Biomedical Informatics: Computer Applications in Health Care and Biomedicine

Description

This 5th edition of this essential textbook continues to meet the growing demand of practitioners, researchers, educators, and students for a comprehensive introduction to key topics in biomedical informatics and the underlying scientific issues that sit at the intersection of biomedical science, patient care, public health and information technology (IT). Emphasizing the conceptual basis of the field rather than technical details, it provides the tools for study required for readers to comprehend, assess, and utilize biomedical informatics and health IT. It focuses on practical examples, a guide to additional literature, chapter summaries and a comprehensive glossary with concise definitions of recurring terms for self-study or classroom use. Biomedical Informatics: Computer Applications in Health Care and Biomedicine reflects the remarkable changes in both computing and health care that continue to occur and the exploding interest in the role that IT must play in care coordination and the melding of genomics with innovations in clinical practice and treatment.  New and heavily revised chapters have been introduced on human-computer interaction, mHealth, personal health informatics and precision medicine, while the structure of the other chapters has undergone extensive revisions to reflect the developments in the area. The organization and philosophy remain unchanged, focusing on the science of information and knowledge management, and the role of computers and communications in modern biomedical research, health and health care. Foreword References Preface to the Fifth Edition Information Management in Biomedicine Overview and Guide to Use of This Book The Study of Computer Applications in Biomedicine The Need for a Course in Biomedical Informatics The Need for Specialists in Biomedical Informatics Acknowledgments Contents Associate Editor Contributors Editors About the Future Perspective Authors (Chapter 30) About the Editors I: Recurrent Themes in Biomedical Informatics 1: Biomedical Informatics: The Science and the Pragmatics 1.1 The Information Revolution Comes to Medicine 1.1.1 Integrated Access to Clinical Information 1.1.2 Today’s Electronic Health Record (EHR) Environment 1.1.3 Anticipating the Future of Electronic Health Records 1.2 Communications Technology and Health Data Integration 1.2.1 A Model of Integrated Disease Surveillance3 1.2.2 The Goal: A Learning Health System 1.2.3 Implications of the Internet for Patients 1.2.4 Requirements for Achieving the Vision 1.2.4.1 Education and Training 1.2.4.2 Organizational and Management Change 1.3 The US Government Steps In 1.4 Defining Biomedical Informatics and Related Disciplines 1.4.1 Terminology Box 1.1: Definition of Biomedical Informatics 1.4.2 Historical Perspective 1.4.3 Relationship to Biomedical Science and Clinical Practice Box 1.2: The Nature of Medical Information 1.4.4 Relationship to Computer Science 1.4.5 Relationship to Biomedical Engineering 1.5 Integrating Biomedical Informatics and Clinical Practice References 2: Biomedical Data: Their Acquisition, Storage, and Use 2.1 What Are Clinical Data? 2.1.1 What Are the Types of Clinical Data? 2.1.2 Who Collects the Data? 2.2 Uses of Health Data 2.2.1 Create the Basis for the Historical Record 2.2.2 Support Communication Among Providers 2.2.3 Anticipate Future Health Problems 2.2.4 Record Standard Preventive Measures 2.2.5 Identify Deviations from Expected Trends 2.2.6 Provide a Legal Record 2.2.7 Support Clinical Research 2.3 Rationale for the Transition from Paper to Electronic Documentation 2.3.1 Pragmatic and Logistical Issues 2.3.2 Redundancy and Inefficiency 2.3.3 Influence on Clinical Research 2.3.4 The Passive Nature of Paper Records 2.4 New Kinds of Data and the Resulting Challenges 2.5 The Structure of Clinical Data 2.5.1 Coding Systems 2.5.2 The Data-to-Knowledge Spectrum 2.6 Strategies of Clinical Data Selection and Use 2.6.1 The Hypothetico-Deductive Approach 2.6.2 The Relationship Between Data and Hypotheses 2.6.3 Methods for Selecting Questions and Comparing Tests 2.7 The Computer and Collection of Medical Data References 3: Biomedical Decision Making: Probabilistic Clinical Reasoning 3.1 The Nature of Clinical Decisions: Uncertainty and the Process of Diagnosis 3.1.1 