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E-raamat: Big Data-Enabled Nursing: Education, Research and Practice

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  • Formaat: EPUB+DRM
  • Sari: Health Informatics
  • Ilmumisaeg: 02-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319533001
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Historically, nursing, in all of its missions of research/scholarship, education and practice, has not had access to large patient databases. Nursing consequently adopted qualitative methodologies with small sample sizes, clinical trials and lab research. Historically, large data methods were limited to traditional biostatical analyses. 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. Health systems electronic health records (EHRs) have now matured to generate massive databases with longitudinal trending. 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 minin

g, including the expanse of data visualization capacities, are enabling sophisticated discovery. New opportunities for nursing and call for new skills in research methodologies are being further enabled by new partnerships spanning all sectors.

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 Da

ta 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.
Part I The New and Exciting World of "Big Data"
1 Why Big Data?: Why Nursing?
3(8)
Connie W. Delaney
Roy L. Simpson
1.1 Why Big Data?
4(2)
1.2 Why Big Data in Nursing?
6(2)
1.3 Summary
8(3)
References
8(3)
2 Big Data in Healthcare: A Wide Look at a Broad Subject
11(22)
Marisa L. Wilson
Charlotte A. Weaver
Paula M. Procter
Murielle S. Beene
2.1 Reaching the Tipping Point: Big Data and Healthcare
12(2)
2.2 Big Data and Analytics Enabling Innovation in Population Health
14(4)
2.2.1 Blending in the Social Determinants
17(1)
2.3 Big Data in Action
18(11)
2.3.1 The Department of Veterans Affairs
18(5)
2.3.2 A View from Home Health
23(2)
2.3.3 The Spine: A United Kingdom Big Data Endeavor
25(4)
2.4 Summary
29(4)
References
29(4)
3 A Big Data Primer
33(30)
Judith J. Warren
3.1 What Is Big Data?
33(4)
3.1.1 Datafication and Digitization
36(1)
3.1.2 Resources for Evaluating Big Data Technology
36(1)
3.2 The V's: Volume, Variety, Velocity
37(3)
3.2.1 Volume
37(2)
3.2.2 Variety
39(1)
3.2.3 Velocity
39(1)
3.3 Data Science
40(2)
3.3.1 What Is Data Science?
40(1)
3.3.2 The Data Science Process
40(2)
3.4 Visualizing the Data
42(1)
3.5 Big Data Is a Team Sport
43(2)
3.6 Conclusion
45(1)
Case Study 3.1 Big Data Resources---A Learning Module
46(17)
Judith J. Warren
E. La Verne Manos
3.1.1 Introduction
46(1)
3.1.2 Resources for Big Data
47(3)
3.1.3 Resources for Data Science
50(2)
3.1.4 Resources for Data Visualization
52(1)
3.1.5 Organizations of Interest
53(2)
3.1.6 Assessment of Competencies
55(1)
3.1.7 Learning Activities
56(1)
3.1.8 Guidance for Learners and Faculty Using the Module
57(1)
References
57(6)
Part II Technologies and Science of Big Data
4 A Closer Look at Enabling Technologies and Knowledge Value
63(16)
Thomas R. Clancy
4.1 Introduction
64(1)
4.2 Emerging Roles and the Technology Enabling Them
65(3)
4.3 A Closer Look at Technology
68(6)
4.3.1 Handheld Ultrasound
