Healthcare Information Management Systems: Cases, Strategies, and Solutions

Foreword
Acknowledgments
Contents
Contributors
Part I: The Current State
1: Estimating the United States’ Cost of Healthcare Information Technology
1.1 Introduction
1.1.1 The Myriad Benefits of HIT
1.1.2 A Frequent Misunderstanding
1.1.3 “Carrot and Stick”
1.1.4 The “Real” Costs
1.1.5 Our Scope and Plan
1.2 Our Task
1.3 Who Buys and Uses HIT?
1.3.1 Inclusions
1.3.2 Exclusions
1.4 Cost Estimates: Method
1.4.1 Information Sources
1.4.2 Responses from Vendors and Limitations
1.5 Software Cost Estimates: Findings
1.5.1 Introductory Notes
1.5.2 Government Systems
1.5.2.1 Emergency Medical Services (not part of Fire Departments)
1.5.2.2 Department of Defense: Cerner-Leidos-Accenture EHR Implementation
1.5.2.3 US Indian Health Service (IHS)
1.5.2.4 Prison and Jail EHR Software
1.5.2.5 State and County Health Departments
1.5.2.6 The Veterans Health Administration (VHA) Cerner EHR Implementation
1.5.3 Home Care, Long Term Care, and Elderly Care
1.5.3.1 Adult Day Care
1.5.3.2 Home Health Care (HHC)
1.5.3.3 Hospices
1.5.3.4 Remote Patient Monitoring (RPM)
1.5.3.5 Skilled Nursing Facilities (SNFs)
Base Assumptions and Estimates
1.5.3.6 Visiting Nurses
1.5.4 Clinical and Other Services (Usually Not in Hospitals)
1.5.4.1 Acupuncturists
1.5.4.2 Chiropractic
1.5.4.3 Clinical Trials Software
1.5.4.4 Dental Care
1.5.4.5 Dialysis
1.5.4.6 Medical Practices’ EHRs (MD, DO, some NPs)
1.5.4.7 Occupational Therapy
1.5.4.8 Optometry
1.5.4.9 Pharmacies--Chain and Independent
1.5.4.10 Pharmacy Benefit Manager (PBM) Software
1.5.4.11 Physical Therapy (PT)
1.5.4.12 Podiatry
1.5.4.13 Telehealth
1.5.5 Hospitals and Usually Linked Services
1.5.5.1 Hospital EHR Costs
Data Sources for Hospital Software Costs
Cost Estimates
HIT Operating Costs
HIT Purchase and Implementation Costs (One Time Costs Amortized Over 5 Years)
Licensing Costs (Including Maintenance, Updates and Service)
1.5.5.2 ICU Monitoring: Tele-Monitoring for ICU Beds
1.5.5.3 Medical Imaging
1.5.5.4 Medical Image Management--Picture Archiving & Communications Systems (PACS)
1.5.5.5 Medical Laboratory Management Systems (LIMS)
1.5.5.6 Medical Social Workers
1.5.6 Cybersecurity Risk Insurance Premiums
1.6 The Final Tally and Estimate
1.7 Conclusion
References
2: Innovating Payment Models for High-Value Healthcare
2.1 Introduction
2.2 Toward an Aligned and Comprehensive Healthcare Payment System
2.2.1 History
2.2.2 Innovation and Payment
2.3 Units of Care, Payment, and Accountability
2.3.1 Total Cost, Price and Quantity
2.3.2 Episode-Based Accountability
2.4 Measuring the Value of Care
2.4.1 Healthy Life Expectancy (HALE) Score
2.4.2 Quality Measures
2.4.3 Relative Health and Cost
2.5 Attributing Performance Outcomes to Clinicians, Teams, and Risk-Bearing Entities
2.5.1 Responsibility and Accountability
2.5.2 Identifying Roles for Attribution
2.6 Paying for Higher Value of Care
2.7 Conclusions and Outlook
References
