Clinical Data as the Basic Staple of Health Learning Creating and Protecting a Public Good: Workshop Summary (2010) / Chapter Skim
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1 Clinical Data as the Basic Staple of the Learning Health System
1 Clinical Data as the Basic Staple of the Learning Health System
Pages 43-68
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Roundtable on Value & Science-Driven Health Care's February 2008 workshop, Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good (Box 1-1)
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The Roundtable and Clinical Data The IOM's Roundtable on Value & Science-Driven Health Care provides a trusted venue for key stakeholders -- patients, health providers, payers, employers, manufacturers, health information technology, researchers, and policy makers -- to work cooperatively on innovative approaches to generating and applying evidence to drive improvements in the effectiveness and efficiency of medical care in the United States. Participants seek the development of a learning health system that is designed to generate and apply the best evidence for the collaborative healthcare 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.
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Patients, providers, payers, researchers, and government registries collect health information with the goal to assess and improve care provision and treatment, advance discovery and research, direct reimbursement, develop the evidence base for medical practice, and inform public health and health reform policy development. Progress in health information technology and analytic tools have dramatically expanded our capacity to capture and use these data.
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pose significant challenges to realizing the full potential of clinical data as the basic staple of a learning health system (Piwowar et al., 2008)
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The aims of the workshop and this publication are to provide an overview of these issues; to survey some of the current, potentially transformative research and clinical data initiatives under way; to discuss the notion of public and private goods; to consider implications of privacy, security, and proprietary concerns; and to suggest some possible opportunities to encourage a data sharing culture and the engagement of the public in advancing progress to the next generation of clinical data resources. Perspectives on Clinical Data and Health Learning To build a foundation for the presentations that would follow, each of the two days of the workshop began with a keynote address designed to take a broad look at relevant issues.
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Folic acid and birth defects: Medical records research led to the discovery that supplementing folic acid during pregnancy can prevent neural tube birth defects (NTDs)
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9 BASIC STAPLE OF THE LEARNING HEALTH SYSTEM the risk of death among those patients. As a result, the FDA now requires that the prescribing information for all antipsychotic drugs contain the same information about risks found in the Warnings section.
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It is about more than clinical data per se because the need for data is obvious. At its core, it is about whether clinical data are a public or private good.
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Those two goals are in conflict because we lack all of the components necessary for EHRs and other information tools to be able to share information in a way that achieves our goals for the learning health system. The Office of the National Coordinator chose to put interoperability first to
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That simple ability to drive information through a process is clearly what is required for clinical information to be used effectively in a learning system. Many healthcare organizations of varying sizes are looking at this agenda and seeking leadership.
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Under another scenario, clinical information could become a private good as something that is used differentially, for comparative advantage that benefits some, but not all. The reality today is that clinical information is largely a private good.
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filing with the Securities and Exchange Commission: "We have developed proprietary methods of care that are protected by patent and that cannot be easily replicated because of our unique information technology capabilities and use of health information." This is one of many healthcare companies that is making substantial investments in health information technology to drive long-term profitability. This raises
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Therein lies the challenge for the use of clinical data as a staple of the learning health system. It must be more useful, specified, efficient, assembled, and valuable.
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Managing Director, Health Program, Markle Foundation Introduction What might be achieved if clinical data could be positioned as a public good? How would such a system work, and what are the technical and policy issues to engage in fostering its evolution?
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BASIC STAPLE OF THE LEARNING HEALTH SYSTEM The Common Framework: Overview and Principles Policy Guides: Technical Guides: How Information is Protected How Information is Exchanged P1 T1 The Architecture for Privacy in a Networked The Common Framework: Technical Issues Health Information Environment and Requirements for Implementation P2 Model Privacy Policies and Procedures for T2 Health Information Exchange: Health Information Exchange Architecture Implementation Guide P3 Notification and Consent When Using a T3 Medication History Standards Record Locator Service P4 Correctly Matching Patients with T4 Laboratory Results Standards Their Records P5 T5 Authentication of System Users Background Issues on Data Quality P6 Patients' Access to Their Own T6 Record Locator Service: Technical Background Health Information from the Massachusetts Prototype Community P7 Auditing Access to and Use of a Health T7 Consumer Authentication for Networked Information Exchange Personal Health Information P8 Breaches of Confidential Health Information Future Technical Guides P9 A Common Framework for Networked Personal Health Information Future Policy Guides Model Contractual Language M1 M2 Key Topics in a Model Contract for Health A Model Contract for Health Information Exchange Information Exchange FIGURE 1-1 Connecting for Health: Common Framework overview and principles. SOURCE: Reprinted with permission from the Markle Foundation, 2009.
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Population Health CFH has defined improving population health as meeting three critical goals: • Bolstering research capabilities and enabling clinical practice to fully participate in and use scientific evidence; • Increasing the effectiveness of our public health system; and • Empowering consumers and professionals with information about cost, quality, and outcomes. The key objective is to improve how information is used to address research, public health, and quality measurement.
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The results indicate there is significant frustration with the current paradigm on the part of providers, as well as others responsible for population health. Although tremendous efforts have been devoted to amassing data, these expensive data collection efforts have not produced the anticipated and hoped-for benefits in terms of quality improvement or cost reduction.
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The challenge is to create alternative models that use modern information technology and take into account a wide variety of users, many and growing data sources, and a new approach to research and evidence creation. CFH has developed nine "First Principles for Population Health" based on the Common Framework attributes of privacy protections, sound network design, and appropriate oversight and accountability.
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As highlighted by these principles, a 21st-century approach needs to develop an information policy framework that broadly addresses public hopes and concerns. If we do not have an environment where people believe appropriate safeguards are in place to protect information, we will not realize our goals.
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. A Vision for the 21st Century A vision for 21st-century information sharing to improve population health will look at the problem from the perspective of the decision maker who needs to make better decisions.
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The IOM Roundtable on Value & ScienceDriven Health Care has outlined a vision for a learning health system where clinical data are a staple resource. This is an important vision, but we may fail to achieve it if we are constrained by historical approaches for collecting and analyzing data.
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Several new models emerging within population health efforts take a distributed approach to how information is generated. One such example in public health that illustrates and provides important insights into the opportunities and challenges of this approach is the DiSTRIBuTE model developed and maintained by the International Society for Disease Surveillance (International Society for Disease Surveillance, 2008)
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In addition, the time for data collection, analysis, and communication is long making timely trend detection and response on the part of public health entities a difficult task. Employing a different approach, the DiSTRIBuTE model considers those clinical delivery organizations that are already tracking flu-like rates by locally derived methods and asks whether meaningful information can be generated by electronically collecting only the summarized counts from each of these entities, regardless of how they were derived (Figure 1-2)
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SOURCE: http://www.sydromic.org/projects/DiSTRIBuTE2008_02_09.doc (accessed August 31, 2010)
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The site has data on historical forced vital capacity, the ALS Functional Rating Scale, and a standardized symptom battery. This example is compelling because it invites us to revisit our basic assumptions about the sources and uses of clinical data and about the nature and structure of the research process itself.
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2006. National surey on electronic personal health records.
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