Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 1
Synopsis and Highlights INTRODUCTION AND OVERVIEW Health and health care are going digital. As multiple intersecting plat- forms evolve to form a novel operational foundation for health and health care—the nation’s digital health utility—the stage is set for fundamental and unprecedented transformation. Most changes will occur virtually out of sight, and the pace and profile of the transformation will be determined by stewardship that fosters alignment of technology, science, and culture in support of a continuously learning health system. In the context of growing concerns about the quality and costs of care, the nation’s health and economic security are interdependently linked to the success of that stewardship. Progress in computational science, information technology (IT), and biomedical and health research methods have made it possible to foresee the emergence of a learning health system that enables both the seamless and efficient delivery of best care practices and the real-time generation and application of new knowledge. Increases in the complexity and costs of care compel such a system. With rapid advances in approaches to di- agnosis (such as molecular diagnostics), therapeutics, genetic insights into individual variation, and emerging measurement modalities (such as within proteomics and imaging), clinicians and patients must sort through expo- nentially increasing numbers of factors with each clinical decision. At the same time, healthcare costs are draining the purchasing power of consum- ers and handicapping the competitiveness of U.S. businesses, yet health outcomes are falling far short of the possible. 1
OCR for page 2
2 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM Against this backdrop of opportunity and urgency, the Institute of Medicine (IOM) of the National Academies, sponsored by the Office of the National Coordinator for Health Information Technology (ONC), con- vened a series of expert meetings to explore strategies for accelerating the development of the digital infrastructure for the learning health system. Presentations and major elements of those discussions are summarized in this publication, Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care. The Learning Health System In 2001, the IOM report Crossing the Quality Chasm called national attention to untenable deficiencies in health care, noting that every patient should expect care that is safe, effective, patient-centered, timely, efficient, and equitable (IOM, 2001). Based on the determination that health care is a complex adaptive system—one in which progress on its central purpose is shaped by tenets that are few, simple, and basic—the report identified several rules to guide health care. In particular, these rules underscore the importance of issues related to the locus of decisions, patient perspectives, evidence, transparency, and waste reduction. The report envisioned, in ef- fect, engaging patients, providers, and policy makers alike to ensure that every healthcare decision is guided by timely, accurate, and comprehensive health information provided in real time to ensure constantly improving delivery of the right care to the right person for the right price. The release of the IOM Chasm report stimulated broad activities re- lated to clinical quality improvement and the effectiveness of health care, including the eventual creation by the IOM of the Roundtable on Value & Science-Driven Health Care. Begun in 2006 as the IOM Roundtable on Evidence-Based Medicine, it has explored ways to improve the evidence base for medical decisions and sought the development of a learning health system “designed to generate and apply the best evidence for collabora- tive health choices of each patient and provider; to drive the process of discovery as a natural outgrowth of patient care; and to ensure innovation, quality, safety, and value in health care.” From its inception, the Round- table has conducted The Learning Health System Series of public meetings to consider the capture of emerging innovations—such as those occurring in IT, research methods, and care delivery—as building blocks in the foun- dation of a learning health system. Characteristics of such a system are noted in Box S-1 and in matrix form in Appendix A. In broad terms, they represent delivery of best practice guidance at the point of choice, continu- ous learning and feedback in both health and health care, and seamless, ongoing communication among participants, all facilitated through the application of IT.
OCR for page 3
3 SYNOPSIS AND HIGHLIGHTS BOX S-1 Learning Health System Characteristics Culture: participatory, team-based, transparent, improving Design and processes: patient-anchored and tested Patients and public: fully and actively engaged Decisions: informed, facilitated, shared, and coordinated Care: starting with the best practice, every time Outcomes and costs: transparent and constantly assessed Knowledge: ongoing, seamless product of services and research Digital technology: the engine for continuous improvement Health information: a reliable, secure, and reusable resource The Data utility: data stewarded and used for the common good Trust fabric: strong, protected, and actively nurtured Leadership: multi-focal, networked, and dynamic SOURCE: Adapted from The Learning Healthcare System (IOM, 2007). Because IT serves as the functional engine for the continuous learn- ing system, this ONC-commissioned exploration was broadly conceived to consider the issues and strategies required for the emergence of a digital infrastructure that allows data collected during activities in various settings— clinical, research, and public health—to be integrated, analyzed, and broadly applied (“collect once, use for multiple purposes”) to inform and improve clinical care decisions, promote patient education and self-management, design public health strategies, and support research and knowledge devel- opment efforts in a timely manner. The Digital Health Infrastructure The digital infrastructure for the learning health system will not solely be the result of features designed and built de novo. Existing initiatives and
OCR for page 4
4 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM resources are actively in play at multiple levels—including electronic health records (EHRs); personal health records (PHRs); telehealth; health informa- tion portals; electronic monitoring devices; biobanks; health information databases maintained by large health systems, private insurers, and regula- tory agencies; and advances in molecular diagnostics. Each adds important capacity for clinical care, clinical and health services research, public health surveillance and intervention, patient education and self-management, and safety and cost monitoring. Still, these capacities are relatively early in their development, and progress depends on improvements on several dimensions. As of 2009, only about 12% of hospitals and 6% of clinician offices had an EHR in place (DesRoches et al., 2008; Jha et al., 2010) and only about 1 in 14 Americans had electronic access to any patient-oriented version of their health record (CHCF, 2010). On the other hand, since 2000, the number of Americans who have access to the internet has jumped from 46% to 74%, and