3: Leadership and Change
3.1 Introduction
3.2 Leadership and Change
3.3 Building Trust
3.3.1 Psychological Safety: A Coaching Mindset
3.3.2 Post-Traumatic Growth Can Lead to Innovation and Empathy (Be Yoda)
3.4 Leadership and Teams
3.5 Leadership for Organizational Resiliency
Appendix 1: Leadership Changes
Appendix 2: Hierarchical Versus Heterarchical Organization
Health IT and Leadership Challenges
References
4: Promoting Informatics Workforce Development Through Global Initiatives
4.1 Introduction
4.2 HIMSS TIGER Initiative
4.2.1 TIGER Global Informatics Definitions
4.2.2 Global Health Informatics Guide
4.2.3 TIGER Scholars Informatics Internship
4.2.4 TIGER International Competency Synthesis Project
4.2.5 eHealth Competency Development: Synergy of Projects
4.2.6 TIGER Virtual Learning Environment (VLE)
4.2.7 TIGER International Task Force
4.3 HIMSS Nursing Informatics Community
4.3.1 HIMSS Nursing Informatics Workforce Survey
4.3.2 Chief Nursing Informatics Officer Job Description Document
4.4 HIMSS-SIIM Enterprise Imaging Community
4.4.1 HIMSS-SIIM Enterprise Imaging Workgroups
4.4.2 HIMSS-SIIM Digital Imaging Adoption Model
4.5 Fostering Global Alliances and Partnerships
4.5.1 TIGER’s Partnerships with Academic Institutions
4.5.2 HIMSS Europe Nursing Informatics Community
4.6 Informatics and COVID-19
4.7 Conclusion
4.8 Links to Online Material
References
5: Preparing Clinicians and Patients for the Future of Virtual Medicine and Telehealth
5.1 Introduction
5.2 Background
5.3 Security and Privacy
5.4 Telemedicine Education
5.5 Telemedicine Models
5.6 Consent Procedures
5.7 Reimbursement
5.8 Home-Based Telemedicine
5.9 Ambulatory-Based Telehealth
5.10 Technology
5.11 Orientation for the Visit
5.12 Performing the Visit
5.13 Physical Examination
5.14 Other Utilization of Telehealth
5.15 The Future of Telemedicine
References
6: Privacy and Security
6.1 Introduction
6.2 Privacy
6.3 HIPAA and FIPP
6.3.1 Notice/Awareness
6.3.2 Consent
6.3.3 Access/Participation
6.3.4 Integrity/Security
6.3.5 Enforcement/Redress
6.4 Information Security
6.5 Characteristics of Information Security
6.5.1 Confidentiality
6.5.2 Integrity
6.5.3 Availability
6.5.4 Accountability
6.6 NIST
6.7 HIPAA Security Rule
6.8 Risk Assessment and Management
6.8.1 Organization and System Purpose
6.8.2 Threats
6.8.3 Vulnerabilities
6.8.4 Calculation of Risk: Likelihood * Damage
6.8.5 Risk Mitigation: Reducing Likelihood
6.8.6 Risk Mitigation: Limiting Damage
6.8.7 Cost Effectiveness of Controls and Priorities
6.9 Security Controls: Major Concepts
6.9.1 Access Control
6.9.2 Physical Controls
6.9.3 Encryption
6.9.4 Network Security
6.9.5 Firewalls and Intrusion Detection/Prevention
6.9.6 Device and User Security
6.9.7 Attack Methodologies
6.10 Conclusion
6.11 Web Resources
References
7: Interoperability: Current Considerations
7.1 Introduction
7.2 Definition
7.3 Current Landscape
7.3.1 Privacy
7.3.1.1 Define Policies for Identifying, Labeling and Managing Data for Disclosure and Consent
