Clinical Research Informatics

Description

This extensively revised new edition comprehensively reviews the rise of clinical research informatics (CRI). It enables the reader to develop a thorough understanding of how CRI has developed and the evolving challenges facing the biomedical informatics professional in the modern clinical research environment. Emphasis is placed on the changing role of the consumer and the need to merge clinical care delivery and research as part of a changing paradigm in global healthcare delivery.  Clinical Research Informatics presents a detailed review of using informatics in the continually evolving clinical research environment. It represents a valuable textbook reference for all students and practising healthcare informatics professional looking to learn and expand their understanding of this fast-moving and increasingly important discipline.  Contents 1: Introduction to Clinical Research Informatics Overview Contexts and Attempts to Define Clinical Research Informatics Perspective, Objectives, and Scope Organization of the Book Conclusion References Part I: Foundations of Clinical Research Informatics 2: From Notations to Data: The Digital Transformation of Clinical Research Historical Perspective Analog Signal Processing Digital Signal Processing The Digitalization of Biomedical Data Dimensions of Complexity Computing Capacity and Information Processing Computational Power Network Capacity Local Storage Data Storage Data Density Design Complexity Analytic Sophistication The Emergence of Big Science Evolution of Astronomy and Physics Biology and Medicine as a Socially Interdependent Process The Social Transformation of Clinical Research Standards Comparable and Consistent Information Interoperable Systems and Constructs 3: Methodological Foundations of Clinical Research The Development of Pharmaceuticals: An Overview Conceptual Framework and Classification of Biomedical Studies Variability of Biological Phenomena Biomedical Studies: Definitions and Classification Observational or Epidemiological Studies Experimental or Interventional Studies Minimal Intervention Studies The Logical Approach to Defining the Outcome of a Clinical Trial Defining the Treatment Effect: From Measurement to Signal Defining the Study Sample Defining the Study Treatments Superiority Versus Non-Inferiority Experimental Designs Definitions and Basic Concepts Before-After Comparisons in a Single Treatment Group Antidotes Against Bias: Randomization, Blinding, and a Priori Definition of Analysis Parallel Group and Crossover Designs Parallel Group Designs Crossover Designs Variants of Parallel Group and Crossover Designs Innovative Approaches to Drug Development References 4: The Clinical Research Environment Overview Clinical Research Processes, Actors, and Goals Common Clinical Research Processes Identifying Potential Study Participants Screening and Enrolling Participants in a Clinical Study Scheduling and Tracking Study-Related Participant Events Executing Study Encounters and Associated Data Collection Tasks Ensuring the Quality of Study Data Regulatory and Sponsor Reporting and Administrative Tracking/Compliance Budgeting and Fiscal Reconciliation Human Subjects Protection Reporting and Monitoring Common Tasks and Barriers to Successful Study Completion Clinical Research Stakeholders Patients and Advocacy Organizations Academic Health Centers Clinical or Contract Research Organizations Sponsoring Organization Federal Regulatory Agencies Healthcare and Clinical Research Information Systems Vendors Other Clinical Research Actors Common Clinical Research Settings Common Clinical Research Goals A Framework for Data and Information Management Requirements in Clinical Research Clinical Research Workflow and Communications Workflow Challenges Paper-Based Information Management Practices Complex Technical and Communications Processes Interruptions Single Point of Information Exchange Cognitive Complexity Emergent Trends in Clinical Research Precision or Personalized Medicine Learning Healthcare Systems and Evidence Generating Medicine Real-World Data and Real-World Evidence (RWD and RWE) Bridging Public Health, Epidemiology, and Clinical Research Conclusion References 5: Next Generation Biorepository Informatics: Supporting Genomics, Imaging, and Innovations in Spatial Biology Introduction Informatics Considerations for a Next-Generation Biorepository Federation and Support for Federated Queries and Shared Ontology Biorepositories Best Practices Guidelines Biorepository Informatics Landscape Standards Considerations for a Next-Generation Biorepository