ONCOLOGY INFORMATICS. USING HEALTH INFORMATION TECHNOLOGY TO IMPROVE PROCESSES AND OUTCOMES IN CANCER

- Introduction
I.1 Why This Book Now?
I.2 The Purpose of This Book
I.3 Organization of the Book
I.4 Conclusion
List of Acronyms and Abbreviations
References
- Part I: An Extraordinary Opportunity
• Chapter 1. Creating a Learning Health Care System in Oncology
Abstract
1.1 The Challenges of Delivering Quality Cancer Care
1.2 Overview of Traditional Learning in Cancer Medicine
1.3 The Interface of Quality, Value, and Learning
1.4 ASCO’s Vision for a Rapid Learning System in Oncology: CancerLinQ
1.5 CancerLinQ Data Architecture
1.6 History and Current Status of CancerLinQ Implementation
1.7 CancerLinQ Solution Operating Characteristics
1.8 Regulatory Underpinnings of CancerLinQ
1.9 Summary and Conclusions
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 2. Reducing Cancer Disparities Through Community Engagement: The Promise of Informatics
Abstract
Section 1: Public Health Informatics: Implications on Cancer Health Disparities
Section 2: CBPR to Inform the Practice of PHIS to Address Health Disparities
Section 3: Examples of Public Health and Health Informatics
Section 4: Discussion
List of Acronyms and Abbreviations
References
• Chapter 3. Cancer Clinical Research: Enhancing Data Liquidity and Data Altruism
Abstract
3.1 Drivers
3.2 Data Liquidity
3.3 Data Science
3.4 Data Access
3.5 Translational Research
3.6 Capturing the Patient Experience Across the Continuum of Care
3.7 Empowering the Patient as a Full Participant in Research
3.8 Building a National Learning Health Care System for Cancer
3.9 Incentives for Data Sharing
3.10 Adaptive Instruments
3.11 Ontologies and Workflow Systems
3.12 Connected Health and Mobile Technologies
3.13 A Focus on Lowering Barriers to Data Access for Cancer Research
3.14 Precision Medicine Drivers
List of Acronyms and Abbreviations
References
• Chapter 4. Engaging Patients in Primary and Specialty Care
Abstract
4.1 Introduction
4.2 Overview of Hit Tools to Engage Patients
4.3 Key Patient Engagement Activities
4.4 HIT Implementation to Promote Patient Engagement
4.5 Conclusions
List of Acronyms and Abbreviations
References
• Chapter 5. Coordination at the Point of Need
Abstract
5.1 Introduction
5.2 Frameworks for Care Coordination
5.3 HIT Functions for Care Coordination
5.4 Current Efforts in Informatics and Coordination at the Point of Need
5.5 Opportunities for Oncology Informatics at the Point of Need
List of Acronyms and Abbreviations
References
- Part II: Support Across the Continuum
• Chapter 6. Prevention, Information Technology, and Cancer
Abstract
6.1 Overview
6.2 Key Behaviors of Interest for the Prevention of Cancer
6.3 Current Use of Information Technology for Cancer Prevention
6.4 Electronic Health Records
6.5 Mobile, Web, and Wearable Applications
6.6 Summary and Future Directions
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 7. Early Detection in the Age of Information Technology
Abstract
7.1 Introduction
7.2 Epidemiology
7.3 Early Detection
7.4 The Process of Care
7.5 The Challenge of Early Detection
7.6 Communication Challenges
7.7 Evidence-Based Solutions
7.8 Challenges with Using IT
7.9 Overcoming Disparities
7.10 Looking to the Future
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 8. Informatics Support Across the Cancer Continuum: Treatment
Abstract
8.1 Overview
8.2 Data Aggregation
8.3 Data Optimization
8.4 Patient Insights to Optimize Care
8.5 Genomics in the Care Continuum
8.6 The Tooling Required
8.7 Novel Organizational Approaches
8.8 Summary
List of Acronyms and Abbreviations
References
• Chapter 9. Survivorship
Abstract
9.1 Cancer Survivorship
9.2 Challenges in Survivorship
9.3 Opportunities for Informatics-Based Solutions
9.4 Envisioning a Future State
9.5 Conclusions
List of Acronyms and Abbreviations
References
• Chapter 10. Advanced Cancer: Palliative, End of Life, and Bereavement Care
Abstract
10.1 Introduction
10.2 Opportunities for eHealth to Address Needs in Advanced Cancer Care
10.3 A Case Example: CHESS—The Comprehensive Health Enhancement Support System
10.4 Future Directions for Research
10.5 Future Directions for Development and Implementation
10.6 Conclusion
List of Acronyms and Abbreviations
Acknowledgments
References
- Part III: Science of Oncology Informatics
• Chapter 11. Data Visualization Tools for Investigating Health Services Utilization Among Cancer Patients
Abstract
11.1 Introduction
11.2 Methods and Data Visualization Tools
11.3 Applications of Data Visualization in the Cancer Setting
11.4 Case Studies
11.5 Conclusion
List of Acronyms and Abbreviations
References
• Chapter 12. Oncology Informatics: Behavioral and Psychological Sciences
Abstract
12.1 Introduction
12.2 Role of Behavioral/Psychological Science in Advancing Informatics
12.3 Definition and Role of Behavioral Informatics
12.4 Applications of Behavioral Informatics Resources and Tools in Cancer Care
12.5 Conclusions
List of Acronyms and Abbreviations
References
• Chapter 13. Communication Science: Connecting Systems for Health
Abstract
13.1 The Communication Revolution
13.2 Using Communication Science to Improve Quality of Cancer Care
13.3 A Functional Approach to Patient-Centered Communication
13.4 Conclusion
List of Acronyms and Abbreviations
References
• Chapter 14. Cancer Surveillance Informatics
Abstract
14.1 Background on Cancer Surveillance
14.2 Current Status and Opportunities for Informatics in Cancer Surveillance: NLP, Automation, and Linkages
14.3 New Areas for Cancer Surveillance Supported Through Informatics
14.4 Conclusion
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 15. Extended Vision for Oncology: A Perceptual Science Perspective on Data Visualization and Medical Imaging
Abstract
15.1 Introduction
15.2 How Vision Works (and How It Can Fail)
15.3 Visualization and Data Exploration
15.4 Example 1: Human Number Perception and Quantitative Data
15.5 Example 2: Visual Attention and Medical Images
15.6 Frontiers of Perceptual Science and Oncology Informatics
15.7 Conclusions
List of Acronyms and Abbreviations
References
- Part IV: Accelerating Progress
• Chapter 16. Crowdsourcing Advancements in Health Care Research: Applications for Cancer Treatment Discoveries
Abstract
16.1 Introduction
16.2 The Crowdsourcing Concept
16.3 Crowdsourcing in Health Care
16.4 Methodological and Ethical Issues in Crowdsourcing
16.5 The Future of Crowdsourcing
16.6 Conclusion
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 17. Patient-Centered Approaches to Improving Clinical Trials for Cancer
Abstract
17.1 While Trials Are Important, Trial Participation Rates Are Dismal
17.2 Barriers to Trial Participation
17.3 Patients Searching for Trials Online
17.4 Current Use of the Internet and Online Cancer Communities
17.5 How the Internet Shaped Online Cancer Communities, and Vice Versa
17.6 What Do Patients Talk About in Online Cancer Communities?
17.7 Impact of Online Patient Communities
17.8 Participatory Research
17.9 Borrowing From Silicon Valley and User-Centered Design
17.10 Participatory Clinical Trial Design
17.11 What If Trial Participants Discuss the Trial in Online Communities?
17.12 Challenges and Next Steps
List of Acronyms and Abbreviations
References
• Chapter 18. A New Era of Clinical Research Methods in a Data-Rich Environment
Abstract
18.1 Transition From a Data-Poor to Data-Rich Science
18.2 Traditional Trial Designs: What’s Wrong With Continuing to Do What We Do?
18.3 Data-Rich Biomedical and Behavioral Research Environment
18.4 The Vision of a Data-Rich Biomedical and Behavioral Research Enterprise
18.5 Recent Advances That Poise Clinical Research to Become a Data-Rich Research Enterprise
18.6 New Approaches to Treatment Testing in Preparation for a Comprehensive Health Data Research Infrastructure
18.7 Conclusion
List of Acronyms and Abbreviations
References
• Chapter 19. Creating a Health Information Technology Infrastructure to Support Comparative Effectiveness Research in Cancer
Abstract
19.1 Introduction
19.2 Cancer CER: Gaps and Opportunities
19.3 Health Information Technology Infrastructure for Improving CER
19.4 Enhancing the Collection of Evidence to Inform Patient Care and CER Through Distance Medicine Technology
19.5 Distance Medicine Technology in Cancer Care and Research
19.6 CYCORE: CYberinfrastructure for Cancer COmparative Effectiveness REsearch
19.7 Application of CYCORE Across the Cancer Prevention and Control Continuum to Accelerate CER
19.8 Strengthening the Capacity of the CER Infrastructure Through EHRs
19.9 Conclusion
List of Acronyms and Abbreviations
Acknowledgments
References
• Chapter 20. Editors’ Conclusion: Building for Change
Abstract
20.1 Introduction
20.2 The Bright Spots in Oncology Informatics
20.3 Living in a Post HITECH World
20.4 Building the Future Together
20.5 Conclusion
List of Acronyms and Abbreviations
References
- Glossary
- Index

Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer Care encapsulates NCI-collected evidence into a format that is optimally useful for hospital planners, physicians, researchers, and informaticians alike as they collectively strive to accelerate progress against cancer using informatics tools. Anyone who wishes to take full advantage of the health information revolution in oncology to accelerate successes against cancer will find the information in this book valuable. It is a translational guide for moving evidence into practice, and meets recommendations from the national Academies of Science to reorient the research portfolio toward providing greater cognitive support for physicians, patients, and their caregivers to improve patient outcomes. Data from systems studies have suggested that oncology and primary care systems are prone to errors of omission that can lead to fatal consequences downstream. By infusing the best science across disciplines, this book creates new environments of smart and connected health and acts as a formational guide for turning clinical systems into engines of discovery. Following recommendations from the IOM’s Roundtable on Evidence-Based Medicine, the authors encapsulate best practice for creating a Learning Healthcare System in oncology.

Features:
• Presents a pragmatic perspective for practitioners and allied health care professionals on how to implement Health I.T. solutions in a way that will minimize disruption while optimizing practice goals
• Proposes evidence-based guidelines for designers on how to create system interfaces that are easy to use, efficacious, and timesaving
• Offers insight for researchers into the ways in which informatics tools in oncology can be utilized to shorten the distance between discovery and practice

Authors
• Bradford W. Hesse , Chief, NCI’s Health Communication and Informatics Research Branch.
• David Ahern, Professor of Psychology, Harvard Medical School, Director of the Program in Behavioral Informatics and eHealth, Bringham and Womens Hospital, Boston, MA.
• Ellen Beckjord, Assistant Professor of Biobehavioral Medicine in Oncology Program, University of Pittsburgh Cancer Institute