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

Health care quality and its affordability have become very pressing issues in the United States. All sectors of the country are attempting to push forward initiatives that will improve the health care system as well as the health of the American population in general. Despite the economical dedication to health care, about 1/5, the system remains uneven and fragmented, patient harm is quite common, care is often uncoordinated, and many more mishaps occur. There exists many obstacles to improve the nation's health care system; these include the capacity to reliably and consistently measure progress. In 2006 the Institute of Medicine (IOM) established the Roundtable on Value & Science-Driven Health Care which has since accelerated the development of a learning health system- one in which science, informatics, incentives, and culture are aligned to create a continuous learning loop. This learning loop would thus help make the health care system better. In response, the IOM organized a 2-day workshop to explore in depth the core measurement needs for population health, health care quality, and health care costs. The workshop hoped to gain a full understanding of how to improve the nation's measurement capacity to track progress in the health care system. Having this knowledge would help the nation get one step closer to the creation of an efficient learning loop. The workshop was divided into a series of sessions that focused on different aspects of measurement. Core Measurement Needs for Better Care, Better Health, and Lower Costs: Counting What Counts: Workshop Summary includes explanations and key details for these sessions: Vision, Current Measurement Capabilities, Specifying the Shape of a Core Metric Set, and Implementation. The report also features common themes within these areas, the workshop agenda, and information about those involved. Table of Contents Front Matter 1 Introduction 2 Vision 3 Current Measurement Capabilities 4 Core Metrics Sets in Use 5 Specifying the Shape of a Core Metrics Set 6 Implementation 7 Building the Infrastructure 8 Common Themes Appendix A: Biographical Sketches of Speakers and Planning Committee Appendix B: Workshop Agenda Appendix C: Workshop Participants

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