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1 Introduction Digital health data are the lifeblood of a continuous learning health system. A steady flow of reliable data is necessary to coordinate and moni- tor 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 (IOM, 2012). This publication summarizes discussions at the March 2012 Institute of Medicine (IOM) workshop to identify and char- acterize the current deficiencies in the reliability, availability, and usability of digital health data and consider strategies, priorities, and responsibilities to address such deficiencies.1 The ability to collect, share, and use digital health data is rapidly evolv- ing. 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 EHRs in a meaningful way. However, although more than half of office-based physicians were using basic EHRs in 2011, only a third had access to the basic features necessary 1 The planning committee’s role was limited to planning the workshop, and the workshop summary has been prepared by the workshop rapporteurs as a factual summary of what occurred at the workshop. Statements, recommendations, and opinions expressed are those of individual presenters and participants, and are not necessarily endorsed or verified by the Institute of Medicine, and they should not be construed as reflecting any group consensus. 1
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2 DIGITAL DATA IMPROVEMENT PRIORITIES to leverage this information for improvement, such as the ability to view laboratory results, maintain problem lists, or manage prescription ordering, (Decker et al., 2012). 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 devel- opment 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.2 Data sharing partnerships are also opening up across organiza- tions. The Care Connectivity Consortium, a group of five health systems at the leading edge of using EHRs (Kaiser Permanente, Geisinger Health Sys- tem, Mayo Clinic, Intermoutain Healthcare, and Group Health Coopera- tive), have agreed to securely exchange clinical data for care coordination. Sharing is also happening across industries. In the case of AstraZeneca and WellPoint, a payer and a product manufacturer have initiated a study on the clinical and cost effectiveness of treatments for some chronic and common diseases. Finally, efforts to increase patient access to their own data, such as the Blue Button initiative which allows patients to download their health information with the click of a button, have been adopted by organizations such as the Veterans Health Administration and UnitedHealthcare, and included in the criteria for Meaningful Use. The increased collection and sharing of health data is quickly moving health care into the era of “big data.” This term refers to the huge volume and diversity of data collected in increasingly connected digital technolo- gies. The scale of “big data” has implications for analysis and learning in a way that has been leveraged by other industries, such as intelligence, but is only beginning in health care. Increasing collection, sharing, and aggregation of data are being matched by advances in methods for learning from these data. Clinical and administrative data can be used for studies to assess the effectiveness of health care interventions; identify product safety issues; detect emerging epidemics; and measure health care utilization and value. Observational methods that use data collected in the course of providing patient care are increasingly appreciated as valuable contributors to generating and testing hypotheses. The rapidly rising costs and extended duration of traditional randomized control trials (RCTs) have contributed to the interests of inves- tigators and funders, among whom there is a growing appreciation of the need to harness big data for innovative streamlined approaches to testing new interventions. 2 Personal communication, Joel Beetsch, Sanofi.
