Publications of the NAM Leadership Consortium
The effective use of data is foundational to the concept of a learning health system—one that leverages and shares data to learn from every patient experience, and feeds the results back to clinicians, patients and families, and health care executives to transform health, health care, and health equity. More than ever, the American health care system is in a position to harness new technologies and new data sources to improve individual and population health.
Learning health systems are driven by multiple stakeholders—patients, clinicians and clinical teams, health care organizations, academic institutions, government, industry, and payers. Each stakeholder group has its own sources of data, its own priorities, and its own goals and needs with respect to sharing that data. However, in America’s current health system, these stakeholders operate in silos without a clear understanding of the motivations and priorities of other groups. The three stakeholder working groups that served as the authors of this Special Publication identified many cultural, ethical, regulatory, and financial barriers to greater data sharing, linkage, and use. What emerged was the foundational role of trust in achieving the full vision of a learning health system.
This Special Publication outlines a number of potentially valuable policy changes and actions that will help drive toward effective, efficient, and ethical data sharing, including more compelling and widespread communication efforts to improve awareness, understanding, and participation in data sharing. Achieving the vision of a learning health system will require eliminating the artificial boundaries that exist today among patient care, health system improvement, and research. Breaking down these barriers will require an unrelenting commitment across multiple stakeholders toward a shared goal of better, more equitable health. We can improve together by sharing and using data in ways that produce trust and respect. Patients and families deserve nothing less.
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril
The emergence of artificial intelligence (AI) in health care offers unprecedented opportunities to improve patient and clinical team outcomes, reduce costs, and impact population health. While there have been a number of promising examples of AI applications in health care, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or futher exacerbation of existing health- and technology-driven disparities.
This Special Publication synthesizes current knowledge to offer a reference document for relevant health care stakeholders. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.
AI is poised to make transformative and disruptive advances in health care, but it is prudent to balance the need for thoughtful, inclusive health care AI that plans for and actively manages and reduces potential unintended consequences, while not yielding to marketing hype and profit motives.
Evidence-based medicine arose from a clear need and represents a major advance in the science of clinical decision-making. Despite broad acceptance of evidence-based medicine, however, a fundamental issue remains unresolved: evidence is derived from groups of people, yet medical decisions are made by and for individuals. Despite persistent assertions from clinicians that determining the best therapy for each patient is a more complicated endeavor than just picking the best treatment on average, traditional approaches have been overly reliant on the average effects estimated from the outcomes of clinical trials.
This Special Publication is based on a workshop, held by the NAM, that considered patient and stakeholder perspectives on the importance of understanding heterogeneous treatment effects (HTE) and best practices for implementing clinical programs that take HTE into account. For evidence to be more applicable to individual patients, we need to combine methods for strong causal inference (first and foremost, randomization) with methods for prediction that permit inferences about which particular patients are likely to benefit and which are not. Better population-based outcomes will only be realized when we understand more completely how to treat patients as the unique individuals they are.
The Future of Health Services Research: Advancing Health Systems Research and Practice in the United States
Health services research is “the multidisciplinary field of scientific investigation that studies how social factors, financing systems, organizational structures and processes, health technologies, and personal behaviors affect access to health care and the quality and cost of health care.” Ironically, at a time in which appreciation has never been higher for both the need and potential from health services research, the political and financial support for sustenance and growth appear to be weakening.
Now is a critical time for the field to articulate its priorities, demonstrate its utility, and transform to meet the needs of a 21st-century health care system. The physical and financial health of the nation is at stake.
Realizing the promise of digital technology will depend on the ability to share information across time and space from multiple devices, sources, systems, and organizations. The major barrier to progress is not technical; rather, it is in the failure of organizational demand and purchasing requirements. Better procurement practices, supported by compatible interoperability platforms and architecture, will allow for better, safer patient care; reduced administrative workload for clinicians; protection from cybersecurity attacks; and significant financial savings across multiple markets.
Faster progress toward interoperability is both essential and possible – and is an organizational obligation that must be acted on now.
To advance insights and perspectives on how to better manage the care of the high-need patient population, the National Academy of Medicine, with guidance from an expert planning committee, was tasked with convening three workshops held between July 2015 and October 2016 and summarizing the presentations, discussions, and the relevant literature.
Improving care for high-need patients is not only possible–it also contributes to a more sustainable health system. But progress will take a coordinated effort from policy makers, payers, providers, and researchers, as well as patients and their loved ones.
The Learning Health System Series
To facilitate progress toward the development of a learning health system — in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience — the NAM Leadership Consortium has marshaled the insights of the nation’s leading experts to explore in detail the prospects, and the necessity, for transformational change in the fundamental elements of health and health care. The assessments are reported in the 15 volumes of the Learning Health System Series, below.
