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Big data-enabled nursing [electronic resource] : education, research and practice

Big data-enabled nursing [electronic resource] : education, research and practice

자료유형
E-Book(소장)
개인저자
Delaney, Connie W.
서명 / 저자사항
Big data-enabled nursing [electronic resource] : education, research and practice / Connie W. Delaney ... [et al.], editors.
발행사항
Cham :   Springer,   c2017.  
형태사항
1 online resource (xxxv, 488 p.) : ill. (some col.).
총서사항
Health informatics,1431-1917
ISBN
9783319532998 9783319533001 (eBook)
요약
This text reflects how the learning health system infrastructure is maturing and being advanced by health information exchanges (HIEs) with multiple organizations blending their data or enabling distributed computing.  It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery.  Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing has consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Big Data-Enabled Nursing reflects on how health systems have developed and how electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. It provides instruction on the new opportunities for nursing and educates readers on the new skills in research methodologies that are being further enabled by new partnerships spanning all sectors. 
일반주기
Title from e-Book title page.  
내용주기
Big Data and Its Importance in Nursing -- Big Data Use and Its Importance in Healthcare -- A Big Data Primer -- A Closer Look at the Enabling Technologies and Knowledge Value -- Big Data in Healthcare -- Getting to Big Data: National Center Data Repository for Interprofessional Education and Collaborative Practice -- Wrestling with Big Data:  How Nurse Leaders Can Engage -- Clinical and Translational Science Awards (CTSA) Extended Clinical Data Project -- Working in the New Big Data World – Academic/Corporate Partnership Model -- Transformation of Research and Scholarship -- Enhancing Data Access and Utilization: Federal Datasets Relevant to Social Determinants of Health & Health Disparities Research -- Transformation of Health Care Systems -- State of the Science in Data Mining Methods -- Veteran’s Administration Database (VINCI) -- Kaiser-Permanente’s Nursing-Focused Analytics Initiative -- Mobilizing the Nursing Workforce with Data and Analytics at the Point of Care -- The Power of Disparate Data Sources for Answering Thorny Questions in Healthcare: Five Case Examples -- What Big Data Means for Schools of Nursing and Academia -- Readiness for Big Data Science - Scholarship and Research -- Global Society & Big Data: The Future We Can Get Ready For -- Data Analytics and Visualization: The Future with Big Data.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Nursing informatics. Big data.
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URL
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020 ▼a 9783319532998
020 ▼a 9783319533001 (eBook)
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082 0 4 ▼a 610.730285 ▼2 23
084 ▼a 610.730285 ▼2 DDCK
090 ▼a 610.730285
245 0 0 ▼a Big data-enabled nursing ▼h [electronic resource] : ▼b education, research and practice / ▼c Connie W. Delaney ... [et al.], editors.
260 ▼a Cham : ▼b Springer, ▼c c2017.
300 ▼a 1 online resource (xxxv, 488 p.) : ▼b ill. (some col.).
490 1 ▼a Health informatics, ▼x 1431-1917
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Big Data and Its Importance in Nursing -- Big Data Use and Its Importance in Healthcare -- A Big Data Primer -- A Closer Look at the Enabling Technologies and Knowledge Value -- Big Data in Healthcare -- Getting to Big Data: National Center Data Repository for Interprofessional Education and Collaborative Practice -- Wrestling with Big Data:  How Nurse Leaders Can Engage -- Clinical and Translational Science Awards (CTSA) Extended Clinical Data Project -- Working in the New Big Data World – Academic/Corporate Partnership Model -- Transformation of Research and Scholarship -- Enhancing Data Access and Utilization: Federal Datasets Relevant to Social Determinants of Health & Health Disparities Research -- Transformation of Health Care Systems -- State of the Science in Data Mining Methods -- Veteran’s Administration Database (VINCI) -- Kaiser-Permanente’s Nursing-Focused Analytics Initiative -- Mobilizing the Nursing Workforce with Data and Analytics at the Point of Care -- The Power of Disparate Data Sources for Answering Thorny Questions in Healthcare: Five Case Examples -- What Big Data Means for Schools of Nursing and Academia -- Readiness for Big Data Science - Scholarship and Research -- Global Society & Big Data: The Future We Can Get Ready For -- Data Analytics and Visualization: The Future with Big Data.