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INTRODUCTION 3 Crucial to all of these efforts is the appropriate alignment of data sources with their intended use. Different uses have different requirements of data, and therefore different priorities in terms of the evolving clinical data utility. This challenge is magnified by the lack of lessons and best practices for how to approach data quality assurances needed to support the multiple facets of a learning health system. To address these issues and gain a better understanding of the types, sources, applications, limitations, appropriate uses, and quality improvement needs for digital health data, the IOM’s Roundtable on Value & Science-Driven Health Care convened a meeting on March 23, 2012, titled Digital Data Priorities for Continu- ous Learning in Health and Health Care. This meeting followed a series of related discussions summarized in the IOM publication titled Digital Infrastructure for the Learning Health System (2011), and built on a body of work done by the Roundtable on the centrality of a clinical data utility to support continuous learning and improvement in health and health care (IOM, 2010, 2011a,b,c). DATA SOURCES IN THE DIGITAL HEALTH UTILITY Digital health data are produced in a variety of different environments, which impact greatly the characteristics of the data. Who collects the data, how it is collected, why it is collected, and what is collected are some of the ways that digital health data differ depending on their source and have implications for the use of that data. Understanding these characteristics is necessary to match data users with appropriate sources, and to understand limitations and barriers in data analysis. The increased adoption of EHRs has given data from routine care increasing prominence as a potential component of the data utility. Data collected in the course of delivering patient care come from a variety of sources such as clinician offices, ambulatory procedure centers, hospitals, and nursing and extended care facilities. The types of data vary by care setting, but generally include both clinical and administrative elements. Clinical elements include structured fields and free text notes, laboratory results, images, and diagnostic test results. Administrative information in- cludes process performance metrics, and details needed for billing, such as International Classification of Diseases (ICD) codes. Also growing in importance is data originating directly from patients. These data can be captured through the use of personal health records or patient portals, in clinical records as recorded by healthcare personnel, or in records external to the health system. They can include personal re- ports of current health status and wellness, family history, and remote site laboratory readings, as well as health-related data such as socioeconomic, environmental, and lifestyle factors. There is increasing interest in including
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4 DIGITAL DATA IMPROVEMENT PRIORITIES patient-generated data in data sources such as the use of patient-reported outcomes in research studies. Ongoing and completed clinical trial data offer yet another major source of new data insights—even beyond the immediate study focus. Trials funded by public or private sponsors are largely carried out either at aca- demic institutions or in community settings. Clinical trial data are typically collected in addition to those already collected in routine care, usually fol- low a standardized protocol, and are recorded in a case report form. When the trial is performed for the approval or assessment of a regulated medical product such as a new drug or device, the Food and Drug Administration (FDA) closely regulates how and what data are collected. In addition to traditional clinical trials, registries for quality activities, research, or post- marketing surveillance are a parallel source of enriched clinical data. Employers, as the purchasers of health insurance for much of the population, often possess data on employees’ health care utilization, basic health status, and associated expenses which can be used for knowledge development. In addition, these data can have greater longitudinal richness than records from clinical care providers. A final source of health data is population health data routinely col- lected through the public health system and its surveys and surveillance activities. These data provide information on overall health trends, such as births and deaths, disease prevalence, community health, environmental health, and access to care, as well as disease incidence and threat data. The collection and reporting of this information is increasingly digital, either through freestanding systems and portals or as integrated parts of EHR systems. Given the many levels at which public health works—local, county, state, and federal—different data collection approaches and requirements exist at the different levels. Other health-related community level data are routinely collected by various organizations, departments, and agencies. This includes community socioeconomic status profile data; community physical profile data, such as density, design, and use; civic engagement profiles; and community employer profile. Additionally, organizations col- lect data on individuals through their commercial and social activities, such as through supermarket rewards programs and Web-surfing patterns. These data are used not only on their own, for insights into the community, but in concert with other health data to yield a more complete understanding of population health. MOVING TO A CONTINUOUSLY LEARNING HEALTH SYSTEM Although the collection of large amounts of health and health-related data holds promise for both the scale and types of learning possible, data alone are not sufficient for learning. Sharing, aggregation, analysis, and the
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INTRODUCTION 5 continuous management and improvement of these data are necessary to enable the transition to a continuously learning health system. The applications of digital health data in a learning system are multiple, including care coordination; management of patient populations; associated care and business processes; outcome, quality, and value assessments; gen- eration of clinical evidence, including clinical trials, clinical effectiveness, and genomic studies; surveillance and trend detection, including medical products safety, syndromic and actionable surveillance, and hypothesis generation; and public health program management. These differing uses vary in their requirements for data quality and characteristics, but all share common challenges related to data access, liquidity, interoperability, and the development of innovative methods for analysis. These issues formed the foundation for the presentations and discussions at the IOM public workshop on Digital Data Priorities for Continuous Learning in Health and Health Care. WORKSHOP SCOPE AND OBJECTIVES Workshop participants included experts from across medicine, public health, informatics, health information technology, health care services research, health care quality reporting, biomedical research, clinical re- search, statistics, medical product manufacturing, health care payment and financing, and patient advocacy. Content was structured to explore the data quality challenges and opportunities in a learning health system, highlighting the opportunities and priorities beyond care coordination such as population and care process management, clinical research, translational informatics, and public health support at the national and state level. The workshop also explored the potential for learning from large-scale health datasets, focusing on innovative approaches to overcoming the challenges of distributed data, data harmonization, and identity resolution. The workshop statement of task can be found in Box 1-1, and the ele- ments are reflected in the stated meeting objectives: 1. Discuss the current quality status of digital health data. 2. Explore challenges, and identify key questions related to data qual- ity in the use of EHRs, patient registries, administrative data, and public health sources for learning—continuous and episodic—and for system operational and improvement purposes. 3. Engage individuals and organizations leading the way in improv- ing the reliability, availability, and usability of digital health data for real-time knowledge generation and health improvement in a continuously learning health system.
