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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary 2 U.S. Healthcare Data Today: Current State of Play INTRODUCTION Clinical data hold the potential to help transform the U.S. healthcare system. By providing greater insight to patients, providers, and policy makers into the appropriate application of interventions, and quality and costs of care, these data offer the opportunity to accelerate progress on the six dimensions of quality care—safe, effective, patient centered, timely, efficient, and equitable (Chaudhry, 2006; IOM, 2001, 2009; Safran et al., 2007). Understanding the scale of this potential and of the missed opportunities to improve health and health care due to gaps in data collection or barriers to their use requires an overview of existing healthcare data—the sources, types, accessibility, and uses. Through examples of healthcare data used to manage and drive improvements in care and for healthcare marketing, this chapter explores important aspects of healthcare data in the United States—examines what drives the collection of these data and the accessibility of these data for new clinical insights; reflects on how well these data are used and key barriers to wider use; and focuses attention on how clinical data from all sources—both public and private—could be made more widely useful to monitor clinical effectiveness. As reviewed in this chapter, data are collected on socioeconomic, environmental, biomedical, and genetic factors; individual health status and health behaviors; biomedical and genetic factors, as well as on resource use, outcomes, financing, and expenditures. These data are stored in a variety of electronic health records (EHRs), personal medical records, disease registries, and other databases. However, the distribution of clinical
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary data across the healthcare system is highly fragmented, presenting significant opportunity for those offering services that coordinate and aggregate data resources. To generate and organize data for evidence-based decision support, it will be important to explore technologies to enhance interoperability, data standardization, and compatibility for future data utilities. Leveraging access to both administrative and clinical data may require additional investments in developing linkages across the variety of healthcare data and data warehouses. Furthermore, emerging opportunities to deliver data at the point of care for healthcare decisions may enhance the public’s involvement in data-mining, data-sharing, and data-generating initiatives. Given the broad range of data sources and possible applications, a national strategy is needed to develop the requisite infrastructure and fill existing gaps in data collection and use. Speaking from his experience at Kaiser Permanente and in his role as chair of the National Committee on Vital and Health Statistics (NCVHS), Simon Cohn offers an overview of current major activities in healthcare data collection and database capacity development, including those related to administrative and claims data, quality indicators, health status and outcomes data, clinical research data, industry-sponsored pre- and postmarket studies, regulatory studies, registries, and emerging datasets. To help frame the discussion, Cohn presents a taxonomy for health data, then reflects on key issues and barriers to address as we move to a learning health system. Cohn highlights the NCVHS recommendations for enhancing protections for secondary uses of data collected electronically as particularly informative for advancing the clinical data agenda. In the area of enhanced health data stewardship, NCVHS recommends that covered entities be more specific about what data will be used, how, and by whom; that notices of privacy practices need to be more meaningful; and that data stewardship needs to extend to personal health data held by noncovered entities in personal health records and similar instruments. Massachusetts Health Quality Partners (MHQP) aggregates healthcare data to measure and report on physician performance in a more meaningful and transparent way—creating reports on performance at the physician network, medical group, practice site, and individual physician level, for both doctors and consumers. MHQP Executive Director Barbra Rabson shares aspects of this model, including its success in influencing investments in information systems to support quality and incentives for individual physicians and the challenges of engaging consumers. Overall, Rabson suggests, the MHQP experience and similar models hold promise for a world in which EHRs would be more fully and effectively integrated into medical practice, and clinical, claims, and personal data would be more fully integrated for quality improvement initiatives. For decades, researchers and clinicians have taken advantage of sources
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary of rich clinical and population-based data to generate new insights, stimulate major research programs, and develop robust clinical guidelines. Michael Lauer, director of the Division of Prevention and Population Sciences at the National Heart, Lung, and Blood Institute (NHLBI), asserts that to achieve the goal of the IOM Roundtable, clinical data ultimately will need to be integrated across the research and care delivery continuum and be made available to patients, clinicians, and researchers. Examples from abroad and within U.S. health systems, such as the Health Maintenance Organization Research Network (HMORN) and the Department of Veterans Affairs, demonstrate that rigorous and prospective data collection can be incorporated into routine clinical care. Still, most clinical data are not collected at the point of care, and most are organized in isolated silos that are difficult to access. As data are increasingly integrated within the care continuum, Lauer cautions against using inherently biased observational data in lieu of well-designed experimental data for synthesizing evidence-based policy recommendations. Although confounders in observational data can be statistically controlled to reduce biases somewhat, an ongoing national need remains for enhancing, networking, and analyzing existing data. Three major types of data are used by public and private entities to market healthcare products and services: health survey data, information about general consumption patterns, and administrative data generated by the healthcare delivery system. William Marder, senior vice president of the research and pharmaceutical units of Thomson Healthcare, reports on the use of data assets by providers and pharmaceutical companies, describing business models for the collection and analysis of these data. CURRENT HEALTHCARE DATA PROFILE Simon P. Cohn, M.D., M.P.H. Chair, National Committee on Vital and Health Statistics Associate Executive Director, The Permanente Federation, Kaiser Permanente This section aims to provide a brief overview of major current activities in healthcare data development and collection—including administrative and claims data, quality indicators, health status and outcomes data, clinical research data, industry-sponsored pre- and postmarket studies, regulatory studies, registries, and emerging datasets. The goal is to lay the groundwork and provide a context for addressing a variety of salient issues surrounding these data sources. Included are general comments about U.S. healthcare data today, with a view toward the future and a framework and taxonomy for health data, followed by reflections on key issues and barriers