the number of American adults who have looked online for health informa- tion has jumped from 25% to 61% (Fox, 2010). Wireless technology is quickening the pace of change. With 6 in 10 American adults using wireless capability with a laptop or mobile device (Smith, 2010), mobile applica- tions are rapidly developing the potential for remote site access to health information, as well as diagnostic and even treatment services. This developing potential presents opportunities and challenges for stewardship. Issues related to interoperability, governance, patient and public engagement, and privacy and security concerns, among others, will need to be better addressed for successful progress toward a learning health system. Approaches and lessons from sectors outside health include those from energy and the financial sector, two examples discussed in the meet- ings and summarized in this publication (see Appendix B). VISA used a minimalist approach, crafted on the combination of mutual self-interest and basic rules-of-play, to build its platform for a global credit card network. Consumer Energy’s work in the Smart Grid Initiative applied an analytically driven approach to accommodate and network a wide variety of legacy nodes in growing the electronic platform operating the nation’s energy system. Background on the Smart Grid Initiative is presented in Box S-2. Regardless of the model, a key rationale for the workshop discussions was the reality that effective and efficient progress in the growth and de- velopment of our national and global digital health infrastructure requires active cooperation, collaboration, and role delineation among many orga- nizations, companies, and agencies—private and public—at the cutting edge of using health IT to improve health and health care. The striking, and accelerating, progress in the capacity and transfor- mative influences of IT on society over the past three decades is a blended product of interrelated initiatives arising from within the commercial, in-
OCR for page 5
5 SYNOPSIS AND HIGHLIGHTS BOX S-2 Case: The Smart Grid The Smart Grid is a long-term, complex systems development project to grow the electronic platform operating the nation’s energy system using an engineering approach to accommodate a wide variety of legacy nodes that are organic—con- stantly growing and evolving, much like a biological system. This continuous evolu- tion allows the Smart Grid’s architecture to preserve and encourage the capacity of each node to innovate locally and deal with complexity in a way that suits local and grid needs. As conceived, the Smart Grid will • Enable active participation by consumers • Accommodate all generation and storage options • Enable new products, services, and markets • Provide power equality for the digital economy • Optimize asset utilization and operate efficiently • Anticipate and respond to system disturbances (self-heal) • Operate resiliently against attack and natural disaster Because there is no need for consensus among the nodes on how they should operate within local boundaries, the Smart Grid development methodology is not based on comprehensive internal design and operating standards for each node on the Grid to follow. Instead, the approach accommodates highly diverse nodes connecting to the Smart Grid using open data translation protocols that standardize information management, rather than using the internal workings of each node. The Grid becomes a communications bus to which each node must be able to write, and from which each node must be able to read. This architecture preserves capacities for local operating autonomy and innovation throughout the Smart Grid. It also manages a standardized communications capacity among complex, and otherwise noninteroperable, legacy nodes on the Grid. These fea- tures are all characteristics of ultra-large-scale (ULS) software-intensive systems. dependent, and public sectors. Leaps in the speed, power, and efficiency of information processing, the development of the Internet and World Wide Web, and its use to facilitate near-universally available real-time access to information, have spawned a new economy and new vehicles for progress. Health information vendors, large and small, have emerged to meet the growing demand for capacity to manage the retrieval, storage, and delivery of information for agencies, institutions, professionals, and individuals in virtually every aspect of health and health care. The range of newly digi- talized services—and the growth of vendors to provide them—is startling. Through technologies developed by companies such as Google, Microsoft, and Yahoo, the amount of web-based health information accessed daily
OCR for page 6
6 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM by individuals and clinicians is already transforming the care process. Beyond the publicly available digital resources, a vast array of specialized care management products have emerged for activities such as scheduling and billing; claims processing and payment; supply and equipment inven- tory maintenance; individual patient charting; medication prescribing and tracking; family and personal health records; clinician-patient communica- tion; clinician and patient decision support; robotics-assisted procedures; telehealth for remote site diagnosis and treatment; disease surveillance; vital statistics reporting; postmarket product monitoring; safety and hazard exposure monitoring; clinical research protocols; disease and intervention registries; and data aggregation, analysis, and modeling. Various large academic health centers and healthcare delivery organi- zations—Veterans Health Administration (VHA), Kaiser Permanente (see Box S-3), Geisinger Health System, Vanderbilt, MD Anderson, Palo Alto Medical Foundation, Group Health Cooperative, several Harvard facilities, Children’s Hospital of Philadelphia, Virginia Mason, and the Mayo Clinic, to name a few—have invested substantially in the creation of advanced digital resources for administrative, patient care, and research functions. Additionally, some related collaborative research networks have begun to develop. Nonetheless, the diversity and limited compatibility of the products, and the lack of economic incentives for their use have, to date, restrained the broader uptake, application, and functional utility of digital capacity across the system. A number of public, private, and independent sector initiatives have emerged to accelerate stakeholder action on various dimensions important to progress. To supplement the relatively limited pre-2009 public invest- ments, independent sector leadership has come from foundations such as the Markle Foundation, the Robert Wood Johnson Foundation, and the California HealthCare Foundation. Furthermore, in addition to the formation of capacity-building resources such as the Health Information Exchanges, a number of facilitative stakeholder groups have emerged—for example, the eHealth Initiative, the Clinical Data Interchange Standards Consortium (CDISC), and the National eHealth Collaborative. On the professional advancement dimension, the American Medical Informatics Association has developed as a growing resource for the contributions of biomedical and health informaticians working in activities to organize, manage, analyze, and use information in health care. An example of the coordinative potential of these groups is found in the development of inte- gration profiles by Integrating the Healthcare Enterprise and CDISC to sup- port the use of EHRs for clinical research, quality, and public health, and the testing and demonstration of these profiles by several vendors including Cerner, Allscripts, Greenway Medical, and General Electric Healthcare. At the federal level, ONC was created in 2004 in the U.S. Department