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6 DIGITAL DATA IMPROVEMENT PRIORITIES BOX 1-1 Statement of Task An ad hoc committee will plan and conduct a public workshop to explore the data quality issues and strategies central to the increasing capture and use of digital clinical and patient-reported data for knowledge development. The workshop will engage leading experts in reviewing the challenges, defining key questions, and exploring a strategic framework for progress on the issue of health data quality in a learning health system. Questions/topics of consideration could include What are the data quality requirements to support the various knowledge generation processes required by the learning health system (quality monitoring, sentinel event detection, disease surveillance, clinical research)? What is known about the current state of digital health data quality? What implications does this have for short term uses? What analytical methods are available to assess data quality? What novel analytical methods will need to be developed in order to meet learning health system data-use needs? What are the essential components of a strategy to achieve the necessary data quality levels? What lessons have been learned by those organizations already undertaking learning health system–type efforts? What foundational work has been done that can be built on/leveraged to better meet learning health system data quality needs? 4. Identify and characterize the current deficiencies and consider strat- egies, priorities, and responsibilities to address the deficiencies. 5. Initiate the development of a strategic framework for integrated and networked stewardship of efforts to continuously increase digital data utility. Through a series of expert presentations and discussions, workshop participants addressed issues of matching data quality to use, how these needs align with current data sources, and what the potential and chal- lenges are for leveraging digital health data for learning—both the short and long term. The final workshop session included a moderated discussion geared toward describing ways forward on the issues highlighted earlier in the workshop. ORGANIZATION OF THE SUMMARY This publication summarizes the proceedings of the workshop on Digi- tal Data Priorities for Continuous Learning in Health and Health Care, the 12th in the Learning Health System Series of publications by the Roundtable
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INTRODUCTION 7 on Value & Science-Driven Health Care. Chapters 2 through 5 summarize the expert presentations at the workshop, and are organized by thematic focus on the presentations, while Chapter 6 covers the concluding discus- sion. Chapter 2 addresses data quality challenges and opportunities in a learning health system, including explorations of data heterogeneity and the importance of focusing on data of value to the patient. Chapter 3 focuses on the many uses of the digital health data utility, covering the management of patient populations, clinical research, translational informatics, and both national and local public health efforts. Chapter 4 looks at emerging issues and opportunities in the use of large datasets, including a discussion of the challenge of data bias, and recent advances in mathematics that promise to move research toward generating real time insights. Chapter 5 explores emerging innovations in the use of digital health data including distributed queries, data normalization, and data linkages. Chapter 6 summarizes the concluding discussion in which many workshop participants suggested potential strategies and actions to catalyze progress. REFERENCES Decker, S. L., E. W. Jamoom, and J. E. Sisk. 2012. Physicians in nonprimary care and small practices and those age 55 and older lag in adopting electronic health record systems. Health Affairs 31(5):1-7. IOM (Institute of Medicine). 2010. Redesigning the clinical effectiveness research paradigm: Innovation and practice-based approaches. Washington, DC: The National Academies Press. _______. 2011a. Clinical data as the basic staple for health learning: Creating and protecting a public good. Washington, DC: The National Academies Press. _______. 2011b. Digital infrastructure for the learning health system: The road to continuous improvement in health and health care. Washington, DC: The National Academies Press. _______. 2011c. Learning what works: Infrastructure required for comparative effectiveness research. Washington, DC: The National Academies Press.
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