Biospecimen Preservation Standards: ISO/TC 212 Biorepository Testing and Calibration Standard—ISO/IEC 17025 NAACCR Cancer Patient Standard Annotations Social Determinants of Health Significance, Relevance, and Challenges of Next-Gen Biorepositories The Human Cell Atlas (HCA) The Human BioMolecular Atlas (HuBMAP) The Human Tumor Atlas Network (HTAN) The Cellular Senescence Network (SenNet) Multiplex Technology for Biorepositories and Cell Atlases and “Spatial Biology” Conclusion References 6: Study Protocol Representation Overview The Study Protocol: Core Essence of a Clinical Research Study The Study Protocol Enabled by Clinical Research Informatics Current Inefficiencies in Research Protocol Informatics Benefits of the Computable Study Protocol Capturing the Complete Study Plan in Computable Form Providing Decision Support During Study Conduct Facilitating Timely and Accurate Data Capture and Storage Supporting Appropriate Statistical Analysis and Reporting Facilitating Appropriate Interpretation and Application of Results Promoting Reuse of Study Data and Artifacts Computability and Standardization Requirements Protocol Representation Standards Standards for Model Representation HL7 Reference Information Model (RIM) and Regulated Clinical Research Information Model (RCRIM) The Clinical Data Interchange Standards Consortium (CDISC) Protocol Representation Model (PRM) Standard Protocol Reporting Initiatives Biomedical Research Integrated Domain Group (BRIDG) Ontology of Clinical Research Other Protocol Modeling Approaches Eligibility Criteria Representation Standards Examples of Computable Protocol-Driven Research Across the Study Life Cycle Improving Study Design Improving Clinical Trial Efficiencies Improving Applications to Care and Research COVID-19 and the Computable Protocol The Protocol Model-Driven Future References 7: Clinical Research Information Systems CRIS Vendor Models Why Have Clinical Research Information Systems Evolved? The Concept of a Protocol Is Fundamental to CRISs CRISs Implement User Roles That Are Specific to Research Designs Supporting Differential Access to Individual Studies Representing Experimental Designs The Scope of a CRIS May Cross Institutional or National Boundaries Certain Low-Risk Clinical Studies May Not Store Personal Health Information Workflow in Clinical Research Settings Is Mostly Driven by the Study Calendar Time Windows Associated with Events The Event-CRF Cross-Table Clinical Research Subjects Are Not Typical “Patients” CRISs Often Need to Support Real-Time Self-Reporting of Subject Data Clinical Research Data Capture Is More Structured Than in Patient Care CRIS Electronic Data Capture Needs to Be Robust and Flexible and Efficient to Setup Use of Data Libraries Data Entry in Clinical Research May Not Always Be Performed in Real Time: Quality Control Is Critical CRIS-Related Processes During Different Stages of a Study Study Planning and Protocol Authoring Recruitment and Eligibility Determination Protocol Management and Study Conduct Patient Monitoring and Safety Analysis and Reporting Miscellaneous Issues Validation and Certification Standards Pragmatic Clinical Trials: Use of EHRs Instead of CRISs Interoperation Between CRISs and Non-EHR Software Concluding Remarks References 8: Public Policy Issues in Clinical Research Informatics Introduction and the Role of Public Policy in Clinical Research Informatics (CRI) Foundations of Clinical Research Policy Foundational Federal Legislation Food, Drug, and Cosmetic Act of 1938 Public Health Services Act of 1944 Core Regulations and Guidance for CRI Common Rule Common Rule Revisions Food and Drugs Regulation and Guidance HIPAA Privacy Rule and Research Regulatory Science and the Role of Informatics Regulatory Science as a Driver of Informatics at the FDA Real-World Evidence NIH as a Driver of Informatics Through Public Policy Twenty-First Century Cures Act Data Sharing Policies Emerging Policy Trends in CRI References Part II: Enabling Frameworks and Processes and Tools 9: Data Sharing and Reuse of Health Data for Research Introduction Relevant Concepts and Terms eSource Data (Electronic Source Data) Traceability Interoperability and Semantic Interoperability Data Standards FHIR Common Data Model Electronic Case Report Form (eCRF) Secondary Use of Data Benefits of Data Sharing and Reusing Health Data for Research Requirements for the Use of eSource for Regulated Research Technical Considerations for Reuse of Health Data for Research Retrieve Form for Data Capture (RFD) Common Data Model Harmonization (CDMH) HL7 FHIR Accelerators HL7 FHIR Accelerators Focused on the Use of