Decision Making Under Uncertainty 3.1.2 Probability: An Alternative Method of Expressing Uncertainty 3.1.3 Overview of the Diagnostic Process 3.2 Probability Assessment: Methods to Assess Pretest Probability 3.2.1 Subjective Probability Assessment 3.2.2 Objective Probability Estimates 3.3 Measurement of the Operating Characteristics of Diagnostic Tests 3.3.1 Classification of Test Results as Abnormal 3.3.2 Measures of Test Performance 3.3.3 Implications of Sensitivity and Specificity: How to Choose Among Tests 3.3.4 Design of Studies of Test Performance 3.3.5 Bias in the Measurement of Test Characteristics 3.3.6 Meta-Analysis of Diagnostic Tests 3.4 Post-test Probability: Bayes’ Theorem and Predictive Value 3.4.1 Bayes’ Theorem 3.4.2 The Odds-Ratio Form of Bayes’ Theorem and Likelihood Ratios 3.4.3 Predictive Value of a Test 3.4.4 Implications of Bayes’ Theorem 3.4.5 Cautions in the Application of Bayes’ Theorem 3.5 Expected-Value Decision Making 3.5.1 Comparison of Uncertain Prospects 3.5.2 Representation of Choices with Decision Trees 3.5.3 Performance of a Decision Analysis 3.5.4 Representation of Patients’ Preferences with Utilities 3.5.5 Performance of Sensitivity Analysis 3.5.6 Representation of Long-Term Outcomes with Markov Models 3.6 The Decision Whether to Treat, Test, or Do Nothing 3.7 Alternative Graphical Representations for Decision Models: Influence Diagrams and Belief Networks 3.8 Other Modeling Approaches 3.9 The Role of Probability and Decision Analysis in Medicine 3.10 Appendix A: Derivation of Bayes’ Theorem References 4: Cognitive Informatics 4.1 Introduction 4.1.1 Introducing Cognitive Science 4.1.2 Cognitive Science and Biomedical Informatics 4.2 Cognitive Science: The Emergence of an  Explanatory Framework 4.3 Human Information Processing 4.3.1 Cognitive Architectures and Human Memory Systems 4.3.2 The Organization of Knowledge 4.4 Medical Cognition 4.4.1 Expertise in Medicine 4.5 Human Factors Research and Patient Safety 4.5.1 Patient Safety 4.5.2 Unintended Consequences 4.5.3 Distributed Cognition and Electronic Health Records 4.6 Conclusion References 5: Human-Computer Interaction, Usability, and Workflow 5.1 Introduction to Human-Computer Interaction 5.2 Role of HCI in Biomedical Informatics 5.3 Theoretical Foundations 5.4 Usability of Health Information Technology 5.4.1 Analytical Approaches 5.4.1.1 Task Analysis 5.4.1.2 Inspection-Based Evaluation 5.4.1.3 Model-Based Evaluation 5.5 Usability Testing and User-Based Evaluation 5.5.1 Interviews and Focus Groups 5.5.2 Verbal Think Aloud 5.5.3 Usability Surveys and Questionnaires 5.5.4 Field/Observational Approaches 5.6 Clinical Workflow 5.7 Future Directions 5.8 Conclusion References 6: Software Engineering for Health Care and Biomedicine 6.1 How Can a Computer System Help in Health Care? 6.2 Software Functions in Health Care 6.2.1 Case Study of Health Care Software 6.2.2 Acquiring and Storing Data 6.2.3 Summarizing and Displaying Data 6.2.4 Facilitating Communication and Information Exchange 6.2.5 Generating Alerts, Reminders, and Other Forms of Decision Support 6.2.6 Supporting Educational, Research, and Population and Public Health Initiatives 6.3 Software Development and Engineering 6.3.1 Software Development 6.3.1.1 Planning/Analysis 6.3.1.2 Design 6.3.1.3 Development 6.3.1.4 Integration and Test 6.3.1.5 Implementation 6.3.1.6 Verification and Validation 6.3.1.7 Operations and Maintenance 6.3.1.8 Evaluation 6.3.2 Software Development Models 6.3.2.1 Waterfall Model 6.3.2.2 Agile Models 6.3.3 Software Engineering 6.3.3.1 Software Acquisition 6.3.3.2 Case Studies of EHR Adoption 6.3.3.3 Enhancing Acquired Software 6.3.3.4 Integration with Existing Systems 6.3.3.5 Development of Custom Applications that Supplement or Enhance Commercial Systems 6.4 Emerging Influences and Issues 6.5 Summary References 7: Standards in Biomedical Informatics 7.1 The Idea of Standards 7.2 The Need for Health Informatics Standards 7.2.1 Early Standards to Support the Use of IT in Health Care 7.2.2 Transitioning Standards to Meet Present Needs 7.2.3 Settings Where Standards Are Needed 7.3 Standards Undertakings and Organizations 7.3.1 The Standards Development Process 7.3.2 Data Standards Organizations 7.3.2.1 American National Standards Institute 7.3.2.2 ASC X12 7.3.2.3 ASTM International 7.3.2.4 Clinical Data Interchange Standards (CDISC) 