OCR for page 7
7 SYNOPSIS AND HIGHLIGHTS BOX S-3 Case: Kaiser Permanente In 2003, Kaiser Permanente (KP) launched a $4 billion health information system called KP HealthConnect that links its facilities and clinicians throughout their delivery system and represents the largest civilian installation of electronic health records in the United States. The EHR at the heart of KP HealthConnect provides a reliably accessible longitudinal record of member encounters across clinical settings including laboratory, medication, and imaging data; as well as supporting: • lectronic prescribing and test ordering (computerized physician-order E entry) with standard order sets to promote evidence-based care • opulation and patient-panel management tools such as disease regis- P tries to track patients with chronic conditions • ecision support tools such as medication-safety alerts, preventive-care D reminders, and online clinical guidelines • lectronic referrals that directly schedule patient appointments with spe- E cialty care physicians • ersonal health records providing patients with the ability to view their P personal clinical information including lab results, plus linkage with phar- macy, physician scheduling, and secure and confidential e-mail messag- ing with clinicians. • erformance monitoring and reporting capabilities P • atient registration and billing functions P Physician leaders report that access to the EHR in the exam room is helping to promote compliance with evidence-based guidelines and treatment protocols, eliminate duplicate tests, and enable physicians to handle multiple complaints more efficiently within one visit. Ongoing evaluation by Kaiser indicates that pa- tient satisfaction with outpatient physician encounters has increased and that the combination of computerized physician-order entry, medication bar coding, and electronic documentation tools is helping to reduce medication administration errors in hospital care. Overall, Kaiser’s experience suggests that use of the EHR and online portal to support care management and new modes of patient encounters is having positive effects on utilization of services and patient engagement. For example, three-quarters or more of online users surveyed agreed that the portal enables them to manage their health care effectively and that it makes interacting with the healthcare team more convenient. of Health and Human Services (HHS) to stimulate progress in the field. Since 2009, with the enactment of the Health Information Technology for Economic and Clinical Health Act (HITECH) as part of the American Recovery and Reinvestment Act, the federal government leadership profile has become especially prominent. This has included the commitment of
OCR for page 8
8 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM unprecedented resources for health information technology (HIT), admin- istered through the leadership of ONC. Under HITECH, ONC was granted $2 billion to facilitate the adoption and meaningful use of HIT. In addition, an estimated $27 billion was designated for the Centers for Medicare & Medicaid Services (CMS) to distribute as incentive payments for physicians and hospitals to become meaningful users of HIT. Designed as a set of staged requirements to qualify for CMS incentive payments, the first-stage elements of “meaningful use” were released by CMS on July 13, 2010. These established a core set of requirements for eligible professionals and hospitals, as well as a menu of additional choices, from which five are to be chosen. The stage 1 meaningful use target ele- ments are listed in summary fashion in Box S-4, and details are contained in Appendix D. The subsequent stages of meaningful use are currently under development and are presented later in this summary, along with an indica- tion of related issues flagged in workshop discussions. In addition to the meaningful use requirements, ONC has funded a series of grant programs through HITECH, including the Beacon Commu- nity grants, aimed at demonstrating community-wide digital infrastructure capacity and use for health improvement, and the Strategic Health Informa- tion Technology Advanced Research Projects Program, to foster the capture of technological advances to improve system performance. At the broader level, ONC is pursuing a series of initiatives to foster health information exchange among stakeholders, including the regional health information ex- changes and under the Nationwide Health Information Network (NWHIN). Several additional HHS agencies have activities important to the de- velopment of the digital learning health system. CMS, in addition to estab- lishing rules for meaningful use and requirements for uniform condition identifiers central to healthcare payment and research, recently created the Center for Medicare and Medicaid Innovation to test innovative pay- ment and program service delivery methods. Within the National Institutes of Health (NIH), the National Library of Medicine serves as the central coordinating body for clinical terminology standards, and other NIH pro- grams, such as the Clinical and Translational Science Awards Program, and the National Cancer Institute’s Enterprise Vocabulary Series and cancer Biomedical Informatics Grid (caBIG®, see Box S-5 and Appendix B for additional information) serve as key contributors to building the capacity to derive scientific discovery from patient care. Through its National Re- source Center for Health IT and capacity initiatives on patient registries, the Agency for Healthcare Research and Quality (AHRQ) supports a number of programs to advance the digital utility for healthcare quality and safety. At the Food and Drug Administration (FDA), the Sentinel Initiative (see Box S-6 and Appendix B) has been designed to build and implement a national electronic system for postmarket surveillance of approved drugs
OCR for page 9
9 SYNOPSIS AND HIGHLIGHTS BOX S-4 Meaningful Use Requirement Categories Core structured personal data (age, sex, ethnicity, smoking status) Core list of active problems and diagnoses Core structured clinical data (vital signs, meds, [labs]) Outpatient medications electronically prescribed Automated medication safeguard/reconciliation Clinical decision support Care coordination support/interoperability Visit-specific information to patients Automated patient reminders e-Record patient access (copy or patient portal) Embedded measures for clinical quality reporting Security safeguards Examples of optional elements: Advance directives for ages >65 Condition-specific data retrieval capacity Public health reporting (reportable conditions) SOURCE: Adapted from Blumenthal and Tavenner (2010). See Appendix D for details. and other medical products. The Centers for Disease Control and Prevention (CDC) supports several IT-based public health data collection and surveil- lance programs and serves as the primary agency responsible for these track- ing efforts, response and public health links to domestic and international public health data systems, and the Health Resources and Services Admin- istration (HRSA) has developed initiatives introducing HIT to improve care access and coordination in rural areas and for underserved populations. Efforts to promote the development, implementation, and widespread adoption of HIT also build on a wide array of digital learning leadership efforts by other federal agencies. In particular, important contributions stem