70(1)
4.3.2 Point of Care Lab Testing
70(1)
4.3.3 The Quantified Self Movement
71(1)
4.3.4 Sleep Monitors
72(1)
4.3.5 Activity Monitors
72(1)
4.3.6 Data Mash-Ups
73(1)
4.3.7 Symptom Checkers
73(1)
4.3.8 Augmented Cognition
74(1)
4.4 Big Data Science and the Evolving Role of Nurses
74(2)
4.5 Conclusion
76(3)
References
77(2)
5 Big Data in Healthcare: New Methods of Analysis
79(24)
Sarah N. Musy
Michael Simon
5.1 Introduction
80(1)
5.2 Sources of Big Data
81(2)
5.3 Big Data Analytics
83(3)
5.3.1 Data Mining
83(1)
5.3.2 Text Mining
84(1)
5.3.3 Predictive Modelling
85(1)
5.3.4 Machine Learning
85(1)
5.4 Big Data Applications in Nursing
86(4)
5.5 Challenges of Big Data
90(1)
5.6 Conclusions
91(4)
References
91(4)
Case Study 5.1 Value-Based Nursing Care Model Development
95(8)
John M. Welton
Ellen Harper
5.1.1 Value-Based Nursing Care and Big Data
96(2)
5.1.2 The Cost of Nursing Care
98(2)
5.1.3 Summary
100(1)
References
100(3)
6 Generating the Data for Analyzing the Effects of Interprofessional Teams for Improving Triple Aim Outcomes
103(12)
May Nawal Lutfiyya
Teresa Schicker
Amy Jarabek
Judith Pechacek
Barbara Brandt
Frank Cerra
6.1 Introduction
104(1)
6.2 Raison D'etre for the NCDR
105(8)
6.2.1 Characteristics of the NCDR
106(1)
6.2.2 Data Volume
107(1)
6.2.3 Data Velocity
108(1)
6.2.4 Data Value
108(1)
6.2.5 Ecosystem of the NCDR
109(1)
6.2.6 Infrastructure
110(3)
6.3 Conclusions
113(2)
References
113(2)
7 Wrestling with Big Data: How Nurse Leaders Can Engage
115(24)
Jane Englebright
Edmund Jackson
7.1 Introduction
115(1)
7.2 Denning Big Data and Data Science
116(1)
7.3 Nursing Leader Accountabilities and Challenges
116(1)
7.4 Systems Interoperability
117(1)
7.5 Non-Standardization
118(1)
7.6 The Invisibility of Nursing
118(1)
7.7 A Common Data Repository Across the System
119(1)
7.8 The Value of Big Data for Nurse Leaders
119(1)
7.9 The Journey to Sharable and Comparable Data in Nursing
120(3)
7.10 Gaining Insight from Data in Real Time
123(1)
7.11 Strategies for Moving Forward
123(1)
7.12 Instilling a Data-Driven Culture Through Team Science
124(1)
7.13 Putting It All Together: An Example
125(2)
7.13.1 Step 1: Diagnostic Analytics
125(1)
7.13.2 Step 2: Diagnostic Analytics
126(1)
7.13.3 Step 3: Predictive Analytics
126(1)
7.13.4 Step 4: Prescriptive Analytics
126(1)
7.14 Conclusions
127(2)
References
127(2)
Case Study 7.1 Improving Nursing Care Through the Trinity Health System Data Warehouse
129(10)
Nora Triola
Miriam Halimi
Melanie Dreher
7.1.1 Introduction
129(1)
7.1.2 Trinity Health
130(2)
7.1.3 Case Studies
132(4)
7.1.4 Conclusion
136(1)
Acknowledgements
136(1)
References
137(2)
8 Inclusion of Flowsheets from Electronic Health Records to Extend Data for Clinical and Translational Science Awards (CTSA) Research
139(18)
Bonnie L. Westra
Beverly Christie
Grace Gao
Steven G. Johnson
Lisiane Pruinelli
Anne LaFlamme
Jung In Park
Suzan G. Sherman
Piper A. Ranallo
Stuart Speedie
Connie W. Delaney
8.1 Introduction
140(1)
8.2 CTSAs to Support Big Data Science
140(2)
8.3 Clinical Data Repositories (CDRs)
142(3)
8.3.1 CDR Structure and Querying Data
143(1)
8.3.2 Standardizing Patient Data
144(1)
8.4 What Are Flowsheets?
145(5)
