Healthcare Information Management Systems: Cases, Strategies, and Solutions

Description

This new edition of the classic textbook provides bold and honest descriptions of the current and evolving state of US healthcare information technology. Emerging technologies and novel practice and business models are changing the delivery and management of healthcare, as innovation and adoption meet new needs and challenges, such as those posed by the recent COVID-19 pandemic. Many facets of these are presented in this volume: • The increasing mutual impact of information technology and healthcare with respect to costs, workforce training and leadership • The changing state of healthcare IT privacy, security, interoperability and data sharing through health information exchange • The rise and growing importance of telehealth/telemedicine in the era of COVID-19 • Innovations and trends in the development and deployment of health IT in public health, disease modeling and tracking, and clinical/population health research • Current work in health IT as it is used in patient safety, chronic disease management, critical care, rehabilitation/long-term/home-based patient care and care coordination • “Brave new world” visions of healthcare and health IT, with forward- looking considerations of the impact of artificial intelligence, machine learning on healthcare equity and policy Building on the success of previous editions, this 5th edition of Healthcare Information Management Systems: Cases, Strategies, and Solutions provides healthcare professionals insights to new frontiers and to the directions being taken in the technical, organizational, business and management aspects of information technology in the ongoing quest to optimize healthcare quality and cost, and to improve universal health at all levels. 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