520 ▼a This text reflects how the learning health system infrastructure is maturing and being advanced by health information exchanges (HIEs) with multiple organizations blending their data or enabling distributed computing.  It educates the readers on the evolution of knowledge discovery methods that span qualitative as well as quantitative data mining, including the expanse of data visualization capacities, are enabling sophisticated discovery.  Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing has consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. In the United States, large payer data has been amassed and structures/organizations have been created to welcome scientists to explore these large data to advance knowledge discovery. Big Data-Enabled Nursing reflects on how health systems have developed and how electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. It provides instruction on the new opportunities for nursing and educates readers on the new skills in research methodologies that are being further enabled by new partnerships spanning all sectors. 
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Nursing informatics.
650 0 ▼a Big data.
700 1 ▼a Delaney, Connie W.
830 0 ▼a Health informatics.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-53300-1
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 610.730285 등록번호 E14015437 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

목차

Part I: The New and Exciting World of “Big Data”
Chapter 1: Why Big Data?: Why Nursing?
1.1 Why Big Data?
1.2 Why Big Data in Nursing?
1.3 Summary
References
Chapter 2: Big Data in Healthcare: A Wide Look at a Broad Subject
2.1 Reaching the Tipping Point: Big Data and Healthcare
2.2 Big Data and Analytics Enabling Innovation in Population Health
2.2.1 Blending in the Social Determinants
2.3 Big Data in Action
2.3.1 The Department of Veterans Affairs
2.3.1.1 Advanced Analytics in the VHA
2.3.1.2 Consolidating Data Sources
2.3.1.3 Data Governance, Access and Quality
2.3.1.4 Advanced Analytics
2.3.1.5 Expanding Data Sources
2.3.1.6 User Impact
2.3.1.7 The VHA Pushes to Use Big Data
2.3.2 A View from Home Health
2.3.2.1 Telemonitoring to Prevent Hospital Readmission from the Home
2.3.2.2 Improving Outcomes of Home Healthcare Patients with Wounds
2.3.2.3 Home Health Powered by Big Data
2.3.3 The Spine: A United Kingdom Big Data Endeavor
2.3.3.1 Healthcare in England
2.3.3.2 The NHS and Health Information Technology
2.3.3.3 The NHS and Big Data
2.4 Summary
References
Chapter 3: A Big Data Primer
3.1 What Is Big Data?
3.1.1 Datafication and Digitization
3.1.2 Resources for Evaluating Big Data Technology
3.2 The V’s: Volume, Variety, Velocity
3.2.1 Volume
3.2.2 Variety
3.2.3 Velocity
3.3 Data Science
3.3.1 What Is Data Science?
3.3.2 The Data Science Process
3.4 Visualizing the Data
3.5 Big Data Is a Team Sport
3.6 Conclusion
Case Study 3.1: Big Data Resources—A Learning Module
3.1.1 Introduction
3.1.2 Resources for Big Data
3.1.2.1 Big Data Conferences
3.1.2.2 Big Data Books and Articles
3.1.2.3 Big Data Videos
3.1.2.4 Big Data Web Sites
3.1.3 Resources for Data Science
3.1.3.1 Data Science Conferences
3.1.3.2 Data Science Books and Articles
3.1.3.3 Data Science Videos
3.1.3.4 Data Science Web Sites
3.1.4 Resources for Data Visualization