8.4.1 How Do Organizations Decide What to Record on Flowsheets?
146(1)
8.4.2 Strengths and Challenges of Flowsheet Data
147(1)
8.4.3 Example of Pressure Ulcer
148(2)
8.5 Standardization Essential for Big Data Science
150(4)
8.5.1 Nursing Information Models
151(1)
8.5.2 Example Nursing Information Models and Processes
151(1)
8.5.3 National Collaborative to Standardize Nursing Data
152(2)
8.6 Conclusion
154(3)
References
155(2)
9 Working in the New Big Data World: Academic/Corporate Partnership Model
157(26)
William Crown
Thomas R. Clancy
9.1 The Evolving Healthcare Data Landscape
158(1)
9.2 The Promise and Complexity of Working with Multiple Sources of Data
159(1)
9.3 Implications of Linked Claims and EHR Data for Nursing Studies
160(2)
9.4 Big Data Methods
162(2)
9.5 Beyond Research---Accelerating Clinical/Policy Translation and Innovation
164(1)
9.6 Innovation and Management of Intellectual Property in Academic/Corporate Partnerships
165(3)
9.7 The Ongoing Debate About the Merits of RCTs Versus Observational Studies
168(1)
9.8 Conclusions
169(3)
References
170(2)
Case Study 9.1 Academic/Corporate Partnerships: Development of a Model to Predict Adverse Events in Patients Prescribed Statins Using the OptumLabs Data Warehouse
172(11)
Chih-Lin Chi
Jin Wang
9.1.1 Introduction: Research Objective
172(2)
9.1.2 Resources Needed for Big-Data Analysis in the OptumLabs Project
174(3)
9.1.3 Research Process
177(2)
9.1.4 Conclusion
179(1)
References
179(4)
Part III Revolution of Knowledge Discovery, Dissemination, Translation Through Data Science
10 Data Science: Transformation of Research and Scholarship
183(28)
Lynda R. Hardy
Philip E. Bourne
10.1 Introduction to Nursing Research
184(4)
10.1.1 Big Data and Nursing
185(2)
10.1.2 Nursing and Data
187(1)
10.2 The New World of Data Science
188(1)
10.3 The Impact of Data Proliferation on Scholarship
189(2)
10.4 Initiatives Supporting Data Science and Research
191(4)
10.4.1 National Institutes of Health
192(1)
10.4.2 National Science Foundation
193(1)
10.4.3 U.S. Department of Energy
194(1)
10.4.4 U.S. Department of Defense
194(1)
10.5 Summary
195(2)
References
195(2)
Case Study 10.1 Complexity of Common Disease and Big Data
197(14)
Sandra Daack-Hirsch
Lisa Shah
10.1.1 Type 2 Diabetes (T2D) as a Significant Health Problem
197(1)
10.1.2 Factors Contributing to T2D
198(2)
10.1.3 Epigenetics
200(3)
10.1.4 Current Initiatives to Leverage the Power of Big Data for Common Disease
203(2)
10.1.5 Scope and Practice of Genetics/Genomics Nursing
205(1)
10.1.6 Conclusion
206(1)
References
206(5)
11 Answering Research Questions with National Clinical Research Networks
211(16)
Katherine K. Kim
Satish M. Mahajan
Julie A. Miller
Joe V. Selby
11.1 The Vision
212(1)
11.2 Electronic Data
212(1)
11.3 Distributed Data Networks
213(1)
11.3.1 The Mini-Sentinel Distributed Database
214(1)
11.4 PCORnet, the National Patient-Centered Clinical Research Network
214(3)
11.4.1 The Partner Networks
215(1)
11.4.2 Governance
216(1)
11.4.3 Data Handling
216(1)
11.5 Current State
217(1)
11.6 Future Plans
218(1)
11.7 PCORnet in Practice: pSCANNER
218(5)
11.7.1 Stakeholder Engagement
219(2)
11.7.2 Research in pSCANNER
221(2)
11.7.3 UC Davis Betty Irene Moore School of Nursing's Role in pSCANNER
