Clinical Analytics and Data Management for the DNP

Description:

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

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Preface:

When we first conceived of this book, our intent was to create a resource that would introduce the theory, processes, and tools needed by professionals to achieve impactful clinical scholarship. We described improvement processes that originated with data from practice that pointed to opportunities to improve and concluded with data that helped to determine if the evidence-based solutions implemented had been effective. We are excited by the number of programs that have adopted this book as a course text; by the quality of the clinical scholarship that has employed this process; and by our conversations with faculty, students, and DNPs at conferences who have shared the pride you feel in the success you have achieved.

It is again time to refresh this resource in order to continue to advance high-quality, high-impact clinical scholarship in the context of a great many developments in policy, analytics, and innovation. In this third edition, we intend to help you stay in the groove with the world of big data, value-based care, and data-driven decision making. We maintain our bright focus on prevention, population health, and the contribution of DNPs to clinical scholarship and practice leadership.

It is useful to identify several trends and historical conditions that have developed since the last version of this text was published. We will take them one-by-one.

■ Big data is in the headlines more than ever. COVID-19 has invited the public to consider large data sets deeply and has introduced suspicion regarding the quality of those data sets as a function of the purposes for which they were constructed. The lay public reads about facts and alternative facts. They have learned unfortunate lessons about data sources, reliability, veracity, bias, and value. The same concerns are relevant in the sciences, healthcare in general, and nursing in particular. These concerns are significant in any conversation about application of data where intent to make sound decisions is entirely dependent on use of reliable, valid, integrated, and well-groomed data. In each situation, the public trust and the strength of resultant decisions rests on the quality and application of strong data skills.

■ Value-based care has supplanted the traditional “pay-for-services” model that formed the structure of our modern healthcare system as we know it and drove costs skyward without similar increase in outcomes and impact. Achieving the full potential of VBC will require engagement of consumers, patients, advocates, and families in identification of the goals of intervention as well as the selection of the measures to be used to evaluate impact.

■ Data-driven decision making is now normative in healthcare. Patients consult the Internet to access research findings, best practices, and outcomes data. They can select providers, hospitals, enterprises, and services on the basis of metrics. The media compares performance on the basis of publicly disclosed data, and colleagues propose solutions citing sources that may be germane to their discipline, specialty, or community. Learning to critically evaluate these sources and findings is key to competent decision making.

■ Accountable care has transformed the market. Regardless of the political context, the movement toward accountable care has accomplished greater transparency in the form of consistent public reporting of healthcare outcomes. This has, in turn, stepped up the pressure to improve and make new performance data public.

■ Prevention, which has always made sense but the compensation for which has been tricky, now registers as an efficient and socially responsible approach to health and healthcare. New providers, new processes, and new data are available from preventive services, and these need to be married to traditional healthcare data to evaluate and improve outcomes.

■ Health disparities related to race, gender and gender identity, faith, economic status, housing status, and education have come into bright focus and engaged many individuals and groups in advocacy to achieve inclusion, health equity, and social justice.

■ Population health has increasingly become part of everyday conversations about the pandemic, vaccines, masking, social distancing, and other measures we can employ to protect individuals in society and promote the good of society as a whole.

■ Broad-reaching collaborations among payers, providers, and systems of healthcare delivery are becoming the new norm in caring for populations of patients. Through these partnerships, collaborators share the risk for the cost of healthcare services, quality outcomes, and patient experiences. They share data sets and outcomes as well.

■ DNPs serve in a variety of practice leadership roles and are positioned to influence outcomes on a large scale. Today, more than 357 programs in 50 states and the District of Columbia offer DNP education, and 124 more will open programs in the coming years. Some states, including California, Florida, Illinois, Massachusetts, Minnesota, New York, Ohio, Pennsylvania, and Texas, have more than 10 programs from which to choose. Postbaccalaureate programs are fast approaching post-master’s degree programs in number. During 2018 to 2019, the number of students enrolled in DNP programs increased from 32,678 to 36,069.

During that same period, the number of DNP graduates increased from 7,039 to 7,944 (American Association of Colleges of Nursing, 2020).

Under these conditions, it is essential that DNPs master the clinical data management and analytics skills essential for testing the impact of the practice improvements they lead and effectively reporting the findings of their work.

We are committed to the success of the DNP. To that end, we have assembled a team of authors with diverse expertise to present a compendium of best practices and resources to guide DNPs through conduct of robust scholarly work that concludes in reliable, impactful, and a useful analysis, evaluation, and report. This book prepares students to capably apply evidence to address important clinical problems and use data to quantify and measure the results of every step of the evidence-based process.