3.1.4.1 Data Visualization Conferences
3.1.4.2 Data Visualization Books and Articles
3.1.4.3 Data Visualization Videos
3.1.4.4 Data Visualization Web Sites
3.1.5 Organizations of Interest
3.1.5.1 Professional Associations
3.1.5.2 Listservs: A Sampling
3.1.5.3 Certificates and Training: A Sampling
3.1.5.4 Degree Programs: A Sampling
3.1.6 Assessment of Competencies
3.1.7 Learning Activities
3.1.8 Guidance for Learners and Faculty Using the Module
References
Part II: Technologies and Science of Big Data
Chapter 4: A Closer Look at Enabling Technologies and Knowledge Value
4.1 Introduction
4.2 Emerging Roles and the Technology Enabling Them
4.3 A Closer Look at Technology
4.3.1 Handheld Ultrasound
4.3.2 Point of Care Lab Testing
4.3.3 The Quantified Self Movement
4.3.4 Sleep Monitors
4.3.5 Activity Monitors
4.3.6 Data Mash-Ups
4.3.7 Symptom Checkers
4.3.8 Augmented Cognition
4.4 Big Data Science and the Evolving Role of Nurses
4.5 Conclusion
References
Chapter 5: Big Data in Healthcare: New Methods of Analysis
5.1 Introduction
5.2 Sources of Big Data
5.3 Big Data Analytics
5.3.1 Data Mining
5.3.2 Text Mining
5.3.3 Predictive Modelling
5.3.4 Machine Learning
5.4 Big Data Applications in Nursing
5.5 Challenges of Big Data
5.6 Conclusions
Case Study 5.1: Value-Based Nursing Care Model Development
5.1.1 Value-Based Nursing Care and Big Data
5.1.1.1 Extracting Nursing Data from the EHR
5.1.1.2 Nursing Business Intelligence and Analytics (NBIA)
5.1.2 The Cost of Nursing Care
5.1.3 Summary
References
Chapter 6: Generating the Data for Analyzing the Effects of Interprofessional Teams for Improvi
6.1 Introduction
6.2 Raison D’être for the NCDR
6.2.1 Characteristics of the NCDR
6.2.2 Data Volume
6.2.3 Data Velocity
6.2.4 Data Value
6.2.5 Ecosystem of the NCDR
6.2.6 Infrastructure
6.2.6.1 Project Management
6.2.6.2 Scientific Review
6.2.6.3 Scientific Support
6.2.6.4 Data Repository Structure and Function
6.2.6.5 Analytics
6.2.6.6 Applications
6.3 Conclusions
References
Chapter 7: Wrestling with Big Data: How Nurse Leaders Can Engage
7.1 Introduction
7.2 Defining Big Data and Data Science
7.3 Nursing Leader Accountabilities and Challenges
7.4 Systems Interoperability
7.5 Non-Standardization
7.6 The Invisibility of Nursing
7.7 A Common Data Repository Across the System
7.8 The Value of Big Data for Nurse Leaders
7.9 The Journey to Sharable and Comparable Data in Nursing
7.10 Gaining Insight from Data in Real Time
7.11 Strategies for Moving Forward
7.12 Instilling a Data-Driven Culture Through Team Science
7.13 Putting It All Together: An Example
7.13.1 Step 1: Diagnostic Analytics
7.13.2 Step 2: Diagnostic Analytics
7.13.3 Step 3: Predictive Analytics
7.13.4 Step 4: Prescriptive Analytics
7.14 Conclusions
Case Study 7.1: Improving Nursing Care Through the Trinity Health System Data Warehouse
7.1.1 Introduction
7.1.2 Trinity Health
7.1.2.1 Trinity Health Data Warehouse: A Cross-Continuum Data Environment
7.1.3 Case Studies
7.1.3.1 Interdisciplinary Plans of Care (IPOC) Case Study
7.1.3.2 Pressure Ulcer Case Study
7.1.3.3 Venous Thromboembolus (VTE) Advisory Case Study
7.1.3.4 General to Specific and Failure to Diagnose Case Study
7.1.4 Conclusion
Acknowledgements
References
Chapter 8: Inclusion of Flowsheets from Electronic Health Records to Extend Data for Clinical an