7.3.2.5 Digital Imaging and Communications in Medicine (DICOM) 7.3.2.6 European Committee for Standardization Technical Committee 251 7.3.2.7 GS1 7.3.2.8 Health Level Seven International (HL7) 7.3.2.9 Institute of Electrical and Electronic Engineering (IEEE) 7.3.2.10 ISO Technical Committee 215—Health Informatics 7.3.2.11 Integrating the Healthcare Enterprise 7.3.2.12 National Council for Prescription Drug Program (NCPDP) 7.3.2.13 OpenEHR 7.3.2.14 Personal Connected Health Alliance (PCHA) 7.3.2.15 SNOMED International 7.3.2.16 OHDSI 7.4 Detailed Clinical Models, Coded Terminologies, Nomenclatures, and Ontologies 7.4.1 Motivation for Structured and Coded Data 7.4.2 Detailed Clinical Models 7.4.3 Vocabularies, Terminologies, and Nomenclatures 7.4.4 Specific Terminologies 7.4.4.1 International Classification of Diseases and Its Clinical Modifications 7.4.4.2 Current Procedural Terminology 7.4.4.3 Diagnostic and Statistical Manual of Mental Disorders 7.4.4.4 SNOMED Clinical Terms and Its Predecessors 7.4.4.5 GALEN 7.4.4.6 Logical Observations, Identifiers, Names, and Codes 7.4.4.7 Nursing Terminologies 7.4.4.8 Drug Codes 7.4.4.9 Medical Subject Headings 7.4.4.10 RadLex 7.4.4.11 Bioinformatics Terminologies 7.4.4.12 Unified Medical Language System 7.5 Data Interchange Standards 7.5.1 General Concepts and Requirements 7.5.2 Specific Data Interchange Standards 7.5.2.1 HL7 Standards Compendium of HL7 Standards Clinical Document Architecture HL7 Fast Healthcare Interoperability Resources 7.5.2.2 American Dental Association Standards 7.5.2.3 Health Industry Business Communications Council Standards 7.5.2.4 The Electronic Data Interchange for Administration, Commerce, and Transport Standard 7.6 Today’s Reality and Tomorrow’s Directions 7.6.1 The Interface: Standards and Systems 7.6.2 Future Directions References 8: Natural Language Processing for Health-Related Texts 8.1 Motivation 8.2 NLP Applications and Their Context 8.2.1 NLP Applications 8.2.2 Context for NLP Applications 8.3 Basic Computational NLP Tasks 8.3.1 Topic Modeling 8.3.2 Text Labeling 8.3.3 Sequence Labeling 8.3.4 Relation Extraction 8.3.5 Template Filling 8.4 Linguistic Knowledge and Representations 8.4.1 Terminological and Ontological Knowledge 8.4.2 Word-Level Representations 8.4.3 Sentence-Level Representations 8.4.4 Document-Level Representations 8.4.4.1 Automated Resolution of Referential Expressions 8.4.5 Pragmatics 8.5 Practical Considerations 8.5.1 Patient Privacy and Ethical Concerns 8.5.2 Good System Performance 8.5.3 System Interoperability 8.6 Research Considerations 8.6.1 Data Annotation 8.6.2 Evaluation 8.6.2.1 Evaluation Metrics 8.7 Recapitulation and Future Directions References 9: Bioinformatics 9.1 The Problem of Handling Biological Information 9.1.1 Many Sources of Biological Data 9.1.2 Implications for Clinical Informatics 9.2 The Rise of Bioinformatics 9.2.1 Roots of Modern Bioinformatics 9.2.2 The Genomics Explosion 9.3 Biology Is Now Data-Driven 9.3.1 Sequences in Biology 9.3.2 Structures in Biology 9.3.3 Genome Sequencing Data in Biology 9.3.4 Expression Data in Biology 9.3.5 Metabolomics Data in Biology 9.3.6 Epigenetics Data in Biology 9.3.7 Systems Biology 9.4 Key Bioinformatics Algorithms 9.4.1 Early Work in Sequence and Structure Analysis 9.4.2 Sequence Alignment and Genome Analysis 9.4.3 Prediction of Structure and Function from Sequence 9.4.4 Clustering of Gene Expression Data 9.4.4.1 Classification and Prediction 9.4.5 The Curse of Dimensionality 9.5 Current Application Successes from Bioinformatics 9.5.1 Data Sharing 9.5.2 Data Standards, Metadata and Biomedical Ontologies 9.5.2.1 Sequence and Genome Databases 9.5.3 Structure Databases 9.5.4 Analysis of Biological Pathways and Understanding of Disease Processes 9.5.5 Integrative Databases 9.6 Future Challenges as Bioinformatics and Clinical Informatics Converge 9.6.1 Linkage of Molecular Information with Symptoms, Signs, and Patients 9.6.2 Computational Representations of the Biomedical Literature 9.6.3 Computational Challenges with an Increasing Deluge of Biomedical Data 9.7 Conclusion References 10: Biomedical Imaging Informatics 10.1 Introduction 10.2 Image Acquisition 10.2.1 Anatomic (Structural) Imaging 10.2.2 Functional Imaging 10.2.3 Imaging Modalities 10.2.4 Image Quality 10.2.5 Imaging Methods in Other Medical Domains 10.3 Image Content Representation 