Oncology Informatics

Using Health Information Technology to Improve Processes and Outcomes in Cancer

Bradford W. Hesse, David Ahern, Ellen Beckjord

• Publisher: Academic Press
• Year: 2016
• ISBN:
  9780128022009
• Topic: Computer Science
• Subtopic: Bioinformatics
• Index: Computer Science

Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer Care encapsulates National Cancer Institute-collected evidence into a format that is optimally useful for hospital planners, physicians, researcher, and informaticians alike as they collectively strive to accelerate progress against cancer using informatics tools.

This book is a formational guide for turning clinical systems into engines of discovery as well as a translational guide for moving evidence into practice. It 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, which can lead to fatal consequences downstream. By infusing the best science across disciplines, this book creates new environments of "Smart and Connected Health."

Oncology Informatics is also a policy guide in an era of extensive reform in healthcare settings, including new incentives for healthcare providers to demonstrate "meaningful use" of these technologies to improve system safety, engage patients, ensure continuity of care, enable population health, and protect privacy. Oncology Informatics acknowledges this extraordinary turn of events and offers practical guidance for meeting meaningful use requirements in the service of improved cancer care.

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.

• 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

Part I

An Extraordinary Opportunity

Outline

Chapter 1 Creating a Learning Health Care System in Oncology

Chapter 2 Reducing Cancer Disparities Through Community Engagement: The Promise of Informatics

Chapter 3 Cancer Clinical Research: Enhancing Data Liquidity and Data Altruism

Chapter 4 Engaging Patients in Primary and Specialty Care

Chapter 5 Coordination at the Point of Need

Chapter 1

Creating a Learning Health Care System in Oncology

Richard L. Schilsky, MD, FACP, FASCO and Robert S. Miller, MD, FACP, FASCO, CancerLinQ, American Society of Clinical Oncology, Alexandria, VA, United States

Abstract

A series of reports by the Institute of Medicine (IOM) of the National Academies has called for the creation of a rapid learning health care system to improve clinical care. The IOM has expressed the view that a learning health care system supports patient–clinician interactions by providing patients and clinicians with the information and tools necessary to make well-informed medical decisions. It plays an integral role in developing the evidence base that supports clinical decisions by capturing data from real-world care settings that researchers can then analyze to generate new hypotheses and insights. Further, it can be used to deliver point of care education and to collect and report quality metrics, implement performance improvement initiatives, and allow payers to identify and reward high-quality care. The American Society of Clinical Oncology has embraced this concept and is bringing it to fruition with the creation of the Cancer Learning Intelligence Network for Quality (CancerLinQ), a physician-led quality improvement initiative designed as an oncology rapid learning health care system. By capturing the longitudinal electronic health record of every patient along with practice management data from participating practices, CancerLinQ harnesses the power of Big Data to “learn” from every patient. CancerLinQ meets the need for more effective, adaptable, and comprehensive quality improvement tools at the point of care. It compares the process and outcomes of care against the best standards available to rapidly feed information back to practices on the quality of care achieved. CancerLinQ, by capturing the longitudinal record of each patient’s experience, will enable insights that are more broadly applicable and more clinically nuanced than can be determined from clinical trials, registries, or claims data sets and that can be generated in far less time by posing queries to an enormous database containing millions of patient records. Over time, the value of the CancerLinQ data will be further enhanced by incorporating genomic data, imaging data, and patient-reported outcomes that will be collected via a patient portal to be included in future versions of the system. Linkage of the CancerLinQ data to insurance claims data and cancer registry data in the future will provide a rich portrait of the characteristics, experiences, preferences, goals, outcomes, and resource utilization of every cancer patient and provide the ability to rapidly generate the evidence needed to deliver the best care for each cancer patient.

Keywords

Rapid learning health care system; CancerLinQ; quality improvement; clinical trials; cancer registry; clinical decision support