8.1 Introduction
8.2 CTSAs to Support Big Data Science
8.3 Clinical Data Repositories (CDRs)
8.3.1 CDR Structure and Querying Data
8.3.2 Standardizing Patient Data
8.4 What Are Flowsheets?
8.4.1 How Do Organizations Decide What to Record on Flowsheets?
8.4.2 Strengths and Challenges of Flowsheet Data
8.4.3 Example of Pressure Ulcer
8.5 Standardization Essential for Big Data Science
8.5.1 Nursing Information Models
8.5.2 Example Nursing Information Models and Processes
8.5.3 National Collaborative to Standardize Nursing Data
8.6 Conclusion
References
Chapter 9: Working in the New Big Data World: Academic/Corporate Partnership Model
9.1 The Evolving Healthcare Data Landscape
9.2 The Promise and Complexity of Working with Multiple Sources of Data
9.3 Implications of Linked Claims and EHR Data for Nursing Studies
9.4 Big Data Methods
9.5 Beyond Research—Accelerating Clinical/Policy Translation and Innovation
9.6 Innovation and Management of Intellectual Property in Academic/Corporate Partnerships
9.7 The Ongoing Debate About the Merits of RCTs Versus Observational Studies
9.8 Conclusions
Case Study 9.1: Academic/Corporate Partnerships: Development of a Model to Predict Adverse Event
9.1.1 Introduction: Research Objective
9.1.2 Resources Needed for Big-Data Analysis in the OptumLabs Project
9.1.2.1 Multi-disciplinary Teamwork
9.1.2.2 Remote Data Analysis
9.1.2.3 Hardware and Software
9.1.3 Research Process
9.1.3.1 Extracting Relevant Data for Research
9.1.3.2 Preprocessing Data to Support Machine-Learning Work
9.1.3.3 Selecting Appropriate Machine-Learning Approaches for Data Analysis
9.1.4 Conclusion
References
Part III: Revolution of Knowledge Discovery, Dissemination, Translation Through Data Science
Chapter 10: Data Science: Transformation of Research and Scholarship
10.1 Introduction to Nursing Research
10.1.1 Big Data and Nursing
10.1.2 Nursing and Data
10.2 The New World of Data Science
10.3 The Impact of Data Proliferation on Scholarship
10.4 Initiatives Supporting Data Science and Research
10.4.1 National Institutes of Health
10.4.1.1 Training
10.4.1.2 Centers
10.4.1.3 Software
10.4.1.4 Commons
10.4.1.5 Data Index
10.4.2 National Science Foundation
10.4.3 U.S. Department of Energy
10.4.4 U.S. Department of Defense
10.5 Summary
Case Study 10.1: Complexity of Common Disease and Big Data
10.1.1 Type 2 Diabetes (T2D) as a Significant Health Problem
10.1.2 Factors Contributing to T2D
10.1.2.1 Genetics/Genomics
10.1.2.2 The Environment
10.1.3 Epigenetics
10.1.3.1 Overview of Epigenetics
10.1.3.2 Examples of Epigenetic Modification and T2D
10.1.3.3 Summary of Factors Contributing to T2D
10.1.4 Current Initiatives to Leverage the Power of Big Data for Common Disease
10.1.4.1 Omics
10.1.4.2 Clinical Genomic Resources
10.1.5 Scope and Practice of Genetics/Genomics Nursing
10.1.6 Conclusion
References
Chapter 11: Answering Research Questions with National Clinical Research Networks
11.1 The Vision
11.2 Electronic Data
11.3 Distributed Data Networks
11.3.1 The Mini-Sentinel Distributed Database
11.4 PCORnet, the National Patient-Centered Clinical Research Network
11.4.1 The Partner Networks
11.4.2 Governance
11.4.3 Data Handling
11.5 Current State
11.6 Future Plans
11.7 PCORnet in Practice: pSCANNER
11.7.1 Stakeholder Engagement
11.7.2 Research in pSCANNER
11.7.3 UC Davis Betty Irene Moore School of Nursing’s Role in pSCANNER
11.8 Role of Nursing Science in and with PCORnet
11.8.1 Nursing Data
References
Chapter 12: Enhancing Data Access and Utilization: Federal Big Data Initiative and Relevance to H
12.1 The U.S. Department of Health and Human Services and the Health Data Initiative
12.1.1 Integrating Nursing Data into Big Data
12.2 Eliminating Health Disparities and Building Health Equity with Big Data
12.2.1 The Social Determinants of Health
12.2.2 Health Disparities and Health Equity
12.2.3 Using Big Data to Eliminate Disparities and Build Equity in Symptoms Management
Case Study 12.1: Clinical Practice Model (CPM) Framework Approach to Achieve Clinical Practice Int
12.1.1 Introduction
12.1.2 A Framework Approach
12.1.2.1 Coding CPM Framework and Models to Standardized Clinical Terminologies
12.1.2.2 Within Defined Limits (WDL) Assessments Mapped to Standardized Terminology
12.1.2.3 CPM Clinical Practice Guidelines Mapped to Standardized Terminology
12.1.3 CPG Pressure Ulcer-Risk For- Example
12.1.4 The Challenges of Utilizing and Sharing Big Data
12.1.5 Conclusion
References
Chapter 13: Big Data Impact on Transformation of Healthcare Systems
13.1 Introduction
13.2 Limitations of the Past
13.3 How Healthcare Systems Come Together Electronically