10.3.1 Representing Visual Content in Digital Images 10.3.2 Representing Knowledge Content in Digital Images 10.4 Image Processing 10.4.1 Types of Image-Processing Methods 10.4.2 Global Processing 10.4.3 Image Enhancement 10.4.4 Image Rendering/Visualization 10.4.5 Image Quantitation 10.4.6 Image Segmentation 10.4.7 Image Registration 10.5 Image Interpretation and Computer Reasoning 10.5.1 Content-Based Image Retrieval 10.5.2 Computer-Based Inference 10.6 Conclusions References 11: Personal Health Informatics 11.1 Introduction 11.2 Patient-Centered Care and Personal Health Informatics 11.2.1 Using Biomedical Informatics to Impact Patient-Centered Medicine 11.2.2 Limitations of Patient- Centered Care 11.3 Historical Perspective of Personal Health Informatics 11.3.1 Paternalism and  Professionalism of Medicine and Informatics 11.3.2 The Rise of Patient-Centered Medicine and Personal Health Informatics 11.4 Important Concepts in Personal Health Informatics 11.4.1 Health Literacy and Numeracy 11.4.2 Digital Divide 11.4.3 Chronic Conditions 11.4.4 Conditions Associated with Aging 11.4.5 Behavior Management 11.5 The Impact of Personal Health Informatics on Biomedical Informatics 11.5.1 Data Science 11.5.2 Precision Medicine 11.5.3 Ethical, Legal and Social Issues 11.5.4 Communication 11.5.5 Mobile Health Care (mHealth) 11.5.6 Social Network Systems 11.5.7 Application Example: EHR Portals 11.5.8 Application Example: Personal Health Records 11.5.9 Application Example: Sensors for Home Monitoring and Tailored Health Interventions 11.6 Future Opportunities and Challenges 11.6.1 Reimbursement and Business Models 11.6.2 Opportunities for Innovation References 12: Ethics in Biomedical and Health Informatics: Users, Standards, and Outcomes 12.1 Ethical Issues in Biomedical and Health Informatics 12.2 Health-Informatics Applications: Appropriate Use, Users, and Contexts 12.2.1 The Standard View of Appropriate Use 12.2.2 Appropriate Users and Educational Standards 12.2.3 Obligations and Standards for System Developers and Maintainers 12.2.3.1 Ethics, Standards, and Scientific Progress 12.2.3.2 System Evaluation as an Ethical Imperative 12.3 Privacy, Confidentiality, and Data Sharing 12.3.1 Foundations of Health Privacy and Confidentiality 12.3.2 Electronic Clinical and Research Data 12.3.2.1 Technological Methods 12.3.2.2 Policy Approaches 12.3.2.3 Electronic Data and Human Subjects Research 12.3.2.4 Challenges in Bioinformatics 12.4 Social Challenges and Ethical Obligations 12.4.1 Vendor Interactions 12.4.2 Computational Prognosis 12.4.3 Effects of Informatics on Traditional Relationships 12.4.3.1 Professional–Patient Relationships 12.4.3.2 Consumer Health Informatics 12.4.3.3 Personal Health Records 12.5 Legal and Regulatory Matters 12.5.1 Difference Between Law and Ethics 12.5.2 Legal Issues in Biomedical Informatics 12.5.2.1 Liability Under Tort Law 12.5.2.2 Privacy and Confidentiality 12.5.2.3 Copyright, Patents, and Intellectual Property 12.5.3 Regulation and Monitoring of Computer Applications in Health Care 12.5.4 Software Certification and Accreditation 12.6 Summary and Conclusions References 13: Evaluation of Biomedical and Health Information Resources 13.1 Introduction 13.2 Why Are Formal Evaluation Studies Needed? 13.2.1 Computing Artifacts Have Special Characteristics 13.2.2 The Special Issue of Safety 13.3 Two Universals of Evaluation 13.3.1 The Full Range of What Can Be Formally Studied 13.3.2 The Structure of All Evaluation Studies, Beginning with a Negotiation Phase 13.4 Deciding What to Study and What Type of Study to Do: Questions and Study Types 13.4.1 The Importance of Identifying Questions 13.4.2 Selecting a Study Type 13.4.3 Factors Distinguishing the Nine Study Types 13.5 Conducting Investigations: Collecting and Drawing Conclusions from Data 13.5.1 Two Grand Approaches to Study Design, Data Collection, and Analysis 13.5.2 Conduct of Objectivist Studies 13.5.2.1 Structure and Terminology of Comparative Studies 13.5.2.2 Issues of Measurement 13.5.2.3 Sampling Strategies 13.5.2.4 Control Strategies in Comparative Studies 13.5.2.5 Drawing Conclusions from Observational Data: Real World Evidence 13.5.3 Conduct of Subjectivist Studies 13.5.3.1 The Rationale for Subjectivist Studies 13.5.3.2 A Rigorous, but Different, Methodology 13.5.3.3 Natural History of a Subjectivist Study 13.5.3.4 Subjectivist Data-Collection and Data-Analysis Methods 13.6 Communicating Evaluation Results 13.7 Conclusion: Evaluation as an Ethical and Scientific Imperative Appendices Appendix A: Two Evaluation Scenarios Appendix B: Exemplary Evaluation Studies References II: Biomedical Informatics Applications 14: Electronic Health Records 14.1 What Is an Electronic Health Record? 