Health Data for Research Standards-Based Healthcare Research Networks and Collaborative Projects IMI EHR4CR/I~HD/TriNetX PCORI and PCORNet N3C Elligo ResearchConnect General Considerations for Implementing eSource for Reuse of Health Data Best Practices and Methods of Data Sharing for Research Planning Adoption and Implementation of Data Standards from the Start Streamlining Processes Role of Research in Learning Health Systems and LHS Core Values Conclusion Appendix Examples of Collaborations, Initiatives, Models, and Tools Related to Data Sharing in Clinical Research References 10: Data Quality in Clinical Research Clinical Research Data Processes and Relationship to Data Quality Example 1 Example 2 Example 3 Example 4 Errors Exist Defining Data Quality Systematic Data Quality Planning Identifying and Defining Data to Be Collected Defining Data Collection Specifications Observing and Measuring Data Recording Data Processing Data Analyzing Data, Reporting Status, and Reporting Results Planning for Data Quality Assessing the Quality of Secondary Use Data Identification of Required Clinical Concepts Definition of Data Elements Exploration and Availability Assessment of Clinical Data Source Extraction of Relevant Data Elements Transformation and Curation of Extracted Clinical Data Fitness-for-Use Assessment and Data Analysis A Note on Data Bias Infrastructure for Assuring Data Quality Data Governance Impact of Data Quality on Research Results Summary References 11: Research Data Governance, Roles, and Infrastructure Introduction: A Conceptual Model Research Data Governance What Does Data Governance Govern? Why Data Governance?: The Value of Data Accuracy Validity Reliability Timeliness Relevance Completeness Ethical Fairness and Bias The Life Cycle of Data Why Data Governance? From Data Protection to Research Ethics Organizational Structure The Rules in Action Theories of Information Governance Data Governance Organization and Roles Implementation: An Effective Data Governance Structure The Building Blocks of an Effective Strategy: Case Study References 12: Informatics Approaches to Participant Recruitment Typical Clinical Research Recruitment Workflows Informatics Interventions in Clinical Research Recruitment Computerized Clinical Trial Decision Support Internet-Based Patient Matching Systems Informatics Intervention in Clinical Research Recruitment Support Data Repository-Based Clinical Trial Recruitment Support Sociotechnical Challenges Conclusion and Future Work References 13: Patient Registries for Clinical Research Definitions and Types of Registries The Role of Registries in Evolving Research Contexts The Role of Registries in Quality Improvement and Learning Health Systems Using Clinical Data for Patient Registries Interoperability and Data Standards Data Exchange Standards Content Standards Coding Systems and Controlled Terminologies Content Standards: Common Clinical Models and Data Elements Entity Identifiers Including the Unique Device Identifier (UDI) Clinical Phenotype Definitions Outcome Measures The Common Clinical Registry Framework Model and Other HL7 Standards Limitations of Registries Informatics Approaches for Building Registries Registry Functions The Future: Enabling the Creation and Use of Patient Registries for Biomedical and Health Services Research References Part III: Managing Different Types of Data Across Clinical and Translational Research 14: Best Practices for Research Data Management Introduction Purpose and Scope Metadata and Provenance Documentation Training Quality Control Checks Issues and Corrective Action Noncompliance, Protocol Violations, Unanticipated Events/Problems Database Access Version Control Roles of Data Management Data Management Plans Definition Purpose Data Management Tools Types of Tools Electronic Data Capture (EDC) Clinical Data Management Systems (CDMS) Clinical Trials Management System (CTMS) eConsent Dashboard and Analytics Tools Metadata Management and Dictionaries Selection and Implementation Data Acquisition Data Flows Definition Purpose When to Start Creating Data Flow Diagrams Example Diagramming Tools Important Considerations Case Report Forms Definition Purpose Developing Case Report Forms Important Considerations Self-Reported Patient Information Definition Purpose Procedures Usability Evaluation Data Transfer Plan Definition Purpose What? Where? How? When? Testing the Data Transfer Plan Contingency and Mitigation Planning Electronic Health Record Data Definition Purpose Acquiring EHR Data Computable Phenotypes Variation in EHR Data EHR Quality Checks Important Considerations Regulatory