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary that must be addressed before successfully moving forward. The conclusion contains an overview of a recent report from the NCVHS (or “the Committee”) that was requested by the Department of Health and Human Services (HHS) to further investigate and consider “secondary uses” of electronically collected and transmitted healthcare data as we move into the world of the Nationwide Health Information Network (NHIN). Background on the NCVHS The NCVHS is a statutory public advisory committee to the HHS. It has a 59-year history of advising on national health information policy, including health data, standards, statistics, privacy, and issues related to developing the National Health Information Infrastructure (NHII). It has 18 members—16 appointed by the HHS Secretary and 2 by Congress. Members are leaders and experts in their field (e.g., public health, healthcare informatics, data standards, population health, privacy, and confidentiality). The NCVHS has a well-deserved reputation for open collaborative processes and the ability to deliver timely and thoughtful recommendations. These attributes allow it to work closely and effectively with HHS organizational entities such as the Office of the National Coordinator (ONC), with a particular focus on challenging and difficult crosscutting issues. The NCVHS has a congressionally mandated role in relation to the Health Insurance Portability and Accountability Act (HIPAA), advising the HHS Secretary on HIPAA regulations and standards related to healthcare data, privacy and security, administrative and financial transactions, and healthcare identifiers. HIPAA code sets, including International Classification of Diseases (ICD), Current Procedural Terminology (CPT), and HIPAA Identifiers (including the National Provider Identifier), are key parts of the data infrastructure, and the NCVHS advises about the need for changes to those standards. Finally, the Committee monitors HIPAA implementation and advises Congress and the HHS Secretary with yearly status reports. In 2000, as part of its charge under HIPAA, the NCVHS set forth a strategy, a framework, and selection criteria for interoperable clinical data standards.1 This work provided the foundation for the selection of clinical message format standards and clinical terminology standards in 2002 and 2003, which became the core of Consolidated Health Informatics standards. Many of the standards were accepted by then-HHS Secretary Thompson and subsequently became key inputs to the Healthcare Information Technology Standards Panel (HITSP) process. Also, as part of the Medicare Modernization Act (MMA), the NCVHS was asked to investigate and advise the Centers for Medicare & Medicaid Services (CMS) and HHS 1 See http://www.ncvhs.hhs.gov/hipaa000706.pdf.
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary on standards for e-prescribing—standards that have been accepted as part of federal rule making. In 2001, after several years of investigation and hearings, the NCVHS published a strategic vision and strategy for building the NHII. The heart of the vision for the NHII is sharing information and knowledge appropriately so it is available to people when they need it to make the best possible health decisions. The NHIN is only one part of the larger vision: the NHII includes not only technologies, but more importantly, values, practices, relationships, laws, standards, systems, and applications to support all facets of individual health, health care, and public health (NCVHS, 2001). One important part of this report was an early recognition of the importance of HHS leadership, and a call for an office within the HHS reporting to the HHS Secretary, to coordinate and move this effort forward. Subsequently, the Office of the National Coordinator was created within the HHS. Since the development of that office, the NCVHS has been tasked with working with the HHS and the ONC to deal with the more challenging cross-cutting issues—such as privacy and the implications for the NHIN. While not answering all questions, because it is unclear how the NHIN will develop and evolve, the NCVHS is beginning to pose the important questions and to start public discussions. The NCVHS has also recommended initial functional requirements for the NHIN and recently produced a report on enhanced protections for uses of health data in the context of NHIN. The NCVHS also investigates and makes recommendations to the HHS Secretary on healthcare quality measurement and data and on population health issues in general. Much of the following is based on the groundbreaking work of the NCVHS. Health and Healthcare Data: Framework and Taxonomy When thinking about evidence-based medicine and about the data or taxonomies needed to support that work, it is important to take a broad view of all possible factors that impact (or are impacted by) health and health care. Figure 2-1 was developed by the Committee in conjunction with the National Center for Health Statistics and the HHS Data Council and published in 2002 (NCVHS, 2002). This graphic provides a reminder of the many influences on the health of the nation. In the context of this discussion of more traditional health and healthcare data, as well as of issues and barriers, it is important to recognize how much information we do not routinely collect, or if we do, we do not normally integrate it into our vision of health and health improvement. This NCVHS work was an important input to subsequent efforts to develop simpler, more approachable health and healthcare conceptual frameworks internationally. Figure 2-2, for example, shows a conceptual frame-
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary FIGURE 2-1 Influences on the population’s health.