7.3.1.2 Document Patient Consent Directives
7.3.1.3 Label Data in Accordance with the Policies and Consent Directives
7.3.1.4 Share Patient Consent Directives
7.3.1.5 Manage and Re-disclose Received Data
7.3.2 Transactions
7.3.3 Document Exchange
7.3.4 Services
7.3.5 USCDI/EHI/DRS
7.3.6 Information Blocking
7.3.7 Nationwide Networks
7.3.8 Coordination of Care
7.3.9 Patient Engagement
7.3.10 Public Health
7.3.10.1 Alternative Methods
7.3.10.2 Accelerating Adoption of Electronic Case Reporting
7.4 Outlook
7.4.1 Privacy
7.4.2 Cross-Organization Workflow Coordination
7.4.3 USCDI/EHI/DRS
7.4.4 Nationwide Networks
7.4.5 Coordination of Care
7.4.5.1 Source
7.4.5.2 Receiver
7.4.6 Patient Engagement
7.4.7 Public Health
7.4.8 Measuring Impact
8: Health Information Exchange
8.1 Intro: Why Health Information Should Be Exchanged
8.2 Common HIE Services
8.2.1 Data at the Point of Care
8.2.2 Data for Care Coordination
8.2.2.1 Notifications
8.2.2.2 Delivery System Coordination
8.2.2.3 Analysis
8.2.3 Public Health Support
8.2.3.1 Surveillance
8.2.3.2 Case Management
8.2.3.3 Sending Data Back to Clinicians
8.2.4 Data Combination, Mastering, and Normalization
8.2.4.1 Operational Reporting
8.2.4.2 Research Analysis
8.2.5 Quality Measurement
8.3 Technical Approaches
8.3.1 Distributed Architecture
8.3.2 Centralized Architecture
8.3.3 Health Record Banks
8.4 Organization
8.4.1 Non-profit HIEs or RHIOs
8.4.2 Vendor Driven
8.4.3 National Networks
8.4.4 For-profit Networks
8.4.5 Case Study: CRISP Maryland’s HIE
8.5 Current Developments
8.5.1 FHIR Specifications for Healthcare Data Exchange
8.5.2 Information Blocking Regulations
8.5.3 Need for Public Health Infrastructure: Health Data Utilities
8.5.4 Social Determinants of Health
8.6 Closing
References
Part II: Innovations and Trends
9: Telemedicine: Its Past, Present and Future
9.1 Introduction
9.1.1 Brief History of Telemedicine
9.2 Telemedicine and Telehealth Before the 2020 COVID-19 Pandemic
9.2.1 Provider-to-Provider Telemedicine Services
9.2.2 Direct-to-Consumer Telehealth Services
9.3 Telemedicine and Telehealth During the 2020 COVID-19 Pandemic
9.3.1 The Challenge to Adopt and Scale Telehealth During “Fog of War”
9.4 Telehealth After the 2020 COVID-19 Pandemic
9.4.1 Emergence of Telehealth Programs: Convergence of Telehealth Projects
9.4.2 Critical Components in Telehealth Program Design
9.5 Future Directions and Innovation in Telehealth in the Future
10: The Telehealth Challenge During COVID-19 Emergency Preparedness and Response
10.1 The Unique COVID-19 Challenges for Health IT
10.2 The Scope and Vital Role of HIT During COVID-19 Response
10.3 The Telehealth Challenge
10.4 COVID-19 as the Catalyst for Rapid Telehealth Adoption and Integration
10.5 Overall Impact of COVID-19 Telehealth Initiatives
10.6 HIPAA Changes with Telehealth During the Pandemic
References
11: Information Technology and Operational Issues for Emergency Preparedness and Response