14.1.1 Purpose of a Patient Record 14.1.2 EHR Overview 14.2 Historical Perspective: Development of EHRs 14.3 Functional Components of an EHR 14.3.1 Patient Data Capture, Aggregation, and Review 14.3.1.1 Data Integration and Standards 14.3.1.2 Clinician Data Entry 14.3.1.3 Data Display Timeline Graphs Timeline Flowsheets Summaries and Snapshots Dynamic On-Demand Data Views 14.3.2 Computerized Provider Order Entry 14.3.3 Clinical Decision Support 14.3.4 Access to Knowledge Resources 14.3.5 Care Team and Patient Communication 14.3.6 Billing and Coding 14.4 EHRs for Secondary and Population-Based Uses 14.4.1 Population-Based Clinical Care 14.4.2 Clinical Research 14.4.3 Quality Reporting 14.4.4 Administration 14.5 Challenges Ahead 14.5.1 Usability 14.5.2 Standards 14.5.3 Costs and Benefits 14.5.4 Leadership References 15: Health Information Infrastructure 15.1 Introduction 15.2 Vision & Benefits of HII 15.2.1 Value Versus Completeness of Information 15.2.2 Value in Patient Care 15.3 History 15.4 Requirements for HII 15.4.1 Privacy and Trust 15.4.2 Stakeholder Cooperation 15.4.3 Ensuring Information in Standard Electronic Form 15.4.4 Financial Sustainability 15.4.5 Community Focus 15.4.6 Governance and Organizational Issues 15.5 Architecture for HII 15.5.1 Institution-Centric Architecture 15.5.2 Patient-Centric Architecture (Health Record Banking) 15.6 Progress Towards HRBs 15.6.1 HRB Opposition 15.6.2 Factors Accelerating HRB Progress 15.7 Evaluation 15.8 Conclusions References 16: Management of Information in Health Care Organizations 16.1 Overview 16.2 Historical Evolution of the Technology of Health Care Information Systems (HCISs) 16.2.1 Central and Mainframe-Based Systems 16.2.2 Departmental Systems 16.2.3 Integration Challenges 16.2.4 Evolution to Enterprise-Wide Health Care System Information Systems 16.2.5 Information Requirements 16.2.6 Process Integration 16.2.7 Security and Confidentiality Requirements 16.2.8 The Impact of Health Care Information Systems 16.2.9 Managing Information Systems in a Changing Health Care Environment 16.2.10 Changing Technologies 16.2.11 Changing Culture 16.2.12 Changing Processes 16.2.13 Changing Sources of Data 16.2.14 Management and Governance 16.3 Functions and Components of a Health Care Information System 16.3.1 Patient Management and Billing 16.3.2 Ancillary Services 16.3.3 Care Delivery and Clinical Documentation 16.3.4 Clinical Decision Support 16.3.5 Financial and Resource Management 16.4 Forces That Will Shape the Future of Health Care Information Systems Management 16.4.1 Changing Organizational Landscape 16.4.2 Changes Within the HCIS Organization 16.4.3 Technological Changes Affecting Health Care Organizations 16.4.4 Societal Change References 17: Patient-Centered Care Systems 17.1 Information Management in Patient-Centered Care 17.1.1 From Patient Care to Patient-Centered Care 17.1.2 Patient-Centered Care in Action 17.1.3 Coordination of Patient-Centered Care 17.1.4 Patient-Centered Care Across Multiple Patients 17.1.5 Integrating Indirect-Care Activities 17.1.6 Information to Support Patient-Centered Care 17.2 The Emergence of Patient- Centered Care Systems 17.2.1 Publications of the National Academy of Sciences 17.2.2 Federal Government Initiatives 17.2.3 Financial and Organizational Structures in Health Care 17.2.4 Advances in Patient-Centered Care Systems 17.3 Designing Systems for Patient-Centered Care 17.4 Current Research Toward Patient-Centered Care Systems 17.4.1 Formulation of Models 17.4.2 Development of Innovative Systems 17.4.3 Implementation of Systems 17.4.4 Effects of Clinical Information Systems on the Potential for Patient-Centered Care 17.5 Outlook for the Future References 18: Population and Public Health Informatics 18.1 Chapter Overview 18.2 What Is Public Health? 