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