The Learning Healthcare System, the first in the series, explores the various dimensions — evidence development and standards, care culture, system design and operation, health data, clinical research, information technology, value — on which emerging insights and scientific advances can be applied for health care in which both evidence development and application flow seamlessly and continuously in the course of care.
Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good identifies the transformational prospects for large interoperable clinical and administrative datasets to allow real-time discovery on issues ranging from disease etiology to personalized diagnosis and treatment. Also explored are key priorities for data stewardship if clinical data are to be a carefully nurtured resource for continuous learning and better care.
Learning What Works: Infrastructure Required for Comparative Effectiveness Research assesses the nature and magnitude of needed capacity for new knowledge and evidence about what care works best under different circumstances, including the necessary skills and workforce, data linkage and improvement, study coordination and results dissemination, and research methods innovation.
Engineering a Learning Healthcare System: A Look at the Future reviews transferable lessons from the systems and operations engineering sciences applicable for improving the organization, structure, and function of the delivery, monitoring and change processes in health care — in effect, engineering approaches to continuous feedback and improvement on quality, safety, knowledge, and value in health care.
Evidence-Based Medicine and the Changing Nature of Health Care explores the forces, such as genetic insights and increasing care complexity, driving the need for better medical evidence; the challenges with which patients and providers must contend; the need to transform the speed and reliability of new medical evidence; and the legislative and policy changes that could enable evolution of an evidence-based, learning system.
Redesigning the Clinical Effectiveness Research Paradigm: Innovation and Practice-Based Approaches reviews the growing scope and scale of the need for clinical effectiveness research alternatives, the limits of current approaches, the potential for emerging research and data networks, innovative study designs, and new methods of analysis and modeling.
Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care explores current efforts and opportunities to accelerate progress in improving health and health care, and identifies priority follow-up action targets: technical innovation; data and research insights; patient and public engagement; and stewardship and governance.
Patients & the Public
Patients Charting the Course: Citizen Engagement and the Learning Health System assesses the prospects for improving health and lowering costs by advancing patient involvement in the elements of a learning health system, and underscores the centrality of communication strategies that account for and engage individual perspectives, needs, preferences, understanding, and support necessary to mobilize change.
Cost & Outcomes
The Healthcare Imperative: Lowering Costs and Improving Outcomes presents a 6-domain framework for understanding and estimating excess healthcare costs: unnecessary services, inefficiently delivered services, excessive administrative costs, prices that are too high, missed prevention opportunities, and medical fraud. Additionally, the volume summarizes estimates of the excessive costs, reviews approaches to their control, and considers ways to reduce health expenditures by 10% within 10 years, without compromising health status or valued innovation.
Digital Data Improvement Priorities for Continuous Learning in Health and Health Care presents the current deficiencies in the reliability, availability, and usability of digital health data and considers strategies, priorities, and responsibilities to address such deficiencies, as the totality of available health data is a crucial resource that should be considered an invaluable public asset in the pursuit of better care, improved health, and lower health care costs.
Resource: Workshop Highlights
Core Measurement Needs for Better Care, Better Health, and Lower Costs: Counting What Counts considers needs, approaches, and metrics most important for tracing progress on care that is better quality, lower cost, and yields better health outcomes, and accounts for factors influencing the implementation of core measure sets, including the data infrastructure, resources, and policies that are needed for the use of core metrics across independent organizations and providers.
Large Simple Trials
Large Simple Trials and Knowledge Generation in a Learning Health System presents the pros and cons of the design characteristics of large simple trials (LSTs), explores the utility of LSTs on the basis of case studies of past successes, and considers the challenges and opportunities for accelerating the use of LSTs in the context of a U.S. clinical trials enterprise.
Value in Health Care: Accounting for Cost, Quality, Safety, Outcomes, and Innovation explores alternative perspectives and approaches for defining, estimating, and attaining value in health care, including case studies on value-enhancing strategies in development—e.g. value-based insurance design, accountable care organizations— and emphasizing the basic need for broad transparency as to cost, quality, and outcomes in care.
Leadership Commitments to Improve Value in Healthcare: Finding Common Ground presents discussions of opportunity statements from those in key health stakeholder sectors—patients, clinicians, health organizations, insurers, product manufacturers, employers, government, IT, and researchers—on priority actions they can and will undertake cooperatively to transform quality and value in health care.