OCR for page 10
10 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM BOX S-5 Case: The National Cancer Institute’s caBIG® Initiative The National Cancer Institute of the National Institutes of Health has devel- oped an informatics program designed to improve patient care and accelerate scientific discoveries by enabling the collection and analysis of large amounts of biological and clinical information and facilitating connectivity and collaboration among biomedical researchers and organizations. More than 700 different orga- nizations are actively engaged in caBIG®, including basic and clinical researchers, consumers, physicians, advocates, software architects and developers, bioinfor- matics specialists and executives from academe, medical centers, government, and commercial software, pharmaceutical, and biotechnology companies from the United States and in 15+ countries around the globe. At the heart of the caBIG® program is caGrid, a model-driven, service- oriented architecture that provides standards-based core “services,” tools, and interfaces so the community can connect to share data and analyses efficiently and securely. More than 120 organizations are connected to caGrid. In partnership with the American Society of Clincal Oncology, caBIG® is developing specifica- tions and services to support oncology-extended EHRs that are being deployed in community practice and hospital settings. caBIG® tools and technology are also being used by researchers working on cardiovascular health, arthritis, and AIDS. In addition, pilot projects have successfully connected caGrid to other net- works, including the Nationwide Health Information Network, the CardioVascular Research Grid, and the computational network TeraGrid. from responsibilities and activities of the VHA—for example, the highly re- garded Veterans Health Information Systems and Technology Architecture system of IT supporting better care, as well as personal tools such as “My HealtheVet” and the Virtual Lifetime Electronic Record programs—the Department of Defense (DOD), the Federal Communications Commission (FCC), and the National Science Foundation (NSF). The VHA and the DOD have formed the Telemedicine and Advanced Technology Research Center as a joint program to advance research and applications in health in- formatics, telemedicine, and mobile health monitoring systems. Because of the deep and broad set of capabilities and initiatives collectively sponsored by federal agencies, their coordination and interface with private sector activities offers a vital strategic opportunity to accelerate the development of a learning health system. Testament to the compelling priority of the prospects, in December 2010, the President’s Council of Advisors on Science and Technology (PCAST) issued its report, Realizing the Full Potential of Health Informa- tion Technology to Improve Healthcare for Americans: The Path Forward (PCAST, 2010). The PCAST report examines the opportunities and needs
OCR for page 11
11 SYNOPSIS AND HIGHLIGHTS BOX S-6 Case: The FDA’s Sentinel Initiative In 2008, the Department of Health and Human Services and the Food and Drug Administration (FDA) announced the launch of FDA’s Sentinel Initia- tive, a long-term program designed to build and implement a national electronic s ystem—the Sentinel System—for monitoring the safety of FDA-approved drugs and other medical products. Data partners in the Sentinel System will include organizations such as academic medical centers, healthcare systems, and health insurance companies. As currently envisioned, participating data partners will access, maintain, and protect their respective data, functioning as part of a “dis- tributed system.” In a related pilot activity, FDA is working with Harvard Pilgrim Health Care, Inc. to develop a smaller working version of the future Sentinel System, dubbed “Mini-Sentinel.” Through this pilot, FDA will learn more about some of the barriers and challenges, both internal and external, to establishing a Sentinel System for medical product safety monitoring. The Mini-Sentinel Coordinating Center (MSCC) represents a consortium of more than 20 collaborating institutions, working with participating data partners to use a common data model as the basis for their ap- proach. Data partners transform their data into a standardized format, based upon which the MSCC will write a single analytical software program for a given safety question and provide it to each of the data partners. Each partner will conduct analyses behind its existing, secure firewall and send only summary results to the MSCC for aggregation and further evaluation. As this pilot is being implemented, a governance structure is being developed to ensure the activity encourages broad collaboration within appropriate guidelines for the conduct of public health surveillance activities. In order to accomplish that, the MSCC is developing a Statement of Principles and Policies that will include descriptions of the organizational structure and policies related to communication, privacy, confidentiality, data usage, conflicts of interest, and intellectual property. for the use of HIT to improve healthcare quality and reduce cost, as well as the activities and aligment of current federal programs with relevant re- sponsibilities. It sets out a series of recommendations intended to facilitate private, entrepreneurial initiatives through governmental action to speed development of a “universal exchange language” for health information, the application of which would maximize the ability to use existing and developing electronic record systems. Specifically, it recommends action by the federal government, especially ONC and CMS, in accelerating the identification of standards required for health information exchange using metadata-tagged data elements; mapping various existing semantic taxono- mies onto the tagged elements; developing incentives for product use of tagged elements; fostering use of metadata for security and safety protocols;
OCR for page 42