11.1 IT Operations
11.2 Key Concerns
11.3 Key Lessons for Emergency Preparedness Stemming from the Recent Pandemic
11.4 Emergency Management Process
11.5 Emergency Management Is Framed by Four Phases [5, 9, 10]
11.6 Developing an Emergency Preparedness Plan for the Organization
11.7 Who Is an Emergency Manager? Does the Organization Need One?
11.8 Community Emergency Response Teams
11.9 Special Situations
11.9.1 Terrorism
11.9.2 Bioterrorism
11.9.3 Pandemics (as has Become All Too Clear with Covid-19)
11.10 Preparing for a Pandemic
11.11 Cybersecurity Threats
11.12 Collaborative Emergency Management
11.13 Examples of Collaboration in Emergency Management
11.14 Vaccine Distribution
11.15 Other Resources
References
12: Data Use in Public Health
12.1 Introduction
12.1.1 Types and Sources of Data Used in Public Health
12.1.1.1 Vital Statistics
12.1.1.2 Administrative Data
12.1.1.3 Census
12.1.1.4 Surveys
12.1.1.5 Surveillance Systems
Active Surveillance
Disease-Specific Surveillance
Demographic and Health Surveillance System
Cohort Studies
Passive Surveillance
Disease Notification
Disease Registries
12.1.1.6 Public Health Research
12.1.2 Translating and Linking Public Health Data and Evidence to Public Health Action
12.1.2.1 What Is Knowledge Translation?
12.1.2.2 Engaging in Knowledge Translation
12.1.2.3 Institutionalizing Knowledge Translation
12.1.3 Application of Big Data to Healthcare and Public Health
12.2 Conclusions and Outlook
12.3 Links to Online Materials (Table 12.3)
Appendix: Answers and Explanations to Review Questions
References
13: Patient Safety and Health Information Technology
13.1 Introduction
13.1.1 Patient Safety and Health Information Technology
13.1.2 Patient Safety at Johns Hopkins
13.1.3 Health IT Safety at Hopkins
13.2 Case Study: CancelRx
13.2.1 Phase 1: Proactive Risk Assessment
13.2.2 Phase 2: Pilot Implementation
13.2.3 Phase 3: Expansion Across the Health System and a Human Factors Analysis
13.2.4 Future Steps
13.3 Conclusion
References
14: Digital Health in Chronic Care and Self-Management
14.1 Introduction
14.2 Types of Digital Health Technologies
14.3 Diving Deep: Software as a Medical Device and Digital Therapeutics
14.3.1 Software as a Medical Device
14.3.2 Digital Therapeutics: A New Category of Medicine
14.3.3 Navigating Infrastructures for Digital Health Development
14.4 The Role of Digital Health in Chronic Disease
14.4.1 Goals of Chronic Disease Management
14.4.1.1 Empowering Individual Self-Management
14.4.1.2 Optimizing (Evidence-Based) Treatment
14.4.1.3 Improving Outcomes
14.4.2 Mapping Data for Digital Health
14.5 Case Study: Welldoc—Leveraging Technology to Transform Diabetes Self-Care
14.5.1 Diabetes as a Clinical Model for Chronic Disease Self-Management
14.5.2 Digital Health Solution Development and Objectives
14.5.3 The Welldoc Digital Health Ecosystem
14.5.4 The User Experience with Welldoc
14.5.5 The Provider/Care Team Experience with the Welldoc Platform
14.5.6 Digital Health Integration into Practice
14.5.7 Digital Solution Evolution
14.6 Moving Ahead
References
15: Algorithmic Fairness and AI Justice in Addressing Health Equity
15.1 Introduction
15.2 Algorithmic Bias
15.3 Definition of Bias
15.4 Sources of Bias
15.5 Metrics for Bias and Fairness Assessment in AI Solutions
15.5.1 Individual vs. Group
15.5.2 Approaches to Achieving Group Fairness
15.6 Bias Mitigation Strategies
15.6.1 Pre-Processing
15.6.2 In-Processing
15.6.3 Post-Processing
15.7 Algorithmic Fairness in Action
15.7.1 Ada Lovelace Institute
15.7.2 AI Now Institute
15.7.3 Algorithmic Justice League
15.7.4 Data and Society
15.7.5 Montreal AI Ethics Institute
15.7.6 Partnership on AI