Considerations Definitions Purpose Common Data Management Regulations Implications for Clinical Data Managers Data Processing Definition Purpose Statistical Analysis Plan Definition Purpose Important Considerations Data Quality Definition Purpose Programmed Edit Checks Statistical Checks Manual/Visual Checks Data Integration Data Reconciliation Important Considerations Reports Definition Best Practices Development Process Important Considerations Vendor Management Types of Vendors Vendor Examples Selecting a Vendor Intellectual Property Chain of Custody Conclusion References 15: Patient-Reported Outcome Data Characteristics of Patient-Reported Outcomes Measurement Issues Comparability of PROs Across Studies and Time Reliability Validity Modes of Administration Personal (Face-to-Face) Administration Telephone Administration Mailed Surveys Web Surveys and Email Communication Electronic Data Collection Devices/Systems (ePRO) Voice Auditory Systems Screen Text Devices Desktop, Laptop, and Touch-Screen Tablet Computers Audiovisual Computer-Assisted Self-Interviewing (A-CASI) Systems Mobile Devices Item and Scale Development Modification of Existing PROs Instrument Repositories Item Banks Patient-Centered Drug Development Standardization and Integration into Clinical Information Systems Conclusion References 16: Molecular, Genetic, and Other Omics Data The Molecular Basis of Life Molecular Biology and Genomics Data Sequence Analysis Data Structure Analysis Data Functional Analysis Data Human Variation Microbiome Data Translating From the Molecular World to the Clinical World Clinical Application of Omics Data Integration of Molecular and Clinical Research Data Molecular Data to Support Clinical Research Application of Molecular Data to Disease Mechanisms of Disease Diagnostic Methods and Therapeutic Application Studies Molecular Epidemiological Data The Role of Microbiome in Disease The Future of Molecular Data in Clinical Research References 17: Clinical Trial Registries, Results Databases, and Research Data Repositories Introduction Rationale for Registration and Reporting Trial Registration Development of Trial Registration Clinical Trial Registries Standards, Policies, and Principles of Trial Registration Timing Quality of Clinical Trial Registries Evolution and Spin-Off Creation and Management of a Trial Registry: The User Perspective Design of Trial Registries International Standards Data Fields First-Level Fields Second-Level Fields Third-Level Fields Trial Registry Features and Data Quality Maintenance of Trial Registries Clinical Trial Results Databases/Results Databases Standards Sharing of Clinical Trial Data, Research Data Repositories and Platforms Anonymization Methods of Clinical Research Data Managing Identity Disclosure Risk in Microdata Other Risks in Microdata The User Perspective of Registration-Results-Data Sharing Process Evolution and Future Directions of Sharing of Trials Results Conclusion References Part IV: Knowledge Representation and Discovery 18: Knowledge Representation and Ontologies Ontology Development Important Ontological Distinctions Building Blocks: Top-Level Ontologies and Relation Ontology Formalisms and Tools for Knowledge Representation OBO Foundry and Other Harmonization Efforts Ontologies of Particular Relevance to Clinical Research Research Metadata Ontology Ontology of Clinical Research Ontology for Biomedical Investigations Biomedical Research Integrated Domain Group (BRIDG) Model Ontology Data Content Ontology National Cancer Institute Thesaurus (NCIT) SNOMED Clinical Terms (SNOMED CT) Logical Observation Identifiers, Names, and Codes (LOINC) RxNorm International Classification of Disease (ICD) Current Procedural Terminology (CPT) Human Phenotype Ontology Ontology Repositories Unified Medical Language System (UMLS) UMLS Knowledge Sources UMLS Tooling UMLS Applications BioPortal BioPortal Ontologies BioPortal Tooling BioPortal Applications Approaches to Ontology Alignment in Ontology Repositories Ontology in Action: Uses of Ontologies in Clinical Research Research Workflow Management Data Integration Electronic Phenotyping The Way Forward References 19: Developing and Promoting Data Standards for Clinical Research Clinical Research: Escalating Efficiencies with Data Standards Clinical Research Standards Developers and Drivers and Stakeholders Advancing Research by Fully Integrating with Health Systems: Relevance of Health Data Standards to Clinical Research Types of Healthcare Standards Data Exchange Standards: The Evolution of FHIR FHIR Data Preparation and Transformation: Terminology Binding International Landscape and