42 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM Priority Action Targets The case: Analyses to assess the potential returns on health and economic dimensions. Because of the centrality of broad-based support to progress, and the “public good” nature of many of the activities, the need to demon- strate a value proposition or business case for participation by stakeholders in a digital learning health system was a topic of much discussion during the workshop series. This emphasis was reinforced by the approach taken by the PCAST report to encourage the development of a market around digital health information exchange. Support of methods that apply seri- ous analytical rigor to these issues and generate both technical and policy suggestions were identified as being crucial to this effort. Researchers and organizations such as think tanks were discussed as likely being the best positioned to undertake the necessary analyses with support of a commis- sioning resource. Involvement: Initiative on citizens, patients, and clinicians as active learning stakeholders. Many workshop discussions considered stakeholder invest- ment to be a necessary component of any successful strategy. Participants identified the need to redefine the roles of citizens, patients, and clinicians in a way that activated their participation in their own health, and the health of the population at large, through the facilitative properties of the digital infrastructure. It was noted that patient and clinician groups can play a crucial role in this effort by helping to convey the value proposi- tion and ensuring that the interests of their constituents are represented in the development and evolution of the system. Efforts that facilitate stake- holder participation—such as increased control of health information by patients and the use of patient-generated data in care plans and knowledge generating processes—were discussed as priority next steps in stakeholder engagement. Additionally, to attend to concerns around privacy, security, trust, and additional work burden, participants stressed the importance of honesty and transparency in facilitating support and understanding. Ulti- mately, discussions noted that demonstrating the value of a digital health infrastructure through the use of case studies that point to improved out- comes and efficiency was likely the most compelling strategy to appeal to stakeholders. Functionality standards: Consensus on standards for core functionalities— care, quality, public health, and research. Progress on the technical stan- dards necessary to support the core functionalities of the learning health system was continually referenced in workshop discussions. Participants focused on the standards necessary not only to improve, monitor, and guide care decisions but also to accelerate research, quality efforts, patient moni-
OCR for page 43
43 SYNOPSIS AND HIGHLIGHTS toring, and health surveillance. Related requirements include the ability to exchange information through the use of minimal standards (such as those to enable use of metadata-tagged information packets), query and analyze distributed repositories of data for research purposes, ensure care decision support, and enable quality improvement initiatives and public health sur- veillance and reporting. Discussions also touched on the need for the digital infrastructure to interface with next-generation systems including mobile health applications and the way in which these and other capacities could help engage patients and the public through improved information access. Participants also underscored the strategic importance of adhering to a minimal set of standards that support core functions but do not introduce unnecessary barriers to progress. Interoperability: Stakeholder vehicle to accelerate exchange and interoper- ability specifications. System interoperability remains a major obstacle to realizing a digital learning health system. When applying the ULS system lens to this challenge, many participants stressed the need to develop a parsimonious set of standards—such as those for metadata—to allow for practical interoperability and information exchange across systems. Noting that this issue lies in the realm of both technical capacity and governance structure, several participants often compared this effort to the evolution and governance of the Internet. While the differences between the digital health infrastructure and the Internet were acknowledged, it was suggested that the establishment and work of the Internet Engineering Task Force might provide guidance for an industrial institution for the governance of interoperability-related standards. Additionally, leveraging and coordinat- ing existing progress and ongoing efforts in the areas of standards develop- ment and facilitation were underscored as strategies to ensure that activities progress as efficiently as possible. ULS system test bed: Identify opportunities, implications, and test beds for ULS system approach. As discussions focused on the characteriza- tion of the health system as a complex sociotechnical ecosystem, analysis was suggested on how the ULS approach might be applied to the health system in both the short and long term. Mapping of a key ULS system report (Northrop et al., 2006) to the learning health system through a col- laborative effort between software engineers, computer scientists, medical informaticians, and clinicians was offered as a starting point for this effort. Furthermore, performing a rigorous engineering systems analysis leading to a concept paper was suggested to clarify further the opportunities and implications for the ULS system approach. Integral to the ULS approach is the need to support rapid prototyping for continuous innovation. It was suggested that test beds for the development, assessment, and dissemination
OCR for page 44
44 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM of these prototypes would be central to continual innovation. In this vein, several participants pointed to the opportunity presented by the creation of the Center for Medicare & Medicaid Innovation (CMMI). Certain communities of excellence already provide some capacity in this area, and participants often referenced ongoing activities at these institutions (see Appendix B). Technical acceleration: Collaborative vehicle for computational scientists and HIT community. Much of the work in the development of a digital learning health system will necessitate interdisciplinary collaboration be- tween academic, public, and private partners across the computer science, HIT, science, and engineering communities. Participants suggested estab- lishing a collaborative forum where these efforts can be initiated and de- veloped. This forum could catalyze the interdisciplinary research program necessary to develop the digital health infrastructure, and some participants suggested that funding for such a forum and its associated activities might best be served by collaborative efforts across relevant federal agencies (such as NIH and NSF), relevant private sector partners, or both. Quality measures: Consensus on embedded outcome-focused quality mea- sures. Participants noted that the first step in determining the usefulness of data collected by the digital health infrastructure is to identify the necessary elements to collect. It was stated several times that in order to support the quality improvement and research activities required for a learning system, consensus around useful outcome-based measures is needed. Participants suggested that this would motivate vendors and users to incorporate these measures into their systems, driving seamless integration of quality mea- surement and reporting into the digital infrastructure. Work at the NQF, through the ONC HIT Policy Committee, and at CMS has already begun addressing these needs. Clinical research: Cooperative network to advance distributed research capacity and core measures. Discussions often highlighted the centrality of ongoing and continuous generation of knowledge from clinical data as a central feature of the learning health system. Efforts to do research on data held in distributed repositories, such as the HMO Research Network and FDA’s Mini-Sentinel program, were pointed to as important early-stage efforts in building systematic, larger scale capacity. Participants suggested that a multidisciplinary, cooperative network of the relevant stakeholders— principally computer scientists, clinical researchers, and data holders— could be a starting point in accelerating progress in this dimension. It was noted that this network would need to consider development of core datasets to facilitate research and quality efforts, fostering consensus on