15.7.7 USC Center for Artificial Intelligence in Society
15.7.8 Upturn
15.7.9 Potential Limitations
15.8 Conclusions
References
16: Managing Clinical Data in Neurocritical Care
16.1 Introduction
16.2 Neurocritical Care (NCC)
16.3 The Neurocritical Care Unit (NCCU)
16.4 NCCU Stakeholders
16.4.1 The Neurocritical Patient
16.4.2 The Neuro-Intensivist
16.4.3 The Neurocritical Care (NCC) Nurse
16.4.4 Neurocritical Care (NCC) Team Members
16.5 Clinical and Data Workflow in the NCCU
16.5.1 Patient Throughput: Pre and Post NCCU Care
16.5.2 Clinical Data Flow in the NCCU
16.5.3 Multimodality Monitoring
16.5.4 Managing the NCCU Data Ecosystem
16.6 Case Study: The Johns Hopkins Hospital NCCU
16.6.1 Introduction
16.6.2 The Need for Multimodality NeuroMonitoring at Johns Hopkins
16.6.3 Building the NCCU Electronic Infrastructure
16.6.4 The NCCU Patient Bed
16.6.5 Problems Encountered and Approaches Taken
16.6.6 Timeline of JHH NCCU Multimodality System Implementation (Table 16.2)
16.6.7 Ongoing Development and Cost/Benefits
16.6.8 Advancing NCC Research
16.7 Conclusions
References
17: Data-Driven Disease Progression Modeling
17.1 Introduction
17.2 Taxonomy of DPM Solutions
17.3 Staging and Trajectory Estimation
17.3.1 Data
17.3.2 Methods
17.3.2.1 Trajectory Models
17.3.2.2 State-Based Models
17.3.3 Challenges and Future Directions
17.4 Predictive Modeling for Disease Progression
17.4.1 Data and Pre-processing
17.4.2 Methods
17.4.2.1 Classical and Deep Learning Models
17.4.2.2 Model Explanations
17.4.3 Challenges and Future Directions
17.5 Time to Event Modeling for Disease Progression
17.5.1 Data and Censoring
17.5.2 Methods
17.5.2.1 Parametric Distribution
17.5.2.2 Semi-Parametric Distribution
17.5.2.3 Non-parametric Distribution
17.5.2.4 Discrete Distribution Regression
17.5.2.5 Continuous Distribution Regression
17.5.2.6 Standard Regression Approaches
17.5.3 Challenges and Future Directions
17.6 Concluding Remarks
References
18: Virtual Health in Patient Care and Clinical Research
18.1 Introduction
18.2 Can We (or How Do We) Define Virtual Health?
18.3 Virtual Health in the Context of Clinical Care
18.4 The Virtual Care Visit
18.5 Virtual Care in Enterprise Healthcare
18.5.1 Hub and Spoke Model for Inpatient Consults for Telestroke and Psychiatry Example
18.5.2 Enterprise Patient-Provider Engagement Example
18.6 Virtual Care in Decentralized Clinical Trials
18.7 Virtual Health in the Context of Clinical Research
18.8 Diversity and Inclusion in Virtual Health and Clinical Trials
18.9 Examples of Virtual Elements in Clinical Research
18.10 A Maturity Model for Virtual Health
18.10.1 Know the Vision but Prioritize Starting the Journey
18.11 Approach to Defining Virtual Health Digital Transformation Solutions
18.12 Technology Considerations for Virtual Health
18.12.1 In the Context of Enterprise Healthcare
18.12.2 In the Context of Clinical Care
18.12.2.1 Patient Clinical Trial Participation Considerations
18.12.2.2 Clinical Trial Sponsor and Trial Staff Considerations
18.12.2.3 Patients as Participants in Decentralized Clinical Trials Considerations
18.13 Virtual Visits, Virtual Health and Clinical Outcomes
18.14 Pre-pandemic State of Virtual Care
18.15 Virtual Visits During the First Wave of the Pandemic
18.16 Virtual Health Post Pandemic
18.17 Conclusions and Future Outlook
18.17.1 Connected Data Everywhere; The New Ecosystem of Patient Care and Clinical Research
18.18 Technology Advances That Can Help the Patient Experience in Virtual Health
18.18.1 Data Analytics
18.18.2 Robotics
18.18.3 Artificial Intelligence
18.19 Case Study: Decentralized Clinical Trial Platform
18.19.1 Joseph Is Officially Enrolled in the Virtual Clinical Trial!