For over 15 years, we have taught students to adopt a posture of inquiry in their work, to use evidence to tackle important problems, and to rigorously evaluate their efforts. This approach prepares graduates to continually improve practice, deliver outcomes, and establish solid, credible programs of translation and improvement.

As faculty, we practice what we preach. We continuously evaluate the outcomes of our teaching and the work of our students. We reviewed curricula and identified the need to provide structure to guide projects through planning, execution, revision, evaluation, and report. We reviewed the scholarly projects of DNPs and identified the need to strengthen analytics. We reviewed dissemination activities and identified the need to help students

present their findings more clearly and more effectively. We spoke with graduates who requested additional resources to guide data collection, management, analysis, and reporting for the broad range of activities they pursue following graduation. Also, we spoke with faculty teaching in DNP programs across the country who requested a more comprehensive resource, including data sets and exercises that could complement and strengthen instruction.

High-quality research and statistics books are available and are useful as a foundation for the design and significance testing. This book complements those resources by preparing DNPs to translate evidence into practice and evaluate impact. This text explains robust strategies that can be used so that data available in everyday practice can serve the purpose of supporting trustworthy evaluation of outcomes.

This new edition includes data files for download as requested. New data sources illustrate content within particular healthcare settings (e.g., hospitals and units within hospitals) as well as data sources that span multiple settings.

Ancillary materials provide an opportunity for faculty to lead students in the conduct of certain activities laid out within the chapters. PowerPoint slides are included. In addition, forms to be used in the conduct of the work are presented in relevant chapters, especially related to data management planning.

Earlier editions of this book have been warmly received. More importantly, students who used that book reported feeling capable, comfortable, and independent in the analysis of their projects.

■ Kristin Hickerson, DNP, designed and implemented a program designed to bridge the preparation–practice gap using a combination of regular communication, preceptor huddles, checklists, online resources, and hands-on teaching materials. Novice nurse satisfaction increased from 3.1 to 3.6; novice nurse Basic Knowledge Assessment Test (BKAT) scores (competency measurement) increased from 73 to 83 (p < .001); preceptor satisfaction increased from 3.0 to 3.2 (p = .04); and preceptor competence increased from 4.7 to 4.8 (Hickerson et al., 2016).

■ Erik Southard, DNP, used evidence and technology to make consultation support available to communities across rural Indiana, where psychiatric professionals are in short supply. He documented a decreased time to consult from 16.2 to 5.4 hours (Southard et al., 2014).

■ Mariam Kashani, DNP, FNP, and her colleagues added family risk and history of premature heart disease to risk assessment on the basis of the Framingham Risk Score. As a result, 48% of 114 patients who had originally been classified as low to moderate risk were reclassified as high risk, and 72% of those were found to have dyslipidemia, 35% had hypertension, 20% were prediabetic, and 61% evidenced atherosclerosis (Kashani et al., 2013).

■ Lina Younan, DNP, used evidence to establish handoff procedures that reduced errors of omission during intershift handoff (n = 90) from 4.96 per patient handoff at baseline (standard deviation = 3.62) to 2.29 per patient handoff postintervention (t = 6.29, p < .000)

(Younan & Fralic, 2013).

These students and many more have tackled problems important to their organizations and communities. The analytics they applied allowed confidence in the conclusions they drew, and all achieved statistically significant improvements. Applications of the processes described in this book have led to an increase in scholarly publications. More importantly, the quality and rigor of DNP scholarship and practice have been elevated (Terhaar & Sylvia, 2016).

Encouraged and challenged by these developments, and aware that conduct of a quality work requires careful and consistent guidance, advising, and support, this second edition presents data sets, exercises, and guidance that will be useful to students and faculty alike. We are confident that the resources presented in this third edition will be useful to students, faculty leading other programs, and the organizations that employ DNPs. We want to share the evidence we gathered to direct planning, gathering, entering, transforming, cleansing, governing, analyzing, and reporting of data. We encourage schools of nursing with DNP programs to adopt and improve this process in support of establishing consistent, rigorous, well-evaluated translation. We want research to reach practical application expediently so the triple aim of quality, experience, and value can be attained (Berwick et al., 2008) and so society can reap the return on its investment in basic research.

As we wrote in the first edition, we subscribe to the old adage, If you want something done well, do it yourself! Now, we set out to write a book that extends and enhances the success our students have earned. We hope it is helpful to all who seek to improve practice and outcomes through judicious application of evidence and rigorous evaluation of the results. We hope this book becomes a resource you will revisit repeatedly to advance your success and improve outcomes for years to come.

Table of contents :

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