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary work, initially developed by the Australian Institute of Health and Welfare, for health system planning. It was subsequently published by the World Health Organization—which has used the diagram as a tool for healthcare terminology and classification planning (Madden et al., 2007). This useful tool frames thinking about the data needed for a learning healthcare system as well as the development of sound health policy. In the center are the key concerns we need to monitor and focus on: health and well-being, including key aspects such as life expectancy, mortality, our own sense of well-being, state of functioning and disability, and, of course, illness, disease, and injury. Impacting these are health system interventions, including prevention and health promotion, and the major activity of the healthcare system—treatment, care, and rehabilitation. Determinants are important inputs into health and well-being such as biomedical and genetic factors, health behaviors, socioeconomic factors, and environmental factors. Impacting our ability to make interventions are resources and systems—human, economic, and others. This particular graphic begins to frame the discussion as we think about evidence-based medicine and data needs going forward. The taxonomy represented in Box 2-1 provides more specifics. Used by FIGURE 2-2 A conceptual framework for health. SOURCE: Madden et al. (2007). Reprinted with permission from the World Health Organization © 2007.
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary the HHS Data Council for health data and health statistics planning, this taxonomy is focused on what we would traditionally describe as healthcare data and represents data that are central to a learning health system. One notable component of this taxonomy is its explicit recognition of the importance of longitudinal data. Unless we can understand the key factors that influence outcomes (including underlying health status and socioeconomic data) and connect them with the interventions and outcomes, it becomes difficult to have a learning health system. BOX 2-1 Taxonomy Used by HHS Data Crucial for Health Data and Statistics Planning Demographics and Socioeconomic Data Age, sex, race, ethnicity, education, and related demographic/socioeconomic variables Health Status Data Individual health status, including morbidity, disability, diagnoses, problems, complaints, and signs and symptoms as well as behavioral and health risk factor data Health Resources Data Capacity and characteristics of the provider, plan, or health system Healthcare Utilization Data Nature and characteristics of the medical care visits, encounter, discharge, stay, or other use of healthcare services. Includes time, data, duration, tests, procedures, treatment, prescriptions, and other elements of the health encounter Healthcare Financing and Expenditure Data Costs, prices, charges, payments, insurance status, and source of payment Healthcare Outcomes Outcomes of prior or current prevention, treatment, counseling, or other interventions on future health status over time in a cyclical, longitudinal process Other Factors Genes and proteins, environmental exposures SOURCE: Adapted from HHS Data Council (2007).