18.19.2 Throughout the Course of the VCT
18.19.3 Use Case Discussion Topics
References
19: Digital Health Solutions Transforming Long-Term Care and Rehabilitation
19.1 Emerging Digital Healthcare Solutions
19.2 Opportunities for Digital Solutions
19.2.1 Long-Term Care
19.2.1.1 Tele-Monitoring Solutions for Home Care
Fatigue and Anxiety
Quality of Life
Sleep
Physical Activity
Smoking Cessation
Nutrition
Tele-Monitoring Solutions for Active and Elderly Care
19.2.2 Challenges in the Use of Digital Health Technologies
19.2.3 Rehabilitation
19.2.3.1 Telerehabilitation Solutions for Efficient Home Rehabilitation
Wearable Devices and Virtual Reality to Support Home Neurorehabilitation
Wearable Devices and Virtual Reality to Support Geriatric Rehabilitation
Telerehabilitation to Support Transition in Care
19.2.3.2 Multimodal Approaches to Rehabilitation and Home Rehabilitation
Virtual Reality, Robotics and Recovery Outcomes
Virtual and Augmented Reality to Support Activities of Daily Living
Virtual and Augmented Reality to Enhance Clinical Assessment
19.2.3.3 Rehabilitation and Telerehabilitation in the Post COVID-19 Era
Acceptance and Adherence
Patient Engagement
Online Shared Decision Making
References
20: Learning Interprofessionally from a Real-Life Simulation in a Smart Home
20.1 Objectives
20.2 Activity Description
20.3 Required Materials
20.3.1 Setting
20.4 Assessment
20.5 Evaluation
20.6 Impact
References
21: Predicting Preventive Care Service Usage in a Direct Primary Care Setting Using Machine Learning
21.1 Introduction
21.2 What Is Direct Primary Care?
21.3 Machine Learning
21.3.1 Classification Using Supervised Learning
21.3.2 Evaluating Classifier Model Performance
21.4 Case Study: Predicting Preventive Care Service Usage in a Direct Primary Care Setting Using Machine Learning
21.4.1 Data Source and Cohort Definition
21.4.2 Model Descriptions and Scope
21.4.3 Predictive Preventive Care Model
21.4.4 Set of Predictive Preventive Screening Test Models
21.4.4.1 Model Performance Testing
21.5 Results
21.5.1 Discussion
21.6 Conclusions/Future Directions
References
Part III: Horizons
22: Healthcare Delivery in the Digital Age
22.1 Introduction
22.2 Implications of Digital Transformation in Healthcare
22.3 A Possible Vision of the Future
22.4 Components and Distribution of a Future Healthcare Delivery System
22.5 Two Examples of Ecosystems of Care
22.6 Conclusions
References
23: Informatics and Clinical Workforce Competencies and Education
23.1 Introduction
23.2 Competencies for Informatics Professionals
23.3 Informatics Competencies for Healthcare Professionals
23.4 Certification of Informatics Professionals
23.5 Education in Informatics
23.6 Characterizing the Health Informatics Workforce
23.7 Conclusions/Outlook
References
24: Emerging Need for a New Vision of Multi-Interprofessional Training in Health Informatics
24.1 Current and Future Needs of Healthcare Systems
24.2 Health Informatics and Multi-Interprofessional Education (MIPE)
24.2.1 What Is Health Informatics?
24.2.2 What Is Multi-Interprofessional Education?
24.3 Attributes of Traditional Interprofessional and Multi-Interprofessional Training in Health Informatics
24.3.1 Interprofessional Training/Education in Health Informatics
24.3.2 Multi-Interprofessional Training/Education
24.4 Benefits and Challenges to Working Multi-Interprofessionally in Healthcare Settings
24.4.1 Benefits
24.4.2 Challenges
24.5 Role of Health Informaticians Within Multi-Interprofessional Teams
24.6 Conclusions and Outlook
References
25: Understanding Disparities in Healthcare: Implications for Health Systems and AI Applications
