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It is essential for patients and clinicians to have the resources
needed to make informed, collaborative care decisions. Despite this
need, only a small fraction of health-related expenditures in the
United States have been devoted to comparative effectiveness
research (CER). To improve the effectiveness and value of the care
delivered, the nation needs to build its capacity for ongoing study
and monitoring of the relative effectiveness of clinical
interventions and care processes through expanded trials and
studies, systematic reviews, innovative research strategies, and
clinical registries, as well as improving its ability to apply what
is learned from such study through the translation and provision of
information and decision support. As part of its Learning Health
System series of workshops, the Institute of Medicine's (IOM's)
Roundtable on Value & Science-Driven Health Care hosted a
workshop to discuss capacity priorities to build the evidence base
necessary for care that is more effective and delivers higher value
for patients. Learning What Works summarizes the proceedings of the
seventh workshop in the Learning Health System series. This
workshop focused on the infrastructure needs-including methods,
coordination capacities, data resources and linkages, and
workforce-for developing an expanded and efficient national
capacity for CER. Learning What Works also assesses the current and
needed capacity to expand and improve this work, and identifies
priority next steps. Learning What Works is a valuable resource for
health care professionals, as well as health care policy makers.
Table of Contents Front Matter Summary 1 The Need and Potential
Returns for Comparative Effectiveness Research 2 The Work Required
3 The Information Networks Required 4 The Talent Required 5
Implementation Priorities 6 Moving Forward Appendix A: Learning
What Works Best: The Nation's Need for Evidence on Comparative
Effectiveness in Health Care Appendix B: Comparative Effectiveness
Studies Inventory Project Appendix C: Comparative Effectiveness
Research Priorities: IOM Recommendations (2009) Appendix D:
Comparative Effectiveness Research Priorities: FCCCER
Recommendations (2009) Appendix E: Affordable Care Act (ACA) (2010)
Provisions for the Patient-Centered Outcomes Research Institute
(PCORI) Appendix F: Workshop Agenda Appendix G: Biographical
Sketches of Workshop Participants Appendix H: Workshop Attendee
List Other Publications in The Learning Health System Series
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. By realizing the potential of
knowledge generation that is more closely integrated with the
practice of care, it should be possible not only to produce more
usable evidence to inform decisions, but also to increase the
efficiency and decrease the costs of doing clinical research.
Patient-Centered Clinical Research Network, or PCORnet, is a
nation-wide patient-centered clinical research network intended to
form a resource of clinical, administrative, and patient data that
can be used to carry out observational and interventional research
studies and enhance the use of clinical data to advance the
learning health care system. The primary goal of the first phase of
PCORnet will be to establish the data infrastructure necessary to
do such research. In April and June 2014 the Institute of
Medicine's Roundtable on Value and Science-Driven Health Care
convened two workshops aimed at accelerating progress toward
real-time knowledge generation through the seamless integration of
clinical practice and research, one of the fundamental concepts of
a continuously learning health system, centered on the development
of the PCORnet. The first workshop brought together health care
system leaders, both administrative and clinical, and researchers
to consider issues and strategic priorities for building a
successful and durable clinical research network and facilitate
progress toward a continuously learning health care system more
broadly, including issues related to science, technology, ethics,
business, regulatory oversight, sustainability, and governance. The
second workshop focused on implementation approaches. Health system
CEOs convened to consider strategic priorities and explore
approaches to implementation. These workshops will inform the
decisions of field leaders moving forward, including PCORI, the
PCORnet steering committee, and PCORnet grantees. Integrating
Research and Practice is the summary of the presentations and
discussions of the workshops.