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary Key Infrastructure Barriers, Issues, and Suggested Next Steps A number of infrastructure issues must be resolved to move to a true learning health system. The good news is that work is under way on some identified issues and others may be relatively inexpensive to resolve. Later chapters will address the political and competitive barriers and issues regarding a learning health system. A barrier in the current healthcare data environment to implementation of the frameworks and taxonomy discussed is the wide distribution of data across the system and significant fragmentation of the data. Given the fact that the national healthcare enterprise consumes 16 percent of the gross national product and given the complexity of the human organism, it is not surprising that the system would be complex and the data systems complex. Currently, data are collected and held in many places—by the patient, provider(s), payers, and government repositories for public health and planning purposes, to name a few. Some of the data held are discrete and unique, and in other cases an extract or copy of data produced as a result of a healthcare interaction or event is stored. Few places, however, have comprehensive, longitudinal views about individuals. The inability to connect data that may include risk factors, medical history, and interventions in a comprehensive way is a fundamental flaw in moving forward. The hopeful news is that the vision of the NHIN is intended to help consolidate the data, but we are rife with fragmentation of health and healthcare data at this point. In addition to the fragmentation of data, the data itself represented in the framework and taxonomy are heterogeneous. Some of the data—such as diagnosis (ICD), procedure (CPT), medication (National Drug Code or NDC), and other administrative data as required in HIPAA administrative and financial transactions—are usually of relatively high quality, coded, and computerized. Laboratory data are becoming increasingly standardized and codified; however, most other data are not available in a computerized form, or are generally in free text even if computerized. EHRs offer the opportunity for computerization and codification of additional key data elements; however, there is limited penetration of EHRs and thus “incomplete” computerization of data in health care. Another issue of concern is variation in the timeliness of data. Timing ranges from clinical data (coded or not) being almost immediately available, at least for caregiving, to coded administrative data, which may take days or weeks to become available, to health statistics in government repositories used for planning purposes or research databases, which may lag by 1, 2, or more years. Lest readers react in despair about the widely distributed nature of the data, uneven data quality, and time delays, previous testimony has
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary highlighted what we have learned from the current distributed environment. In particular, institutions such as Mayo, Kaiser Permanente (KP), the Veterans Administration (VA), and the Department of Defense (DoD) have longitudinal stores of relatively comprehensive, high-quality information on their patients. This is infrastructure that can be leveraged now to help identify evidence-based best practices. Certainly, the work of the ONC and HHS toward the vision and instantiation of the NHIN needs our support. Various initiatives that are also under way to help consolidate healthcare data for important purposes such as quality measurement deserve ongoing support and encouragement. Considerations During the National Transition to EHR To achieve the goal of having most decisions based on evidence as we move toward widespread EHR implementation, two focuses are needed. First, we need to be able to identify those evidence-based best practices, then we need ways to communicate those best practices to the clinician in a way that supports work overflow and high-quality clinical care. The first focus, which is extensively discussed in this roundtable report, relies heavily on access to comparable and standardized data. Such data standardization and comparability, as we move towards fuller use of EHRs, requires uniform healthcare messaging standards (e.g., HL7 messages), an area that has received significant national attention, and robust healthcare terminologies and classifications, an equally important requirement that has, until recently, received much less attention As EHRs are being implemented, they are increasingly using clinical terminologies to codify their data, such as the Systemized Nomenclature of Medicine (SNOMED) and Logical Observation Identifiers Names and Codes (LOINC). Thus, for some time to come, we will need to consider strategies that can leverage claims, administrative health data, and the more specific, clinically rich information that is expected to come from EHRs. In 2003–2004, the NCVHS looked at this transition issue and recommended a set of clinically rich terminologies to form a core for EHRs, calling for an aggressive mapping strategy between these and the HIPAA-mandated terminologies and classifications. The National Library of Medicine was asked to take the lead on this, but the mappings have been notoriously difficult (especially trying to map an archaic ICD classification to more modern clinical terminologies). Another problem is that both sides of the mapping have ongoing changes, so the mapping requires significant upkeep and runs the risk of being inaccurate. We are encouraged by the current discussions about harmonization between SNOMED and ICD and plans to develop ICD-11—building off of an ICD-10 base, which includes plans to develop the clinical richness