25.1 Introduction
25.2 Factors Contributing to Health and Healthcare Disparities
25.2.1 Health Disparities and Health Equity
25.2.2 Factors Contributing to Health and Healthcare Disparities
25.2.3 The Impact of Race and Racism
25.2.4 Geography
25.2.5 Literacy and Language Comprehension
25.2.6 Income and Wealth
25.2.7 Insurance Coverage and Costs of Care
25.2.8 Healthcare Policy and Finance
25.3 The Interconnected Web of Health Disparities: COVID-19
25.4 Implications for Health Systems, Data and AI
25.5 Conclusions
References
26: Addressing Health Equity: Sources, Impact and Mitigation of Biased Data
26.1 Introduction
26.2 Sources of Bias in Healthcare
26.2.1 Educational and Experiential Bias
26.2.2 Data
26.2.2.1 Clinical Trials Data
26.2.2.2 Real-World Data
26.2.2.3 Administrative Claims Data
26.2.2.4 Electronic Health/Medical Record Data
26.3 The Impact of Biased Data
26.4 Addressing Data Bias
26.5 Broader Perspectives
26.5.1 Short-Term Solutions
26.5.2 Medium-Term Solutions
26.5.3 Long-Term Solutions
26.6 Conclusions
References
27: A Future Health Care Analytic System: Part 1—What the Destination Looks Like
27.1 Introduction
27.1.1 Overview
27.1.2 The Context
27.2 Why Is a Comprehensive Health Care Analytic System Needed?
27.2.1 How a Comprehensive Health Care Analytic System Would Help
27.2.2 Why We Need a Planned, Collaborative Effort Based on an Overarching Conceptual Model
27.3 What Is Needed for a Successful System?
27.3.1 High-Level Logical Requirements
27.3.1.1 Patients, Clinicians, and Other Participants
27.3.1.2 Health, Health Problems, Health Outcomes
27.3.1.3 Major Clinical Tasks, Units of Care, Choice of Care, and Processes of Care
27.3.1.4 Suboptimal Care
27.3.1.5 Root and Mediating Causes of Suboptimal Care and Outcomes
27.3.2 High-Level System Requirements
27.3.2.1 An Integrated, Comprehensive System
27.3.2.2 A Generalized Conceptual and Causal Model That Can Be ‘Localized’ to Specific Health Problems
27.3.3 Questions the Analytic System Will Answer
27.3.3.1 Identifying Actionable Opportunities for Systematic Improvement (Table 27.6b)
27.3.3.2 Providing Individualized Patient Support and Advice (Table 27.6c)
27.4 Conclusion
References
28: A Future Health Care Analytic System (Part 2): What is Needed and ‘Getting It Done’
28.1 Introduction
28.1.1 Overview of This Chapter
28.1.2 Recap from Previous Chapter
28.2 Building Blocks
28.2.1 Outcomes of Health Care
28.2.1.1 Health Outcomes
28.2.1.2 Clinical Resource Use
28.2.1.3 Burden of Care on Patient, Family, or Caregivers
28.2.1.4 Equity
28.2.1.5 Respect for the Dignity and Autonomy of Patients
28.2.2 A Standardized Longitudinal Patient History as the Primary Input for Analytics
28.2.2.1 Tracking a Person’s Health Problems
28.2.2.2 Need, Choice, Units, Process, and Norms of Care
28.2.2.3 Organizing Units and Processes of Care for Analysis
28.2.2.4 Assembly of the Analytic Patient History
28.2.2.5 Curated Concepts, Categories, and Relationships
28.2.3 The Range of Analyses Supported
28.3 Getting It Done
28.4 Summary of a ‘Generic’ Analytic Framework for a Comprehensive System
28.5 Conclusion
Reference
29: HIT, Informatics and Ethics
29.1 US Health Care: Background
29.2 Nexus of Informatics and Ethics
29.3 Ethics 101
29.4 Physician Oaths
29.5 Codes of Ethics
29.6 Looking Ahead
29.7 Conclusion
References
30: Nurse Informaticists and the Coming Transformation of the U.S. Healthcare System
30.1 Clinical Transformation and the Critical Role of Nurse Informaticists
30.2 Clinical Transformation Happening in Patient Care Organizations Nationwide
30.3 Nurse Informaticists’ Unique Role
30.4 Number, Scope of CNIOs Continue to Grow and Expand
References
31: The Future of Health Systems: Health Intelligence
31.1 Introduction
31.2 Health Informatics
31.3 Health Intelligence
31.4 Conclusion and Future Directions
References
32: Health IT for the Future – It Isn’t (Just) About the Technology
32.1 Introduction
References
Index