Outline

1.1 The Challenges of Delivering Quality Cancer Care 4

1.1.1 Diversity of Cancer and the Cancer Patient Population 5

1.1.2 The Need for Multidisciplinary Cancer Care 5

1.1.3 The Complexity and Cost of Cancer Care Delivery 6

1.2 Overview of Traditional Learning in Cancer Medicine 7

1.2.1 Clinical Trials as the Foundation of Evidence-Based Medicine in Cancer 7

1.2.2 Limitations of Conventional Clinical Trials 8

1.3 The Interface of Quality, Value, and Learning 9

1.4 ASCO’s Vision for a Rapid Learning System in Oncology: CancerLinQ 10

1.5 CancerLinQ Data Architecture 10

1.6 History and Current Status of CancerLinQ Implementation 12

1.6.1 Background and Prototype Development 2011–12 12

1.6.2 CancerLinQ Development 2013–15 12

1.6.3 CancerLinQ and SAP 13

1.7 CancerLinQ Solution Operating Characteristics 13

1.7.1 Data Ingestion 13

1.7.2 CancerLinQ Portal 14

1.7.3 User Types 14

1.7.4 Quality Benchmarking 14

1.7.5 Clinical Decision Support 14

1.7.6 Other Secondary Uses of Deidentified and/or Limited Data Sets 16

1.8 Regulatory Underpinnings of CancerLinQ 17

1.8.1 Data Governance Guiding Principles and Policies 18

1.9 Summary and Conclusions 19

List of Acronyms and Abbreviations 19

Acknowledgments 19

References 19

1.1 The Challenges of Delivering Quality Cancer Care

More than 1.6 million Americans are diagnosed with cancer each year [1]. By 2030, the incidence of cancer in the United States will rise to 2.3 million per year [2]. Some 14 million people in the United States are cancer survivors, and that number will rise to 18 million by 2030 [1]. These cancer patients and survivors have complex medical and psychosocial needs. They rely on multiple health care specialists, often practicing in different care settings, to apply an ever changing body of scientific evidence about the best way to manage their disease. Not surprisingly then, cancer care providers are increasingly challenged to deliver high-quality care at a time when the burden of cancer is growing, the treatments options are expanding but costly, and health care systems are demanding high value treatments.

The Institute of Medicine (IOM) of the US National Academies and other organizations has addressed these issues in a series of workshops and committee reports extending over many years. In 1999, the National Cancer Policy Board (NCPB) described quality care as “providing patients with appropriate services in a technically competent manner, with good communication, shared decision making, and cultural sensitivity” [3]. The NCPB provided a series of recommendations to achieve quality cancer care including that:

patients undergoing technically complex procedures receive care in highly experienced centers;

care be guided by systematically developed guidelines based on the best available evidence;

efforts be made to measure and monitor the quality of care delivered using a core set of quality measures;

health care systems, health plans, and physicians be held accountable for delivering high-quality care; and

each individual with cancer receives treatment recommendations from experienced professionals; a care plan that clearly outlines the goals of care; and access to all necessary resources to implement the care plan, including access to clinical trials, a mechanism to coordinate care among necessary medical specialists, and psychosocial and other supportive care services—particularly management of cancer-related pain and timely referral to palliative care specialists and hospice services.

The NCPB also stressed the need for those patients without insurance to have equitable access to the cancer care system and called for the development of a “cancer data system” to provide quality benchmarks to providers.

In 2006, the American Society of Clinical Oncology (ASCO) and the European Society of Medical Oncology (ESMO) jointly issued a consensus statement on quality cancer care [4]. This statement incorporated many of the key recommendations of the NCPB report, including that:

patients have access to information about their illness, possible interventions, and the known benefits and risks of treatment options;

patients have access to their medical records;

access to care be provided without discrimination;

patients be empowered to participate in decision making about their treatment and have access to a multidisciplinary care team that comprises all appropriate oncology specialists as well as palliative care experts; and

patients be offered the opportunity to participate in clinical trials.

The consensus statement called for survivorship care planning as an element of quality cancer care and stressed the importance of pain management, palliative care, and end-of-life discussions in the management of all cancer patients.

In September 2013, the IOM released a seminal report entitled “Delivering High-Quality Cancer Care: Charting a New Course for a System in Crisis” [5]. The report identifies six components of a cancer care delivery system that are integral to achieving high-quality care for all cancer patients:

engaged patients and patient-centered communication;

interprofessional cancer care teams integrated with noncancer care teams and caregivers;

evidence-based care, including clinical trials and comparative effectiveness research (CER);

a learning health information technology (HIT) system for cancer;

translation of evidence into clinical practice, quality measurement, and performance improvement; and

affordable care accessible to all patients.

The report highlights the interconnectivity of these six components. To bring about the new system, the report recommends that a “learning health care information technology system” be developed that enables real-time analysis of data from cancer patients in a variety of care settings. The IOM committee expressed the view that a learning health care system supports patient–clinician interactions by providing patients and clinicians with the information and tools necessary to make well-informed medical decisions. It plays an integral role in developing the evidence base that supports clinical decisions by capturing data from real-world care settings that researchers can then analyze to generate new hypotheses and insights. Further, it can be used to deliver point of care education and to collect and report quality metrics, implement performance improvement initiatives, and allow payers to identify and reward high-quality care. A key recommendation of the committee was that “Professional organizations should design and implement the digital infrastructure and analytics necessary to enable continuous learning in cancer care.”

1.1.1 Diversity of Cancer and the Cancer Patient Population

Successful implementation of these many facets of quality cancer care is often challenging in view of the enormous diversity of the cancer patient population, the complexity and risks of contemporary cancer treatment, the need to coordinate care among the many specialists who comprise the multidisciplinary cancer care team, and the fragmented health care delivery system in the United States. Recent recognition of the biological, spatial, and temporal diversity of cancer further complicates clinical evaluation and management of the cancer patient and challenges physicians to assimilate more information than ever before.

Oncologists have recognized for many years that cancer is not a single disease but it has only been recently that the enormous biological diversity of cancer has been revealed through sophisticated molecular profiling studies of human tumors [6]. It is probable that no two cancers are alike in their genomic and proteomic profiles or microenvironment. This biological heterogeneity gives rise to cancers that, while histologically similar, vary in clinical pre...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• List of Contributors
• Foreword
• Preface
• Introduction
• Part I: An Extraordinary Opportunity
• Part II: Support Across the Continuum
• Part III: Science of Oncology Informatics
• Part IV: Accelerating Progress
• Glossary
• Index