OCR for page 45
45 SYNOPSIS AND HIGHLIGHTS levels of consent and de-identification strategies necessary for effective re- use of data, development of methodologies for query-based and automated research and signal detection across distributed systems, development of standards for distributed queries across the system, implications for a ULS approach to existing and future distributed networks, and implications for distributed research from possible advances in data structure and packaging strategies for data interoperability and exchange across systems. Identity resolution: Consortium to address patient identification across the system. One of the major barriers discussed for several key system functions—care appropriateness, continuity, quality assessment, and re- search—relates to the current inability to reliably track and link individual patients with their associated information across the health system. This poses a problem for issues around care coordination, including the goal of being able to make care decisions based on comprehensive health informa- tion, as well as the development of a useful knowledge generation engine that can incorporate all relevant information and deliver useful, accurate support. Privacy and system security are paramount, but participants noted that approaches are available to address these issues responsibly and the barrier appears to be one of cultural hesitancy rather than a lack of tech- nical capability. Targeting this issue through a consortium approach was proposed as a way to provide the opportunity for stakeholder representa- tion and engagement in an honest, transparent conversation about the component value issues involved. Governance and coordination: Determination and implementation of gov- erning principles, priorities, system specifications, and cooperative strate- gies. Workshop participants articulated the idea that governance principles and priorities for a learning health system will require breaking new ground both organizationally and functionally. Discussions identified the need to improve coordination among key stakeholders to accelerate progress in identifying and sharing lessons, examining commonalities, and exploiting opportunities for efficiencies. It was noted that broad agreement will need to be cooperatively marshaled to attend to principles and priorities that support learning system functionalities such as data integrity, policies for data use, human subjects research issues, and proprietary interests. In ad- dition, discussions highlighted the role of governance in planning for and mitigating system failures, an inevitable occurrence in all systems, but one particularly well tolerated within the ULS system. Such failures would, of course, be opportunities for learning, but are potentially alarming in the context of health- and healthcare-associated information. An interdisciplin- ary consortium of computer scientists and health infomaticians, such as the one mentioned above, was suggested as a suitable place to engage this
OCR for page 46
46 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM issue on a technical level. However, addressing system failures in the health system also has a deeply sociocultural component for which approaches that emphasize honesty and transparency with patients and the public were suggested. Education and outreach about this issue were identified as being crucial in preventing irreparable tears in the trust fabric necessary to sup- port a digital learning health system. In this respect, participants noted the important contributions and potential of the Health IT Policy Committee’s Governance Working Group. Discussions also underscored the potential advantages of establishing a novel nongovernmental or public–private ven- ture to foster the necessary governance capacity in this country and to work with similar efforts internationally. Opportunities in the Next Stages of Meaningful Use In line with these priorities, discussions often focused on the ongoing meaningful use requirement development process. Workshop participants discussed the “beyond meaningful use” issue as key to increasing the utility of digitally embedded clinical records in a learning health system. Specifi- cally, since meaningful use is now such a well-established benchmark pro- cess, elements of particular importance to the development of a learning health system might not otherwise be addressed in the meaningful use pro- cess if they are not called out for explicit attention in the upcoming stages. Depicted in Box S-11 is a brief description of the meaningful use stages, the current expected focus of the requirements for stages 2 and 3, and bullets highlighting some key possibilities proposed by workshop participants. Stage 2. Items that workshop participants felt were of particular importance in enhancing the impact that stage 2 of meaningful use could have on the progress of the digital learning health system cut across several dimen- sions. Flagged as especially key were actions to accelerate standards for semantic interoperability and exchange, as well as approaches for consistent identification of patients. In order to further the utility of EHRs in clinical research and population health, participants suggested core data elements for EHRs, and seamless access to information from immunization registries. Reflecting the extensive discussion on the opportunity for using the digital infrastructure to better engage patients in their health care, participants suggested the addition of lay-interpretable language for patient-accessible information, and incorporation of patient-generated data. Finally, discus- sions emphasized the need for clinical decision support to be seamlessly integrated into HIT systems to speed adoption. Stage 3. Looking ahead to stage 3 of meaningful use, workshop participants suggested deepening the focus on requirements related to demonstrating
OCR for page 47