18.2.1 Public Health Versus Population Health 18.3 Public Health Informatics 18.4 Examples of Public Health Informatics Challenges and Opportunities 18.4.1 Overview of Public Health Information Systems in the U.S. 18.4.2 Immunization Information Systems: A Public Health Informatics Exemplar 18.4.3 Global Health Perspective and Opportunities 18.5 Public and Population Health Informatics Conclusion References 19: mHealth and Applications 19.1 Introduction 19.1.1 The Omnipresence of Mobile 19.1.2 Evolution of mHealth Technologies 19.1.2.1 PDAs and Cellular Phones 19.1.2.2 Smartphones and Tablets 19.1.2.3 Wearable Devices 19.1.3 Current Platforms 19.1.4 Data Access & Data Standards 19.2 Key Features of mHealth Technologies 19.2.1 Sensing to Collect Data 19.2.1.1 Assessing Physiological Processes 19.2.1.2 Inferring Activities 19.2.1.3 Inferring Context 19.2.2 Collecting Self-Reported Data 19.2.2.1 In-Situ Data Collection Methods 19.2.2.2 Mobile Research Platforms for In-Situ Data Collection 19.2.2.3 Incorporating Self-Reporting Into User Interactions 19.2.2.4 Easing Data Entry 19.2.3 Providing Interventions in Individuals’ Daily Lives 19.2.4 Providing Just-in-Time Adaptive Interventions 19.2.5 Supporting Self-Experimentation 19.3 Broad Considerations and Challenges 19.3.1 Privacy and Security 19.3.2 Changes in Clinician Work 19.3.3 Changes in Patient Work 19.3.4 Health Disparities 19.3.5 Regulatory Issues 19.4 Future Directions References 20: Telemedicine and Telehealth 20.1 Introduction 20.1.1 Telemedicine and Telehealth to Reduce the Distance Between the Consumer and the Health Care System 20.2 Historical Perspectives 20.2.1 Early Experiences 20.2.2 Recent Advances in Medical-Grade Broadband Technology 20.3 Bridging Distance with Informatics: Real-World Systems 20.3.1 The Forgotten Telephone 20.3.2 Electronic Messaging 20.3.3 Remote Monitoring 20.3.4 Remote Interpretation 20.3.5 Video-Based Telehealth 20.3.6 Telepresence 20.3.7 Delivering Specialty Knowledge to a Network of Clinical Peers 20.3.8 The Emergence of Telehealth during a Global Pandemic 20.4 Challenges and Future Directions 20.4.1 Challenges to Using the Internet for Telehealth Applications 20.4.2 Licensure and Economics in Telehealth 20.4.3 Logistical Requirements for Implementation of Telehealth Systems 20.4.4 Telehealth in Low Resource Environments 20.4.5 Future Directions References 21: Patient Monitoring Systems 21.1 What is Patient Monitoring? 21.1.1 Case Report 21.1.2 Patient Monitoring 21.2 Historical Perspective and the Measurement of Vital Signs 21.3 Development of ICUs 21.4 Development of Bedside Monitors 21.5 Modern Bedside Monitors 21.5.1 ECG Signal Acquisition and Processing 21.5.2 ABP Signal Acquisition and Processing 21.5.3 Pulse Oximeter Signal Acquisition and Processing 21.5.4 Bedside Data Display and Signal Integration 21.5.5 Challenges of Bedside Monitor Alarms 21.5.6 Biomedical Sensors 21.5.7 Strategies for Incorporating Bedside Monitoring Data into an Integrated Hospital EMR 21.6 Monitoring and Advanced Information Management in ICUs 21.6.1 Early Pioneering in ICU Systems 21.6.2 Recent Advances in ICU Clinical Management Systems 21.6.3 Acquiring the Data: Quality and Timeliness 21.6.4 Presentation of Data 21.6.5 Establishing the Decision Rules and Knowledge Base 21.6.6 Clinical Charting Systems: Nurses, Pharmacists, Physicians, Therapists 21.6.7 Automated Data Acquisition From All Bedside Devices 21.6.8 Rounding Process: Single Patient Viewer 21.6.9 Collaborative Process, ICU Change-of-Shift, and Handover Issues 21.7 Computerized Decision Support and Alerting 21.7.1 Laboratory Alerts 21.7.2 Ventilator Weaning Management and Alarm System 21.7.3 Adverse Drug Event Detection and Prevention 21.7.4 IV Pump and Medications Monitoring 21.8 Remote Monitoring and Tele-ICU 21.9 Predictive Alarms and Syndrome Surveillance 21.10 Opportunities for Future Development 21.10.1 Value of Computerized ICU Care Processes 21.11 Clinical Control Tower and Population Management References 22: Imaging Systems in Radiology 22.1 Introduction 22.2 Basic Concepts and Issues 22.2.1 Roles for Imaging in Biomedicine 22.2.1.1 Detection and Diagnosis 22.2.1.2 Assessment and Planning 22.2.1.3 Image-Guided Procedures 22.2.1.4 Communication 22.2.1.5 Education and Training 22.2.1.6 Research 22.2.2 The Radiologic Process and its Interactions 22.2.3 Electronic Imaging Systems 22.2.3.1 Image Acquisition 22.2.3.2 DICOM 22.2.3.3 Image Transmission, Storage, and Display 22.2.4 Integration with Other Healthcare Information 22.2.4.1 Radiology Information Systems (RIS) 22.2.4.2 Speech Recognition 22.2.4.3 Computer-Aided Diagnosis (CAD) 22.2.4.4 Advanced Visualization 22.2.4.5 Advanced Reporting 22.2.4.6 Workflow Management (Including Dashboards) 22.2.4.7 Teleradiology 22.2.4.8 Enterprise Integration (Including HL7, Decision Support) 22.2.4.9 Decision Support 22.3 Imaging in Other Departments 22.3.1 Cardiology 22.3.2 Obstetrics and Gynecology 22.3.3 Intraoperative/Endoscopic Visible Light 22.3.4 Pathology and Dermatology 22.4 Cross-Enterprise Imaging 22.4.1 CD Image Exchange 22.4.2 Direct Network Image Exchange 22.5 Future Directions for Imaging Systems References 23: Information Retrieval 23.1 Introduction 23.2 Evolution of Biomedical Information Retrieval 23.3 Knowledge-Based Information in Health and Biomedicine 23.3.1 Information Needs and Information Seeking 23.3.2 Changes in Publishing 23.3.3 Quality of Information 23.3.4 Evidence-Based Medicine 23.4 Content of Knowledge-Based Information Resources 23.4.1 Bibliographic Content 23.4.2 Full-text Content 23.4.3 Annotated Content 23.4.4 Aggregated Content 23.5 Indexing 23.5.1 Controlled Terminologies 23.5.2 Manual Indexing 23.5.3 Automated Indexing 23.6 Retrieval 23.6.1 Exact-Match Retrieval 23.6.2 Partial-Match Retrieval 23.6.3 Retrieval Systems 23.7 Evaluation 23.7.1 System-Oriented Evaluation 23.7.2 User-Oriented Evaluation 23.8 Research Directions 23.9 Digital Libraries 23.9.1 Functions and Definitions of Libraries 23.9.2 Access 23.9.3 Interoperability 23.9.4 Intellectual Property 23.9.5 Preservation 23.10 Future Directions for IR Systems and Digital Libraries References 24: Clinical Decision-Support Systems 24.1 The Nature of Clinical Decision Making 24.2 Motivation for Computer-Based CDS 24.2.1 Physician Information Needs and Clinical Data Management 24.2.2 EHR Adoption and Integration of CDS 24.2.3 Precision Medicine 24.2.4 Savings Potential with Health IT and CDS 24.3 Methods of CDS 24.3.1 Acquisition and Validation of Patient Data 24.3.2 Decision-Support Methodologies 24.3.2.1 Context-Specific Information Retrieval 24.3.2.2 Organizing or Grouping Information as a CDS Method 24.3.2.3 Hardcoding Clinical Algorithms 24.3.2.4 Learning from Data Probabilistic Systems Machine Learning 24.3.2.5 Declarative Representation of Knowledge Bayesian Belief Networks Rule-Based Approaches More General Representations of Knowledge 24.3.3 Coda 24.4 Translating CDS to the Clinical Enterprise 24.4.1 Standard Patient Information Model 24.4.2 Adoption of Standard Knowledge-Representation Models 24.4.2.1 Standards for Encoding Clinical Guideline Models 24.4.2.2 Standards for Encoding ECA Rules 24.4.3 Modes of Deployment of CDS 24.4.4 Workflow and Setting-Specific Factors 24.4.5 Sharing of Best-Practice Knowledge for CDS 24.5 Future Research and Development for CDS 24.5.1 Standards Harmonization for Knowledge Sharing and Implementation 24.5.2 Context-based Knowledge Selection 24.5.3 Representation Models 24.5.4 Externalizing CDS 24.5.5 Usability Research and CDS 24.5.6 Data-Driven CDS 24.6 Conclusions References 25: Digital Technology in Health Science Education 25.1 Introduction 25.2 Approaches to Teaching and Learning with Digital Technology 25.2.1 Theories of Learning 25.2.2 Digital Technologies for Learning Environments 25.3 Overview of Learner Audiences 25.3.1 Undergraduate and Graduate Health Care Professions Students 25.3.2 Practicing Health Care Providers 25.3.3 Patients, Caregivers, and the Public 25.4 Digital Learning Systems 25.4.1 Learning Content Management Systems 25.4.2 Learning Management Systems 25.4.3 Just-in-Time Learning Systems and Performance Support 25.4.4 Interoperability Standards 25.4.5 Usability and Access 25.5 Digital Content 25.5.1 Text/Image/Video Content 25.5.2 Interactive Content 25.5.3 Games 25.5.4 Cases, Scenarios and Problem-Based Learning 25.5.5 Simulations – Virtual Patients 25.5.6 Simulations – Procedures and Surgery 25.5.7 Simulations – Mannequins 25.5.8 Virtual Worlds 25.5.9 Virtual Reality 25.5.10 Augmented Reality 25.5.11 3D Printed Physical Models 25.6 Assessment of Learning 25.6.1 Quizzes, Multiple Choice Questions, Flash Cards, Polls 25.6.2 Branching Scenarios 25.6.3 Simulations 25.6.4 Intelligent Tutoring, Guidance, Feedback 25.6.5 Analytics 25.7 Future Directions and Challenges 25.8 Conclusion References 26: Translational Bioinformatics 26.1 What Is Translational Bioinformatics? 