Observational Studies in a Learning Health System reviews leading approaches to observational studies and how to chart the course for the use of this growing utility in the most responsible fashion possible by considering how they can be made more rigorous and internally valid, how to deal with bias, the use of observational studies to generalize findings from randomized controlled trials, and how to evaluate treatment heterogeneity.
As explored in Integrating Research and Practice: Health System Leaders Working Toward High-Value Care, health care has been called one of the most complex sectors of the U.S. economy. Driven largely by robust innovation in treatments and interventions, this complexity has created an increased need for evidence about what works best for whom in order to inform decisions that lead to safe, efficient, effective, and affordable care. As health care becomes more digital, clinical datasets are becoming larger and more numerous.
Partnering with Patients
Empowering patients and families to become active partners in their health care — not merely passive participants — is a critical step on the road to achieving the best care at lower cost. Yet the changes necessary to transform the patient role are significant. Encouraging patient engagement in care decisions, value, and research is crucial to achieving better care, improved health, and lower health care costs. This publication details discussions during the February 2013 IOM workshop, sponsored by the Gordon and Betty Moore Foundation and the Blue Shield of California Foundation. The event gathered patients and experts in areas such as decision science, evidence generation, communication strategies, and health economics to consider the central roles patients can play to bring about progress in all facets of the U.S. health care system. The workshop built on the ongoing work of the Roundtable on Value & Science-Driven Health Care to raise awareness and demand from patients and families for better care at lower costs and to create a health care system that learns and improves continuously.
Below are just a sample of the many discussion papers and commentaries published by members of the NAM Leadership Consortium. To browse all NAM Perspectives, visit nam.edu/Perspectives.
- Patient and Family Engaged Care: An Essential Element of Health Equity
- Individual Patient-Level Data Sharing for Continuous Learning: A Strategy for Trial Data Sharing
- Clinician Engagement for Continuous Learning
- Harnessing Evidence and Experience to Change Culture: A Guiding Framework for Patient and Family Engaged Care
- Generating Knowledge from Best Care: Advancing the Continuously Learning Health System
- Observations from the Field: Reporting Quality Metrics in Health Care
- Sustainable Success in Accountable Care
- Social Networking Sites and the Continuously Learning Health System: A Survey
- Return on Information: A Standard Model for Assessing Institutional Return on Electronic Health Records
- Bringing a Systems Approach to Health
- From Pilots to Practice: Speeding the Movement of Successful Pilots to Effective Practice
- Making the Case for Continuous Learning from Routinely Collected Data
- Harmonizing Reporting on Potential Conflicts of Interest: A Common Disclosure Process for Health Care and Life Sciences
- Core Principles & Values of Effective Team-Based Health Care
- Communicating with Patients on Health Care Evidence
- Data Altruism: Honoring Patients’ Expectations for Continuous Learning
- Value-Based Care: Learnings to Shape the Future of Health Care
- Promoting Rigorous Interdisciplinary Research and Building an Evidence Base to Inform Health Care Learning, Practice, and Policy
- Treating a Chronic Condition: Efforts to Reduce Avoidable Readmissions at U.S. Hospitals
- Forging Collaboration Within Academia and Between Academia and Health Care Delivery Organizations: Importance, Successes, and Future Work
- A Continuously Learning Health System in the United States
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America explores the central challenges to health care today and identifies three major imperatives for change: the rising complexity of modern health care, unsustainable cost increases, and outcomes below the system’s potential, and points out that emerging tools like computing power, connectivity, team-based care, and systems engineering techniques—tools that were previously unavailable—make the envisioned transition possible, and are already being put to successful use in pioneering health care organizations.
Vital Signs: Core Metrics for Health and Health Care Progress
Vital Signs: Core Metrics for Health and Health Care Progress confronts the challenges of the thousands of measures in use today to assess health and health care in the United States. Although many of these measures provide useful information, their sheer number, as well as their lack of focus, consistency, and organization, limits their overall effectiveness in improving performance of the health system. To achieve better health at lower cost, all stakeholders — including health professionals, payers, policy makers, and members of the public — must be alert to the measures that matter most. What are the core measures that will yield the clearest understanding and focus on better health and well-being for Americans?
Transforming Health Care Scheduling and Access: Getting to Now
According to Transforming Health Care Scheduling and Access: Getting to Now, long waits for treatment are a function of the disjointed manner in which most health systems have evolved to accommodate the needs and the desires of doctors and administrators, rather than those of patients. The result is a health care system that deploys its most valuable resource — highly trained personnel — inefficiently. The report offers preliminary observations about emerging best practices and promising strategies (including virtually immediate engagement), concluding that opportunities exist to implement those practices and strategies, and it presents recommendations for needed approaches, policies, and leadership.