47 SYNOPSIS AND HIGHLIGHTS BOX S-11 Meaningful Use and the Digital Learning Health System Infrastructure STAGE 1: 2011–2012 Stage 1 of meaningful use established 14–15 (eligible hospitals or eligible professionals) required core functional components, focused on data capture and sharing, along with a menu set of 10 additional components, from which 5 are to be selected by the eligible hospitals or eligible professionals. STAGE 2: 2013–2014 Stage 2 of meaningful use is under development by the HIT Policy Commit- tee, including consideration of further focus on advanced clinical processes such as clinical decision support, disease management, patient access to health infor- mation, quality measurement, research, public health, and interoperability across IT systems. The following are items underscored in IOM discussions as being of particular and immediate importance to the impact of stage 2 enhancements on progress toward the Digital Infrastructure for the Learning Health System: • ntegration of semantic interoperability and exchange standards, including I data provenance and context • lements fostering seamless integration of clinical decision support E • se of lay-interpretable language for patient-accessible EHR information U • ncorporation of patient-generated data, including patient preferences I • nclusion of core data elements that facilitate use of EHR data for clinical I research. • trategy for seamless access to immunization history from immunization S registries • trategy for consistent identification of patients S STAGE 3: 2015+ Stage 3 of meaningful use is expected to expand on requirements from stages 1 and 2, with more direct emphasis on improved patient outcomes through sharpened focus on quality, safety, efficiency, population health, and interoperabil- ity. Following are items, in addition to those noted above for stage 2, underscored in IOM discussions as being of particular and immediate importance to the impact of stage 3 enhancements on progress toward the Digital Infrastructure for the Learning Health System: • bility to access comprehensive, longitudinal patient record at point of A care • ncorporation of patient editing ability I • emonstration of baseline semantic interoperability and exchange capacity D among IT systems • ntegration of nonmedical, health-related information I • eamless clinician–public health agency exchange on case-level informa- S tion and alerts
OCR for page 48
48 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM semantic interoperability and exchange capacity among systems, the ability to access comprehensive patient records at the point of care, and seamless exchange of cases and alerts between clinicians and public health agen- cies. Participants also suggested strategies for including additional types of data—including nonmedical, health-related data—as well as providing patients with an annotated editing ability over their own records. Stakeholder Responsibilities and Opportunities Throughout each workshop, frequent reference was made to leadership responsibilities that fell naturally to individual stakeholders, or groups of stakeholders, to advance progress in the development of the digital infra- structure for the learning health system. In many cases, this involves lever- aging ongoing efforts or building upon them with an orientation toward a continuous learning system. Summarized below are some of those most often noted. Federal Government Even though participants noted the decentralized manner in which localized innovation is likely to contribute to system progress, many of the central strategy elements and priority action targets discussed require strong leadership from federal agencies. Since a clear lead responsibility was given to ONC and the Secretary of HHS by the HITECH statute, many participants pointed to ONC as the natural leadership locus for activities needing coordination at the national level. Opportunities to build on the foundation laid by the HITECH requirements for work on standards, requirements, and certification criteria in meaningful use of EHRs include cooperation with other federal agencies in the development of a strategic plan for national HIT efforts; establishment of a governance mechanism for the NWHIN; accelerating, in cooperation with the National Institute for Standards and Technology, work on standards for exchange and interop- erability; and work with FCC, FDA, and CMS to identify standards and reconcile regulations to facilitate wireless transmission of medical informa- tion. Participants noted that as the HITECH funds are used, the coordinat- ing capacity of ONC will take on even greater importance, as coalitions will be needed to harmonize various key activities geared at developing the standards, policies, governance, and research projects necessary for effective progress toward a learning health system. With respect to technical innovation, as the leading federal agency for funding computer science and engineering research, NSF was noted as a logical locus to work with ONC and NIH in the development of test beds for the rapid deployment and evaluation of innovative technological
OCR for page 49
49 SYNOPSIS AND HIGHLIGHTS approaches. This work would have the potential to transform the function- ality and capacity of the digital health infrastructure, as well as to shepherd the establishment of collaborative vehicles for the ongoing partnerships between the HIT and computational science communities. Similarly, it was noted that progress in the quality and knowledge generation dimensions of the digital platform will require leadership from federal health agencies. AHRQ, working with ONC, professional societies, and groups such as NQF and the National Committee for Quality As- surance, is a natural steward for initiatives that enhance the utility of the digital infrastructure for quality improvement and health services research. The CDC’s focus on population health places it at the center of extend- ing the scope of the digital infrastructure beyond health care. This carries implications for almost all elements of the system, but will be especially important for the support of public health processes and research as well as public engagement. To these ends, participants suggested development of templates and protocols for the integration of nonmedical population health and demographic information into the system. As the nation’s largest healthcare financing organization, CMS cur- rently serves as the principal vehicle for applying economic incentives and standards to accelerate application of the meaningful use requirements. Furthermore, much promise for future innovation in health IT to support a learning system resides in the CMMI which provides an opportunity for testing innovative approaches suggested by workshop participants. These approaches include test beds for ULS-associated programs and new ap- proaches to integrating clinical decision support with care coordination and delivery models. On the research front, both NIH and NSF have mandates and networks to develop and demonstrate methods of improving the functionality of the digital infrastructure for health research applications. NIH, VHA, DOD, FDA, and AHRQ all have active programs under way that can evolve into cooperative leadership efforts to expand the use of EHRs for research into the clinical effectiveness of health interventions. To build support and engagement among patients and the general population, AHRQ, FDA, NIH, and ONC have each established links to patient communities that can serve as the building blocks for a collabora- tive initiative to better characterize and communicate the health and eco- nomic advantages of public involvement in a digital platform for health improvement. Given this level of activity, and the number of central stakeholders, the importance of ONC’s coordination mandate was often underscored. Similarly emphasized was the need to cultivate strong counterpart capacity outside of government to partner in coordination and governance responsibilities.