Like many other industries, health care is increasingly turning to
digital information and the use of electronic resources. The
Institute of Medicine's Roundtable on Value & Science-Driven
Health Care hosted three workshops to explore current efforts and
opportunities to accelerate progress in improving health and health
care with information technology systems. Table of Contents Front
Matter Synopsis and Highlights 1 Introduction 2 Visioning
Perspectives on the Digital Health Utility 3 Technical Issues for
the Digital Health Infrastructure 4 Engaging Patient and Population
Needs 5 Weaving a Strong Trust Fabric 6 Stewardship and Governance
in the Learning Health System 7 Perspectives on Innovation 8
Fostering the Global Dimension of the Health Data Trust 9 Growing
the Digital Health Infrastructure 10 Accelerating Progress Appendix
A: The Learning Health System and the Digital Health Utility
Appendix B: Case Studies for the Digital Health Infrastructure
Appendix C: Example Stakeholder Responsibilities and Opportunities
Appendix D: Summary Overview of Meaningful Use Objectives Appendix
E: PCAST Report Recommendations Appendix F: Workshop Agendas
Appendix G: Workshop Participants Other Publications in The
Learning Health System Series
Clinical research strains to keep up with the rapid and iterative
evolution of medical interventions, clinical practice innovation,
and the increasing demand for information on the clinical
effectiveness of these advancements. In response to the growing
availability of archived and real-time digital health data and the
opportunities this data provides for research, as well as the
increasing number of studies using prospectively collected clinical
data, the Institute of Medicine\'s Roundtable on Value &
Science-Driven Health Care convened a workshop on Observational
Studies in a Learning Health System. Participants, including
experts from a wide range of disciplines - clinical researchers,
statisticians, biostatisticians, epidemiologists, health care
informaticians, health care analytics, research funders, health
products industry, clinicians, payers, and regulators - explored
leading edge approaches to observational studies, charted a course
for the use of the growing health data utility, and identified
opportunities to advance progress. Workshop speakers and individual
participants strove to identify stakeholder needs and barriers to
the broader application of observational studies. Observational
Studies in a Learning Health Systemis the summary of the workshop.
This report explores the role of observational studies in the
generation of evidence to guide clinical and health policy
decisions. The report discusses concepts of rigorous observational
study design and analysis, emerging statistical methods, and
opportunities and challenges of observational studies to complement
evidence from experimental methods, treatment heterogeneity, and
effectiveness estimates tailored toward individual patients. Table
of Contents Front Matter 1 Introduction 2 Issues Overview for
Observational Studies in Clinical Research 3 Engaging the Issue of
Bias 4 Generalizing Randomized Clinical Trial Results to Broader
Populations 5 Detecting Treatment-Effect Heterogeneity 6 Predicting
Individual Responses 7 Strategies Going Forward 8 Common Themes for
Progress Appendix A: Biographies of Workshop Speakers Appendix B:
Workshop Agenda Appendix C: Workshop Participants
Improving our nation's healthcare system is a challenge which,
because of its scale and complexity, requires a creative approach
and input from many different fields of expertise. Lessons from
engineering have the potential to improve both the efficiency and
quality of healthcare delivery. The fundamental notion of a
high-performing healthcare system-one that increasingly is more
effective, more efficient, safer, and higher quality-is rooted in
continuous improvement principles that medicine shares with
engineering. As part of its Learning Health System series of
workshops, the Institute of Medicine's Roundtable on Value and
Science-Driven Health Care and the National Academy of Engineering,
hosted a workshop on lessons from systems and operations
engineering that could be applied to health care. Building on
previous work done in this area the workshop convened leading
engineering practitioners, health professionals, and scholars to
explore how the field might learn from and apply systems
engineering principles in the design of a learning healthcare
system. Engineering a Learning Healthcare System: A Look at the
Future: Workshop Summary focuses on current major healthcare system
challenges and what the field of engineering has to offer in the
redesign of the system toward a learning healthcare system. Table
of Contents Front Matter Summary 1 Engineering a Learning
Healthcare System 2 Engaging Complex Systems Through Engineering
Concepts 3 Healthcare System Complexities, Impediments, and
Failures 4 Case Studies in Transformation Through Systems
Engineering 5 Fostering Systems Change to Drive Continuous Learning
in Health Care 6 Next Steps: Aligning Policies with Leadership
Opportunities Appendixes Appendix A: Workshop Agenda Appendix B:
Biographical Sketches of Workshop Participants Appendix C: Workshop
Attendee List Other Publications in The Learning Health System
Series
Successful development of clinical data as an engine for knowledge
generation has the potential to transform health and health care in
America. As part of its Learning Health System Series, the
Roundtable on Value & Science-Driven Health Care hosted a
workshop to discuss expanding the access to and use of clinical
data as a foundation for care improvement. Table of Contents Front
Matter Summary 1 Clinical Data as the Basic Staple of the Learning
Health System 2 U.S. Healthcare Data Today: Current State of Play 3
Changing the Terms: Data System Transformation in Progress 4
Healthcare Data: Public Good or Private Property? 5 Healthcare Data
as a Public Good: Privacy and Security 6 Creating a Next-Generation
Data Utility: Building Blocks and the Action Agenda 7 Engaging the
Public 8 Clinical Data as the Basic Staple of Health Learning:
Ideas for Action Appendixes Appendix A: Workshop Agenda Appendix B:
Biographical Sketches of Workshop Participants Appendix C: Workshop
Attendee List Appendix D: The IOM Committee on Health Research and
the Privacy of Health Information: The HIPAA Privacy Rule Other
Publications in the Learning Healthcare System Series
Randomized clinical trials (RCTs) are often referred to as the
"gold standard" of clinical research. However, in its current
state, the U.S. clinical trials enterprise faces substantial
challenges to the efficient and effective conduct of research.