13.4 Big Data Emerging from Healthcare Systems
13.5 The Hope of Improving Health and Care Within Healthcare Systems Using Data
13.5.1 Rapid Dissemination of Evidence-Based Care
13.5.2 Integrating Individual Patient Care Data Across the Continuum
13.5.3 Integration to Manage Patient Populations
13.6 Challenges of Gleaning Information and Knowledge from the Data and Recommendations for Op
13.7 Conclusion
References
Chapter 14: State of the Science in Big Data Analytics
14.1 Advances in Predictive Modeling and Feature Selection for Big Data
14.1.1 Kernel-Based Transformation of the Data
14.1.2 Advances in Feature Selection
14.2 Advances in Causal Discovery with Big Data, Causal Feature Selection and Unified Predictive
14.3 Unified Predictive-Causal Modeling and Causal Feature Selection
14.3.1 Synopsis of Other Important Big Data Mining Advances
14.3.1.1 Advances in Integrated Analysis Protocols for Big Data
14.3.1.2 Advances in Big Data Methods for Unstructured Data
14.3.1.3 Polymodal and Multimodal Analysis Methods
14.3.1.4 Clustering
14.3.1.5 Bayesian Methods
14.3.1.6 Boosting
14.3.1.7 Decision Trees and Random Forests
14.3.1.8 Genetic Algorithms (GAs)
14.3.1.9 Artificial Neural Networks (ANNs) and Deep Learning
14.3.1.10 Network Science
14.3.1.11 Active Learning
14.3.1.12 Outlier Detection
14.3.1.13 Visualization
14.4 Conclusions
14.4.1 Achievements, Open Problems, Challenges in Big Data Mining Methods
References
Part IV: Looking at Today and the Near Future
Chapter 15: Big Data Analytics Using the VA’s ‘VINCI’ Database to Look at Delirium
15.1 Introduction
15.1.1 The Problem with Delirium
15.1.2 Big Data Can Help
15.1.3 VHA Data Resources
15.1.4 Case Study 1: Identifying Patients at Risk for Delirium
15.1.4.1 Methods
15.1.4.2 Results
15.1.4.3 Conclusion
15.1.5 Case Study 2: Improving Classification Using Natural Language Processing
15.1.5.1 Method
15.1.5.2 Results
15.1.5.3 Conclusion
15.1.6 Case Study 3: Building a Stewardship Program
15.1.6.1 Methods
15.1.6.2 Results
15.1.6.3 Conclusion
15.2 Overall Discussion
15.2.1 Quality of Data
15.2.2 Matching Data Analytics to the Question and Producing Actionable Information
15.2.3 Integrating the Patient’s Story
15.2.4 Overall Conclusion
References
Chapter 16: Leveraging the Power of Interprofessional EHR Data to Prevent Delirium: The Kaiser P
16.1 Introducing the Delirium Picture
16.2 Introduction
16.3 The Impact of Delirium
16.4 Discovering the Delirium Story Through Multiple Sources of Information
16.5 Accessing Data in the EHR
16.6 The KP Discovery Journey
16.7 Transforming Care with Actionable Information
16.8 An Interdisciplinary Approach to Delirium Prevention
16.9 Measuring Success of the Interdisciplinary Delirium Risk Score
16.10 Summary
References
Chapter 17: Mobilizing the Nursing Workforce with Data and Analytics at the Point of Care
17.1 Introduction
17.2 Background
17.3 Mobile Infrastructure
17.3.1 Mobile Device and App History
17.3.2 History of Mobile in Healthcare
17.4 Mobile Impact on Nurses’ Roles and Processes
17.5 Apps for Nurses: Education
17.6 Apps for Nurses: Practice
17.6.1 Primary Care
17.6.2 Acute Care
17.6.3 Home Care
17.6.4 Care Coordination
17.7 Apps for Patients
17.7.1 Patient Portals
17.8 The Value of Mobile with the Power of Analytics
17.8.1 Extend Healthcare Services
17.8.2 Patient Engagement
17.8.3 Decision Support
17.8.4 Insight through Analytics
17.9 Summary
References
Chapter 18: The Power of Disparate Data Sources for Answering Thorny Questions in Healthcare: Fou
18.1 Introduction
18.2 Nursing Informatics as a Valuable Resource and Analytics Team Member
18.3 The Knowledge Framework and NI
18.4 Conclusion
Case Study 18.1: Alarm Management: From Confusion to Information
18.1.1 Introduction
18.1.2 Testing New Technology
18.1.3 Data-Driven Monitor Management
18.1.4 Results
18.1.5 Conclusion
Case Study 18.2: Nursing Time in the Electronic Health Record: Perceptions Versus Reality
18.2.1 Introduction
18.2.2 Methods
� 18.2.3 Results
18.2.4 Conclusion
Case Study 18.3: Identifying Direct Nursing Cost Per Patient Episode in Acute Care—Merging Data
18.3.1 Introduction and Background
18.3.2 Definition of Direct Nursing Cost per Acute Care Episode
18.3.3 Data Sources and Data Management Plan
18.3.4 Architecture for File Merger
18.3.5 Construction of Outcome Variable
18.3.6 Data Analysis
18.3.7 Key Findings
18.3.8 Discussion
Case Study 18.4: Building a Learning Health System—Readmission Prevention
18.4.1 Introduction
18.4.2 Methods
18.4.3 Results
18.4.4 Discussion
18.4.5 Conclusion
References
Part V: A Call for Readiness
Chapter 19: What Big Data and Data Science Mean for Schools of Nursing and Academia