OCR for page 50
50 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM State and Local Government Leadership Given the regional emphasis of many of the ongoing efforts related to the digital learning health system—such as the establishment of regional health information exchanges—state and local governments and health departments have experience establishing governance structures and devel- oping programs for engaging local stakeholders. As a result, participants noted, state and local bodies can function as resources and foundation stones for broader efforts. By collaborating with ONC, CMS, HRSA, and other federal initiatives, best practices and lessons learned can be leveraged from state and local efforts. Additionally, it was suggested that some of the more advanced local initiatives could serve as test beds for some of the in- novative ULS-associated approaches suggested by participants. Initiatives Outside Government Outside of government, the entrepreneurial capacity of the commercial sector will certainly be a major driver of progress. Similarly, the full po- tential of the learning health system can only be achieved through the full engagement of patients and the public. Workshop discussants frequently underscored the roles of patient and clinician groups to facilitate dialogue between stakeholders and mediate public engagement. In particular, by using case studies to demonstrate the value of the digital infrastructure, participants felt these organizations could help develop the shared learn- ing culture and trust necessary for the learning system to function. Many patient and clinician groups—such as the American College of Physicians, the American College of Cardiology, the Society of Thoracic Surgeons, and the National Partnership for Women and Families—are already involved in this type of work. Participants noted that these existing activities could be expanded to include issues of particular importance to a learning system. Delivery systems, particularly those integrated across healthcare com- ponents, have been at the cutting edge of innovative EHR use, quality improvement, clinical data stewardship, patient engagement, quality ini- tiatives, and distributed research efforts. Workshop conversations often pointed to these efforts, such as those at Kaiser Permanente and Geisinger Health System, suggesting that continued coordination between these deliv- ery systems and relevant federal government agencies would be important in growing the digital health infrastructure. As the stewards of the largest stores of clinical and transactional in- formation outside of the federal government, insurers, payers, and product developers have an essential role to play in development of the digital infrastructure. Their use of transactional health data to assess utilization patterns, effectiveness, and efficiency is a foundational block on which
OCR for page 51
51 SYNOPSIS AND HIGHLIGHTS strategies for broader knowledge generation can build. Furthermore, com- panies such as UnitedHealthcare have begun engaging the public in the use of data in health. These efforts often were cited during discussions as crucial first steps in establishing a learning culture. Research is a fundamental aspect of the learning health system. Con- sequently, participants noted the fundamental role researchers have in de- veloping the infrastructure necessary for continuous knowledge generation and application. Formation of multidisciplinary research communities was often cited as a critical step in accelerating many of the strategies discussed. Funding for these communities was noted as a clear opportunity for col- laboration between NSF and NIH. Additionally, discussions highlighted that much work remains to be done in order to maximize the knowledge generation capabilities of the digital infrastructure, and that clinical re- search and product development communities have an essential role in building this capacity. As much of the progress to date is a result of initiatives from many independent organizations, their continued efforts as facilitators and inno- vators were noted as crucial to accelerating progress. Reference was often made to the importance of these organizations as the foundational elements for coordination and governance leadership from outside government. Finally, and ultimately of paramount importance, is the global perspec- tive. As highlighted during workshop discussions and presentations (see Chapter 8), meeting the goals of a learning health system will inevitably require drawing upon resources and leadership of similar efforts through- out the world. Some of this activity has begun in the limited arena of infec- tious disease surveillance and monitoring and offers a hint of the potential opportunities—and challenges—in developing a truly global clinical data utility for health progress. Collectively, the discussions captured in this publication represent un- precedented promise for innovation and progress in health and health care. Yet, the discussions also underscored that without successful efforts to cre- ate the conditions necessary for seamless interoperability, to create the pro- tocols for enhanced access and use of available information for knowledge generation, and to build the culture of engagement and support on behalf of the sort of information utility possible, the potential will go unmet. By thoroughly and candidly engaging in discussions on the vision, the current state of the system, the key priorities for future work, and the strategic ele- ments for accelerating progress, participants have set in motion perspectives that can quicken the progress in building the digital infrastructure required for the continuously learning health system necessary—and possible—to ensure better health for all.
OCR for page 52
52 DIGITAL INFRASTRUCTURE FOR THE LEARNING HEALTH SYSTEM REFERENCES Blumenthal, D., and M. Tavenner. 2010. The “meaningful use” regulation for electronic health records. New England Journal of Medicine 363(6):501-504. CHCF (California HealthCare Foundation). 2010. New national survey finds personal health records motivate consumers to improve their health. http://www.chcf.org/media/press- releases/2010/new-national-survey-finds-personal-health-records-motivate-consumers-to- improve-their-health#ixzz12kT8FU00 (accessed October 18, 2010). DesRoches, C. M., E. G. Campbell, S. R. Rao, K. Donelan, T. G. Ferris, A. Jha, R. Kaushal, D. E. Levy, S. Rosenbaum, A. E. Shields, and D. Blumenthal. 2008. Electronic health records in ambulatory care—a national survey of physicians. New England Journal of Medicine 359(1):50-60. Fox, S. 2010. E-patients, cyberchondriacs, and why we should stop calling names. http:// www.pewinternet.org/Commentary/2010/August/Epatients-Cyberchondriacs.aspx (ac- cessed October 19, 2010). IOM (Institute of Medicine). 2001. Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press. ______. 2007. The Learning Healthcare System: Workshop Summary. Washington, DC: The National Academies Press. Jha, A. K., C. M. DesRoches, P. D. Kralovec, and M. S. Joshi. 2010. A progress report on electronic health records in U.S. hospitals. Health Affairs (Millwood) 29(10):1951-1957. Northrop, L., P. H. Feiler, B. Pollak, and D. Pipitone. 2006. Ultra-large-scale systems: The software challenge of the future. Pittsburgh, PA: Software Engineering Institute, Carnegie Mellon University. PCAST (President’s Council of Advisors on Science and Technology). 2010. Realizing the full potential of health information technology to improve healthcare for Americans: The path forward. http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-health- it-report.pdf (accessed December 12, 2010). Smith, A. 2010. Mobile access 2010. http://www.pewinternet.org/Reports/2010/Mobile-Ac- cess-2010.aspx (accessed October 19, 2010).