223(1)
11.8 Role of Nursing Science in and with PCORnet
223(4)
11.8.1 Nursing Data
223(2)
References
225(2)
12 Enhancing Data Access and Utilization: Federal Big Data Initiative and Relevance to Health Disparities Research
227(26)
Rosaly Correa-de-Araujo
12.1 The U.S. Department of Health and Human Services and the Health Data Initiative
229(7)
12.1.1 Integrating Nursing Data into Big Data
235(1)
12.2 Eliminating Health Disparities and Building Health Equity with Big Data
236(8)
12.2.1 The Social Determinants of Health
236(2)
12.2.2 Health Disparities and Health Equity
238(2)
12.2.3 Using Big Data to Eliminate Disparities and Build Equity in Symptoms Management
240(2)
References
242(2)
Case Study 12.1 Clinical Practice Model (CPM) Framework Approach to Achieve Clinical Practice Interoperability and Big Data Comparative Analysis
244(9)
Michelle Troseth
Donna Mayo
Robert Nieves
Stephanie Lambrecht
12.1.1 Introduction
244(1)
12.1.2 A Framework Approach
245(4)
12.1.3 CPG Pressure Ulcer-Risk For-Example
249(1)
12.1.4 The Challenges of Utilizing and Sharing Big Data
249(1)
12.1.5 Conclusion
250(1)
References
251(2)
13 Big Data Impact on Transformation of Healthcare Systems
253(12)
Gay L. Landstrom
13.1 Introduction
253(1)
13.2 Limitations of the Past
254(1)
13.3 How Healthcare Systems Come Together Electronically
255(1)
13.4 Big Data Emerging from Healthcare Systems
256(1)
13.5 The Hope of Improving Health and Care Within Healthcare Systems Using Data
257(4)
13.5.1 Rapid Dissemination of Evidence-Based Care
257(2)
13.5.2 Integrating Individual Patient Care Data Across the Continuum
259(1)
13.5.3 Integration to Manage Patient Populations
260(1)
13.6 Challenges of Gleaning Information and Knowledge from the Data and Recommendations for Optimizing Data Within HCS
261(1)
13.7 Conclusion
262(3)
References
262(3)
14 State of the Science in Big Data Analytics
265(22)
C.F. Aliferis
14.1 Advances in Predictive Modeling and Feature Selection for Big Data
265(7)
14.1.1 Kernel-Based Transformation of the Data
269(1)
14.1.2 Advances in Feature Selection
270(2)
14.2 Advances in Causal Discovery with Big Data, Causal Feature Selection and Unified Predictive and Causal Analysis
272(1)
14.3 Unified Predictive-Causal Modeling and Causal Feature Selection
273(8)
14.3.1 Synopsis of Other Important Big Data Mining Advances
275(6)
14.4 Conclusions
281(6)
14.4.1 Achievements, Open Problems, Challenges in Big Data Mining Methods
281(1)
References
281(6)
Part IV Looking at Today and the Near Future
15 Big Data Analytics Using the VA's `VINCI' Database to Look at Delirium
287(14)
Charlene Weir
Joanne LaFluer
Bryan Gibson
Qing Zeng
15.1 Introduction
288(8)
15.1.1 The Problem with Delirium
288(1)
15.1.2 Big Data Can Help
289(1)
15.1.3 VHA Data Resources
290(1)
15.1.4 Case Study I: Identifying Patients at Risk for Delirium
291(1)
15.1.5 Case Study 2: Improving Classification Using Natural Language Processing
292(3)
15.1.6 Case Study 3: Building a Stewardship Program
295(1)
15.2 Overall Discussion
296(5)
15.2.1 Quality of Data
296(1)
15.2.2 Matching Data Analytics to the Question and Producing Actionable Information
297(1)
15.2.3 Integrating the Patient's Story
297(1)