Coordination Standards Influencers: Collaborative Initiatives Driving Efficiencies in Clinical Research Standards Maintenance and Access Conclusion Appendix: Standards Developing Organizations and Standards Organizations and Initiatives US Government Organizations Developing and Naming Standards Controlled Terminologies (Standards) Resources References 20: Nonhypothesis-Driven Research: Data Mining and Knowledge Discovery Introduction The Knowledge Discovery in Databases Process KDD Pipelines Data Selection Preprocessing Transformation Data Mining Artificial Neural Networks Decision Trees Support Vector Machines k-Nearest Neighbor Association Rules Bayesian Methods Unsupervised Machine Learning Interpretation, Evaluation, and Generalizability FAIRness in KDD Applications of Knowledge Discovery and Data Mining in Clinical Research Using Claims and Clinical Data for Temporal Predictions of Clinical Outcomes Using Clinical Data for Drug Repurposing Commonly Encountered Challenges in Data Mining Rare Instances Sources of Bias Other Limitations Infrastructure for Knowledge Discovery Future Directions Uncertainty Quantification Federated Learning Conclusion References 21: Clinical Natural Language Processing in Secondary Use of EHR for Research The Role of Clinical Natural Language Processing in the Secondary Use of EHR Use Case 1: Information Retrieval for Eligibility Screening or Cohort Identification Use Case 2: Information Extraction for Assembling Clinical Research Data Sets Foundations of Clinical Natural Language Processing Task Formulation Corpus Annotation Model Development Symbolic Approach Traditional Machine Learning Deep Learning Hybrid Model Evaluation Model Application A Step-by-Step Case Demonstration Task Formulation Corpus Annotation Model Development Symbolic Approach Deep Learning Approach Model Evaluation Clinical NLP Resources An Overview of Clinical NLP Community Challenges An Overview of Clinical NLP Systems and Toolkits An Overview of Clinical NLP Systems An Overview of Clinical NLP Toolkits and Packages Challenges, Opportunities, and Future Directions Reproducibility and Scientific Rigor Multisite NLP Collaboration Federated Learning and Evaluation Conclusion References Part V: Evolving Models and New Opportunities for the Transformation of Clinical Research 22: Back to the Future: The Evolution of Pharmacovigilance in the Age of Digital Healthcare Introduction Background Coasian Transactions: The Development and Evolution of PV/Drug Safety Research Agenda for Modern Pharmacovigilance Topic 1: The Operational Definition of an Adverse Event Topic 2: Expanding and Formalizing the Data Model Topic 3: Terminologies Topic 4: Discovery/Curation of AEs Topic 5: Delayed Toxicity and Complex Causal Assessments Topic 6: Risk Profiling of the Individual Topic 7: Emerging Data and Technologies for Pharmacovigilance Interoperability of Healthcare Data Alphafold 2 Transformer-Based Language Models and GPT3 The Future of Pharmacovigilance Conclusion References 23: Evolving Opportunities and Challenges for Patients in Clinical Research Introduction Patient Engagement Efforts Supporting Patient Engagement Strategies and Best Practices Citizen Scientist Information Behavior and Health Literacy Information Fields Health Literacy Social Environment for Patients The Role of Third Parties in Information Seeking Self-Help and Advocacy Evolving Medicine and Immersive Technologies in Clinical Research Direct-to-Consumer Testing and Data Collection for Research Crowdsourcing Mobile Health (Digital Health) Clinical Trial Involvement Augmented and Virtual Reality Consumers’ Relationships with Their Own Data Conclusions References 24: Apps in Clinical Research Introduction Operational Support Recruitment and Participant Management Data Capture Participant Generated Data Clinician Generated Data Apps as the Intervention Building Apps Standards for App Development SMART on FHIR Content Standards Development Tools and Hosting Platforms Security, Sharing, and Privacy Considerations Security Guidance Open and Closed Data The Ecosystem of Apps and Electronic Health Records Shareability and Data Ownership Future Directions References 25: Future Directions in Clinical Research Informatics Emergence of CRI Discipline Supporting Clinical and Translational Research Initiatives, Policy, and Regulatory Trends in CRI Role of CRI in Learning Health Systems: Data and Knowledge Management, Evidence Generation, and Quality Improvement Multidisciplinary Collaboration is an Essential Feature of CRI Challenges and Opportunities for CRI Training and Workforce Needs Conclusion References Index