Clinical Research Informatics

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