15.2.4 Overall Conclusion
298(1)
References
298(3)
16 Leveraging the Power of Interprofessional EHR Data to Prevent Delirium: The Kaiser Permanente Story
301(12)
Rayne Soriano
Marilyn Chow
Ann O'Brien
16.1 Introducing the Delirium Picture
301(1)
16.2 Introduction
302(1)
16.3 The Impact of Delirium
303(1)
16.4 Discovering the Delirium Story Through Multiple Sources of Information
304(1)
16.5 Accessing Data in the EHR
305(1)
16.6 The KP Discovery Journey
306(1)
16.7 Transforming Care with Actionable Information
307(1)
16.8 An Interdisciplinary Approach to Delirium Prevention
307(2)
16.9 Measuring Success of the Interdisciplinary Delirium Risk Score
309(1)
16.10 Summary
310(3)
References
310(3)
17 Mobilizing the Nursing Workforce with Data and Analytics at the Point of Care
313(18)
Judy Murphy
Amberly Barry
17.1 Introduction
313(1)
17.2 Background
314(2)
17.3 Mobile Infrastructure
316(1)
17.3.1 Mobile Device and App History
316(1)
17.3.2 History of Mobile in Healthcare
316(1)
17.4 Mobile Impact on Nurses' Roles and Processes
317(1)
17.5 Apps for Nurses: Education
318(1)
17.6 Apps for Nurses: Practice
319(5)
17.6.1 Primary Care
319(2)
17.6.2 Acute Care
321(1)
17.6.3 Home Care
322(1)
17.6.4 Care Coordination
323(1)
17.7 Apps for Patients
324(2)
17.7.1 Patient Portals
325(1)
17.8 The Value of Mobile with the Power of Analytics
326(2)
17.8.1 Extend Healthcare Services
326(1)
17.8.2 Patient Engagement
327(1)
17.8.3 Decision Support
327(1)
17.8.4 Insight through Analytics
327(1)
17.9 Summary
328(3)
References
328(3)
18 The Power of Disparate Data Sources for Answering Thorny Questions in Healthcare: Four Case Studies
331(42)
Ellen M. Harper
Douglas McNair
18.1 Introduction
332(2)
18.2 Nursing Informatics as a Valuable Resource and Analytics Team Member
334(1)
18.3 The Knowledge Framework and NI
335(6)
18.4 Conclusion
341(4)
References
342(3)
Case Study 18.1 Alarm Management: From Confusion to Information
345(7)
Kevin Smith
Vicki Snavely
18.1.1 Introduction
345(1)
18.1.2 Testing New Technology
345(1)
18.1.3 Data-Driven Monitor Management
346(2)
18.1.4 Results
348(3)
18.1.5 Conclusion
351(1)
References
351(1)
Case Study 18.2 Nursing Time in the Electronic Health Record: Perceptions Versus Reality
352(7)
April Giard
Darinda Sutton
18.2.1 Introduction
352(1)
18.2.2 Methods
353(1)
18.2.3 Results
354(3)
18.2.4 Conclusion
357(1)
References
357(2)
Case Study 18.3 Identifying Direct Nursing Cost Per Patient Episode in Acute Care---Merging Data from Multiple Sources
359(5)
Peggy Jenkins
18.3.1 Introduction and Background
359(1)
18.3.2 Definition of Direct Nursing Cost per Acute Care Episode
359(1)
18.3.3 Data Sources and Data Management Plan
360(1)
18.3.4 Architecture for File Merger
360(1)
18.3.5 Construction of Outcome Variable
360(2)
18.3.6 Data Analysis
362(1)
18.3.7 Key Findings
362(1)
18.3.8 Discussion
363(1)
References
363(1)
Case Study 18.4 Building a Learning Health System---Readmission Prevention
364(9)
Marlene A. Bober
Ellen M. Harper
18.4.1 Introduction
364(1)
18.4.2 Methods
365(1)
18.4.3 Results
366(1)
18.4.4 Discussion
366(2)
18.4.5 Conclusion
368(1)
References
369(4)
Part V A Call for Readiness
19 What Big Data and Data Science Mean for Schools of Nursing and Academia
373(26)
