DATA SETS
QUALITY (RELIABILITY AND VALIDITY) OF SECONDARY DATA
MISSING AND INADEQUACY OF CERTAIN CONCEPTS IN SECONDARY DATA
STORING SECONDARY DATA
SUMMARY
END-OF-CHAPTER RESOURCES
PROBLEM STATEMENT EXEMPLAR
CLINICAL DATA MANAGEMENT EVALUATION PLAN
Aim 2: Increase the number of individuals with HCV who receive appropriate referral for treatment t
Aim 3: Increase the percentage of CHCI providers who utilize Project ECHO, a telehealth model of kn
REFERENCES
Chapter 6: Primary Data Collection
LEARNING OBJECTIVES
PRIMARY DATA
Summary
END-OF-CHAPTER RESOURCES
PROBLEM STATEMENT EXEMPLAR
REFERENCES
Chapter 7: Using EHR Data for the DNP Project
LEARNING OBJECTIVES
INTRODUCTION AND BACKGROUND
The electronic health record
Distinguishing Front-End Data Entry from Back-End Data structure
data points available in the EHR
acquiring data from the EHR
using EHR data in the DNP project to perform a root cause analysis of the problem being addressed i
using EHR data to implement, monitor, and evaluate interventions
SUMMARY
END-OF-CHAPTER RESOURCES
EHR DAta Exercise
REFERENCES
Part 3: Preparing for Project Implementation
Chapter 8: Determining the Project Measures
LEARNING OBJECTIVES
DEFINITIONS AND CONSIDERATIONS
WHY MEASURE?
STRUCTURE, PROCESS, OUTCOME
CONSIDERATIONS FOR THE SELECTION OF MEASURES
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 9: Using Statistical Techniques to Plan the DNP Project
LEARNING OBJECTIVES
REVIEW OF VARIABLE CONCEPTS
BASIC STATISTICAL TESTS AND CHOOSING APPROPRIATELY
DETERMINING THE NUMBER OF PARTICIPANTS FOR THE DNP PROJECT INTERVENTION
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 10: Using Workflow Mapping to Plan the DNP Project Implementation
LEARNING OBJECTIVES
INTRODUCTION AND BACKGROUND
WORKFLOW MAPPING CONCEPTS AND TECHNIQUES
WORKFLOW MAPPING SOFTWARE AND TOOLS
SELECTING PROCESS MEASURES
SUSTAINING NEW WORKFLOWS
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 11: Developing the Analysis Plan
Learning Objectives
Focus of the Analysis
DETERMINING THE UNIT OF ANALYSIS
DETERMINING THE VARIABLES OF THE DATA SET
SUMMARY
END-OF-CHAPTER RESOURCES
EXEMPLAR
REFERENCES
Chapter 12: Best Practices for Submission to the Institutional Review Board
LEARNING OBJECTIVES
The Work of the IRB
LAWS RELEVANT TO HUMAN SUBJECTS RESEARCH
Summary
END-OF-CHAPTER RESOURCES
REFERENCES
Part 4: Implementing and Evaluating Project Results
Chapter 13: Creating the Analysis Data Set
LEARNING OBJECTIVES
PRELIMINARY DATA PREPARATION
DATA CLEANSING
FILE AND DATA MANIPULATION
FINAL ANALYSIS DATA SET AND DATA DICTIONARY
Summary
END-OF-CHAPTER RESOURCES
REFERENCES
CASE STUDY
Chapter 14: Exploratory Data Analysis
LEARNING OBJECTIVES
EXPLORING DISTRIBUTIONS OF VALUES FOR EACH VARIABLE
Summary
END-OF-CHAPTER RESOURCES
REFERENCES
CASE STUDY EXAMPLE: EXPLORATORY DATA ANALYSIS (EDA)
Chapter 15: Outcomes Data Analysis
LEARNING OBJECTIVES
BIVARIATE STATISTICAL TESTING
DESCRIBING THE UNIT OF ANALYSIS AND DIFFERENCES BETWEEN GROUPS
DESCRIBING UNCERTAINTY
RECOGNIZING CONFOUNDING
PERFORMING BIVARIATE STATISTICAL TESTING OF OUTCOME MEASURES
PERFORMING MULTIVARIATE TESTING OF OUTCOMES
OTHER CONSIDERATIONS WHEN MEASURING OUTCOMES
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
CASE STUDY EXAMPLE: OUTCOMES DATA ANALYSIS (ODA)
Chapter 16: Summarizing the Results of the Project
LEARNING OBJECTIVES
REPORTING RESULTS
IMPORTANT ASPECTS OF VISUALIZATION AND DISPLAY OF RESULTS
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
CASE STUDY
Chapter 17: Ongoing Monitoring
LEARNING OBJECTIVES
THE NEED FOR ONGOING MONITORING
GOALS OF ONGOING MONITORING
CHALLENGES IN ONGOING MONITORING
RUN CHARTS AND SPC
BENCHMARKS
CONTINUOUS QUALITY IMPROVEMENT
Summary
END-OF-CHAPTER RESOURCES
REFERENCES
Case Study Example: Ongoing Monitoring
Part 5: Key Competencies for DNP Practice
Chapter 18: Data Governance and Stewardship
LEARNING OBJECTIVES
BACKGROUND
ORGANIZATIONAL DATA GOVERNANCE POLICY
DATA STEWARDSHIP, GOVERNANCE STRUCTURES, AND PROCESSES WITHIN THE ORGANIZATION
PATIENT IDENTIFIERS
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 19: Value-Based Care
LEARNING OBJECTIVES
THE INTENT
THE CUSTOMER/PATIENT PERSPECTIVE
THE CLINICIAN’S PERSPECTIVE
BEST PRACTICES FOR EVALUATION OF VBC CARE
EXAMPLES OF VBC
POLICY AND FEDERAL PROGRAMS
CAUTIONS
SUMMARY
END-OF-CHAPTER RESOURCES
VBC EXERCISE
REFERENCES
Chapter 20: Nursing Excellence Recognition and Benchmarking Programs
LEARNING OBJECTIVES
NURSING QUALITY BENCHMARKING PROGRAMS
NURSING EXCELLENCE RECOGNITION PROGRAMS
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Part 6: Advanced Analytic Techniques
Chapter 21: Data Visualization
LEARNING OBJECTIVES
INTRODUCTION AND BACKGROUND
DATA VISUALIZATION CONCEPTS AND TECHNIQUES
DATA VISUALIZATION SOFTWARE AND TOOLS
THE DATA STORY
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
CASE STUDY
Chapter 22: Risk Adjustment
LEARNING OBJECTIVES
RISK ADJUSTMENT
RISK ADJUSTMENT STRATEGY
RISK ADJUSTMENT METHODS
RISK ADJUSTMENT EXAMPLES
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 23: Big Data, Data Science, and Analytics
LEARNING OBJECTIVES
BACKGROUND AND SIGNIFICANCE
BIG DATA
OBJECTIVES OF BIG DATA SCIENCE
LAYERS NEEDED TO SUPPORT DATA SCIENCE AND ANALYTICS
BIG DATA TOOLKIT
APPLICATIONS OF BIG DATA
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
Chapter 24: Predictive Modeling
LEARNING OBJECTIVES
RISK PREDICTION MODELING
DEVELOPING RISK PREDICTION MODELS
ASSESSMENT AND VALIDATION OF RISK PREDICTION MODELS
SUMMARY
END-OF-CHAPTER RESOURCES
REFERENCES
INDEX