26.1.1 Differences from “Traditional” Bioinformatics 26.2 The Rise of Translational Bioinformatics 26.2.1 Promise of the Human Genome Project 26.2.2 What Is Translational Research? 26.2.3 Precision Medicine as a Driving Force 26.3 Key Concepts for Translational Bioinformatics 26.3.1 Data Storage and Management 26.3.2 Biomarkers 26.4 Biomarker Discovery 26.4.1 Clinical Relevance Versus Statistical Significance 26.4.2 Biomarkers for Drug Repurposing 26.4.3 Genomic Data Resources 26.5 Pharmacogenomics 26.5.1 Key Entities and Associated Data Resources 26.5.2 TBI Applications in Pharmacogenomics 26.5.3 Challenges for Pharmacogenomics 26.6 Ontologies for Translational Research 26.6.1 Ontology-Related Resources for Translational Scientists 26.6.2 Enrichment Analysis 26.7 Natural Language Processing for Information Extraction 26.7.1 Mining Electronic Health Records 26.7.2 Dataset Annotations 26.8 Network Analysis 26.9 Basepairs to Bedside 26.9.1 Whole Genome Sequencing 26.9.2 Here Are Some Human Beings 26.10 Challenges and Future Directions 26.10.1 Expansion of Data Types 26.10.2 Changes for Medical Training, Practice, and Support 26.11 Conclusions References 27: Clinical Research Informatics 27.1 Introduction 27.2 A Primer on Clinical Research 27.2.1 The Modern Clinical Research Environment 27.2.1.1 Phased Randomized Controlled Trials 27.2.2 Information Needs and Systems in the Clinical Research Environment 27.2.2.1 Information Systems Supporting Clinical Research Programs 27.2.2.2 Clinical Research Management Systems 27.3 Data Sharing Resources and Networks for Clinical Research 27.3.1 Publicly Deposited Clinical Research Metadata and Data Resources 27.3.2 Clinical and Translational Science Award (CTSA) Network 27.3.3 i2b2 and SHRINE 27.3.4 Accrual to Clinical Trials (ACT) Network 27.3.5 PCORNet 27.3.6 Observational Health Data Sciences and Informatics (OHDSI) 27.3.7 Commercial and Health Care Information Technology Vendor Networks 27.3.8 All of Us 27.4 Data Standards in Clinical Research 27.4.1 Data Modeling Standards to Support Clinical Research 27.4.2 Terminology Standards to Support Clinical Research 27.4.3 Clinical Research Reporting Requirements 27.5 CRI and the COVID-19 Pandemic 27.6 Future Directions for CRI 27.7 Conclusion References 28: Precision Medicine and Informatics 28.1 What Is Precision Medicine? 28.2 Using EHRs for Genomic Discovery 28.3 Finding Research-Grade Phenotypes in EHRs 28.4 Omic Discovery Approaches 28.4.1 Genome-Wide Association Studies (GWAS) 28.4.2 Genomic Sequencing 28.4.3 Phenome-Wide Association Studies (PheWAS) 28.4.4 Other Omic Investigations 28.5 Approaches to Using Dense Genomic and Phenomic Data for Discovery 28.5.1 Combining Genotypes and Phenotypes as Risk Scores 28.5.2 Mendelian Randomization 28.5.3 Using Dense Data-Driven Measures to “Redefine” Disease 28.5.4 Use of Machine Learning and Artificial Intelligence to Advance Precision Medicine 28.6 Large Cohorts to Advance Precision Medicine Discovery 28.6.1 Need for Diversity, and Role of Precision Medicine in Health Disparities 28.7 Implementation of Precision Medicine in Clinical Practice 28.8 Sequencing Early in Life 28.9 Direct to Consumer Genetics 28.10 Conclusion Bibliography III: Biomedical Informatics in the Years Ahead 29: Health Information Technology Policy 29.1 Public Policy and Health Informatics 29.2 How Health IT Supports National Health Goals: Promise and Evidence 29.2.1 Improving Care Quality and Health Outcomes 29.2.2 Reducing Costs 29.2.3 Using Health IT to Measure Quality of Care 29.2.4 Holding Providers Accountable for Cost and Quality 29.2.5 Informatics Research 29.3 Beyond Adoption: Policy for Optimizing and Innovating with Health IT 29.3.1 Health Information Exchange 29.3.2 Patient Portals and Telehealth 29.3.3 Application Programming Interfaces 29.4 Policies to Ensure Safety of Health IT 29.4.1 Should Health IT Be Regulated as Medical Devices? 29.4.2 Alternative Ways to Improve Patient Safety 29.5 Policies to Ensure Privacy and Security of Electronic Health Information 29.5.1 Regulating Privacy 29.5.2 Security 29.5.3 Record Matching and Linking 29.6 The Growing Importance of Public Policy in Informatics References 30: The Future of Informatics in Biomedicine 30.1 The Present and Its Evolution from the Past 30.2 Looking to the Future References Glossary Name Index Subject Index