Linda A. McCauley
Connie W. Delaney
19.1 Why is Big Data Important for Academic Nursing?
374(1)
19.2 Undergraduate Education
375(1)
19.3 Master's Education
376(1)
19.4 Nursing Informatics Graduate Specialty
377(1)
19.5 Doctorate in Nursing Practice (DNP)
378(1)
19.6 PhD Education
378(1)
19.7 Challenges Ahead
379(2)
19.8 Curriculum Opportunities
381(2)
19.9 Conclusion
383(2)
References
383(2)
Case Study 19.1 Informatics Certification and What's New with Big Data
385(6)
Cynthia Gadd
Connie White Delaney
19.1.1 Introduction
385(1)
19.1.2 AMIA's Path Toward Establishing Advanced Health Informatics Certification
386(2)
19.1.3 Advanced Health Informatics Certification (AHIC)
388(1)
Acknowledgements
389(1)
References
390(1)
Case Study 19.2 Accreditation of Graduate Health Informatics Programs
391(8)
Judith J. Warren
19.2.1 Introduction
391(3)
19.2.2 Accreditation Standards
394(2)
19.2.3 Recommendations for Future Accreditation Requirements
396(1)
19.2.4 Conclusion
397(1)
References
397(2)
20 Quality Outcomes and Credentialing: Implication for Informatics and Big Data Science
399(8)
Bobbie Berkowitz
20.1 Introduction
399(1)
20.2 High-Quality Performance
400(2)
20.3 Credentialing and Patient Outcomes
402(3)
20.4 Conclusion
405(2)
References
405(2)
21 Big Data Science and Doctoral Education in Nursing
407(20)
Patricia Eckardt
Susan J. Henly
21.1 Introduction
407(1)
21.2 About Big Data and Nursing
408(2)
21.2.1 Ubiquity of Big Data
408(1)
21.2.2 Definitions
409(1)
21.2.3 Nursing Interface with Big Data
409(1)
21.3 Doctoral Education
410(13)
21.3.1 Context
410(1)
21.3.2 Framework
411(1)
21.3.3 Big Data Knowledge, Skills, and Competencies
411(12)
21.4 Summary
423(4)
References
423(4)
22 Global Society & Big Data: Here's the Future We Can Get Ready For
427(14)
Walter Sermeus
22.1 Introduction: Are We Moving to a Global Society, Except for Healthcare?
427(11)
22.1.1 Phase 1: Thinking Local, Acting Local---Healthcare in the Past and Today
428(2)
22.1.2 Phase 2: Thinking Local, Acting Global---Cross-Border Care and Medical Tourism
430(1)
22.1.3 Phase 3: Thinking Global, Acting Local---Global Healthcare Driven by Networks
431(3)
22.1.4 Phase 4: Thinking Global, Acting Global---Discovering the Long Tail in Healthcare
434(4)
22.2 From Local to Global: What Would It Take?
438(3)
References
439(2)
23 Big-Data Enabled Nursing: Future Possibilities
441(24)
Judith J. Warren
Thomas R. Clancy
Connie W. Delaney
Charlotte A. Weaver
23.1 Introduction
442(1)
23.2 The Future of Big Data in Education: Implications for Faculty and Students
442(5)
23.2.1 Demand for Data Scientists
443(1)
23.2.2 Precision Education for Students
444(2)
23.2.3 Faculty Role Changes
446(1)
23.3 Conclusion
447(1)
23.4 The Future of Partnerships in Generating Big Data Initiatives, Products, and Services
447(3)
23.5 Big Data Through the Research Lens
450(4)
23.5.1 Forces Affecting Big Data and Related Discoveries in Nursing and Health Care
450(3)
23.5.2 Anticipating the Future with Big Data
453(1)
23.5.3 Nursing's Call to Action for Big Data and Data Science
454(1)
23.6 Healthcare in 2020: Looking at Big Data Through the Clinical Executive's Lens
454(7)
23.6.1 Healthcare's Journey into Big Data
456(1)
23.6.2 Looking at Care Delivery in 2020
457(1)
23.6.3 Population Health Managed Care---An Example from Bon Secours Medical Group (BSMG)
458(1)
23.6.4 Looking at Near-Term Future Examples
458(1)
23.6.5 Looking Forward
459(1)
23.6.6 Personalization of Care
460(1)
23.7 Final Thoughts About the Future with Big Data
461(4)
References
461(4)
Glossary 465(8)
Index 473
Five editors are national and international experts in nursing & health informatics, representing  all sectors including  health systems,  corporate and vendors, academia,  policy, and professional associations.  All invited authors are recognized experts.
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