Streamlined approaches to RCTs, such as large simple trials (LSTs),
may provide opportunities for progress on these challenges.
Clinical trials support the development of new medical products and
the evaluation of existing products by generating knowledge about
safety and efficacy in pre- and post-marketing settings and serve
to inform medical decision making and medical product development.
Although well-designed and -implemented clinical trials can provide
robust evidence, a gap exists between the evidence needs of a
continuously learning health system, in which all medical decisions
are based on the best available evidence, and the reality, in which
the generation of timely and practical evidence faces significant
barriers. Large Simple Trials and Knowledge Generation in a
Learning Health System is the summary of a workshop convened by the
Institute of Medicine's Roundtable on Value & Science-Driven
Health Care and the Forum on Drug Discovery, Development, and
Translation. Experts from a wide range of disciplines-including
health information technology, research funding, clinical research
methods, statistics, patients, product development, medical product
regulation, and clinical outcomes research-met to marshal a better
understanding of the issues, options, and approaches to
accelerating the use of LSTs. This publication summarizes
discussions on the potential of LSTs to improve the speed and
practicality of knowledge generation for medical decision making
and medical product development, including efficacy and
effectiveness assessments, in a continuously learning health
system. Large Simple Trials and Knowledge Generation in a Learning
Health System explores acceleration of the use of LSTs to improve
the speed and practicality of knowledge generation for medical
decision making and medical product development; considers the
concepts of LST design, examples of successful LSTs, the relative
advantages of LSTs, and the infrastructure needed to build LST
capacity as a routine function of care; identifies structural,
cultural, and regulatory barriers hindering the development of an
enhanced LST capacity; discusses needs and strategies in building
public demand for and participation in LSTs; and considers
near-term strategies for accelerating progress in the uptake of
LSTs in the United States. Table of Contents Front Matter 1
Introduction 2 Large Simple Trials Now and Looking Forward 3
Examples of Large Simple Trials 4 Medical Product Regulatory Issues
5 Infrastructure Needs and Opportunities 6 Ethical and Privacy
Policy Issues 7 Research Partner Perspectives 8 The Randomized
Evaluations of Accepted Choices in Treatment Trials 9 Strategies
Going Forward Appendix A: Workshop Agenda Appendix B: Biographical
Sketches of Speakers
Digital health data are the lifeblood of a continuous learning
health system. A steady flow of reliable data is necessary to
coordinate and monitor patient care, analyze and improve systems of
care, conduct research to develop new products and approaches,
assess the effectiveness of medical interventions, and advance
population health. 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. The ability to collect, share, and use digital
health data is rapidly evolving. Increasing adoption of electronic
health records (EHRs) is being driven by the implementation of the
Health Information Technology for Economic and Clinical Health
(HITECH) Act, which pays hospitals and individuals incentives if
they can demonstrate that they use basic EHRs in 2011. Only a third
had access to the basic features necessary to leverage this
information for improvement, such as the ability to view laboratory
results, maintain problem lists, or manage prescription ordering.
In addition to increased data collection, more organizations are
sharing digital health data. Data collected to meet federal
reporting requirements or for administrative purposes are becoming
more accessible. Efforts such as Health.Data.gov provide access to
government datasets for the development of insights and software
applications with the goal of improving health. Within the private
sector, at least one pharmaceutical company is actively exploring
release of some of its clinical trial data for research by others.
Digital Data Improvement Priorities for Continuous Learning in
Health and Health Care: Workshop Summary summarizes discussions at
the March 2012 Institute of Medicine (2012) workshop to identify
and characterize the current deficiencies in the reliability,
availability, and usability of digital health data and consider
strategies, priorities, and responsibilities to address such
deficiencies. Table of Contents Front Matter 1 Introduction 2 Data
Quality Challenges and Opportunities in a Learning Health System 3
Digital Health Data Uses: Leveraging Data for Better Health 4
Issues and Opportunities in the Emergence of Large Health-Related
Datasets 5 Innovations Emerging in the Clinical Data Utility 6
Strategies Going Forward Appendix A: Speaker Biographies Appendix
B: Workshop Agenda
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