19.1 Why is Big Data Important for Academic Nursing?
19.2 Undergraduate Education
19.3 Master’s Education
19.4 Nursing Informatics Graduate Specialty
19.5 Doctorate in Nursing Practice (DNP)
19.6 PhD Education
19.7 Challenges Ahead
19.8 Curriculum Opportunities
19.9 Conclusion
Case Study 19.1: Informatics Certification and What’s New with Big Data
19.1.1 Introduction
19.1.2 AMIA’s Path Toward Establishing Advanced Health Informatics Certification
19.1.3 Advanced Health Informatics Certification (AHIC)
Acknowledgements
Case Study 19.2: Accreditation of Graduate Health Informatics Programs
19.2.1 Introduction
19.2.2 Accreditation Standards
19.2.3 Recommendations for Future Accreditation Requirements
19.2.4 Conclusion
References
Chapter 20: Quality Outcomes and Credentialing: Implication for Informatics and Big Data Science
20.1 Introduction
20.2 High-Quality Performance
20.3 Credentialing and Patient Outcomes
20.4 Conclusion
References
Chapter 21: Big Data Science and Doctoral Education in Nursing
21.1 Introduction
21.2 About Big Data and Nursing
21.2.1 Ubiquity of Big Data
21.2.2 Definitions
21.2.3 Nursing Interface with Big Data
21.3 Doctoral Education
21.3.1 Context
21.3.2 Framework
21.3.3 Big Data Knowledge, Skills, and Competencies
21.3.3.1 Common Doctoral Core: DNP and PhD
21.3.3.2 Practice-Focused Doctorate
21.3.3.3 Research-Focused Doctorate
21.3.3.4 Nurse Data Scientist
21.3.3.5 Resources for Implementation
21.4 Summary
References
Chapter 22: Global Society & Big Data: Here’s the Future We Can Get Ready For
22.1 Introduction: Are We Moving to a Global Society, Except for Healthcare?
22.1.1 Phase 1: Thinking Local, Acting Local—Healthcare in the Past and Today
22.1.2 Phase 2: Thinking Local, Acting Global—Cross-Border Care and Medical Tourism
22.1.3 Phase 3: Thinking Global, Acting Local—Global Healthcare Driven by Networks
22.1.4 Phase 4: Thinking Global, Acting Global—Discovering the Long Tail in Healthcare
22.2 From Local to Global: What Would It Take?
References
Chapter 23: Big-Data Enabled Nursing: Future Possibilities
23.1 Introduction
23.2 The Future of Big Data in Education: Implications for Faculty and Students
23.2.1 Demand for Data Scientists
23.2.2 Precision Education for Students
23.2.3 Faculty Role Changes
23.3 Conclusion
23.4 The Future of Partnerships in Generating Big Data Initiatives, Products, and Services
23.5 Big Data Through the Research Lens
23.5.1 Forces Affecting Big Data and Related Discoveries in Nursing and Health Care
23.5.2 Anticipating the Future with Big Data
23.5.3 Nursing’s Call to Action for Big Data and Data Science
23.6 Healthcare in 2020: Looking at Big Data Through the Clinical Executive’s Lens
23.6.1 Healthcare’s Journey into Big Data
23.6.2 Looking at Care Delivery in 2020
23.6.3 Population Health Managed Care—An Example from Bon Secours Medical Group (BSMG)
23.6.4 Looking at Near-Term Future Examples
23.6.4.1 Operations
23.6.4.2 Care Delivery
23.6.5 Looking Forward
23.6.6 Personalization of Care
23.7 Final Thoughts About the Future with Big Data
References

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