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary of SNOMED with the classification discipline and international use of ICD. Linking administrative classifications and clinical terminologies could become an important tool and part of a transition strategy to help maximize the use of computerized data through both the transition to EHRs and the newer versions of ICD. Issues of concern remain, however. These include lack of adequate funding—the ICD-11 classification development work, for example, is currently funded mostly by the Japan Hospital Association. It is in our own national self-interest to get behind this as a way to ensure maintenance of the value of our data as we continue the transition to more current classifications and EHRs. A second issue is that U.S. representation needs to be further strengthened. We need to have a strong voice in how this goes forward because it will be an important piece of the infrastructure. Other data terminology issues remain as we move forward with clinical interoperability and the implementation of standards and clinical terminologies to support MMA e-prescribing and the transition to EHRs and the NHIN. Clinically rich data, all standardized and interoperable, will provide a fertile environment for the learning health system, but many of these terminologies will be stretched to their limits. Unforeseen problems will need to be remedied. The bottom line is that federal terminology development and improvement initiatives are extremely underfunded. Furthermore, we will need adequate funding to fix problems and fill gaps as these standards and terminologies go into wider use. Lack of quick action to fix problems will slow widespread adoption of EHRs and may undermine the NHIN. We are not talking about a huge amount of money: Funding in the range of $10 million per year may be sufficient to deal with both U.S. contributions and these real-world data issues. The second critical issue is communicating evidence-based best practices to the clinicians in a way that supports workflow and high-quality clinician care—in other words, optimizing clinical decision support (CDS). Determination of best practices is critical, but the rate is limiting step may be getting that information to the busy care provider at the point of care in a way that is useful and actionable, and will impact decision making. These practices range from flu shot reminders to warnings about potential medication complications, and the number of evidence-based guidelines and recommendations continues to explode. There is no lack of evidence-based practices. (Most physicians have binders full of them written by their own organizations, by specialty societies, by accrediting organizations, by governmental organizations, etc.) For example, the Agency of Healthcare Research and Quality has 342 guidelines in its national clearinghouse on cardiovascular disease alone. Unfortunately, although CDS exists in many healthcare organizations that have EHRs, it is generally proprietary and nonstandardized, and there
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary FIGURE 2-8 Memorandum dated December 8, 1989, from Claude Lenfant, Director of the National Heart, Lung, and Blood Institute (NHLBI), to division directors regarding public release of data generated by large institute-supported studies. requirements according to the Common Rule, as outlined in 45 C.F.R. 46 (NIH public access, 2008). Plans to share widely complex genetic data have raised new concerns about the level of consent needed, as reflected in the NIH’s recently released Genome-Wide Association Studies policy (National Heart, 2007). In one program, the Personal Genome Project, an “open consent” model is being proposed by which adults volunteer to give DNA samples along with health information with the understanding that their data will be widely available and that there are no guarantees of anonymity, confidentiality, and privacy (Lunshof et al., 2008). Many clinical data are produced as part of industry-supported clinical trials. These data are typically not made available to the public or even the general scientific community. Failure to share data exists on several levels.
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary Results of many trials are never published, leading to a biased impression about the efficacy or effectiveness of some treatments, such as antidepressants (Turner et al., 2008). In other cases, trial data that are not published can be obtained in incomplete format by researchers with affiliations with the Food and Drug Administration (FDA); these have been used, for example, to generate suspicions of the safety of commonly used drugs, such as rofecoxib (Vioxx) (Mukherjee et al., 2001) and rosiglatizone (Avandia) (Nissen and Wolski, 2007). On a more fundamental level, data may be published in aggregate form, yet access to raw data may be limited or delayed to academic researchers, as recently occurred in a multicenter trial of the cholesterol-lowering drug ezetimibe (Berenson, 2007). Some sectors have taken steps to maximize access to clinical trial data, at least for academic researchers. A coalition of journal editors have required authors to attest to having access to all data (Davidoff et al., 2001), to having had clinical trials registered on a public forum (e.g., www.clinicaltrials.gov) (Laine et al., 2007), and, for some journals, to having obtained independent statistical analyses (DeAngelis and Fontanarosa, 2008). Recent federal legislation requires publicly funded research publications to be posted on a government website (National cardiovascular data, 2007) and requires results for many clinical trials, whether publicly funded or publicly noted, to be made publicly available. Summary and Closing Thoughts For many decades, researchers and clinicians have taken advantage of many sources of rich clinical and population-based data to generate new insights, stimulate major research programs, and develop robust clinical guidelines. The story of the cholesterol hypothesis is an excellent example of the power and limitations of clinical and population-based data. Epidemiological cohort studies established and described the strong link between blood cholesterol levels and cardiovascular risk (Kannel, 1995). These observational findings led to a reasonable, but unproven (Moore, 1989), hypothesis that lowering cholesterol could improve health. Drugs were developed that could reduce cholesterol levels, with some (Baigent et al., 2005), but not all (Barter et al., 2007), eventually shown in randomized trials to yield substantial improvements in patient outcomes. Postmarketing surveillance studies demonstrated the safety of statins; however, one exception, cerivastatin, was found to have an unacceptably high risk of a rare side effect, rhabdomyolysis, leading to withdrawal of that drug from the market (Graham et al., 2004). The cholesterol story illustrates the value of observational data for generating hypotheses and detecting safety signals, while also illustrating the critical role of randomized trials to generate robust evidence in support of specific therapies.
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary If the Institute of Medicine’s evidence-based medicine goal is to be realized, clinical data must be recognized as a staple that should be widely available and integrated. Examples from abroad and from some U.S. health systems, such as HMORN and the VA, demonstrate that it is possible to incorporate rigorous and prospective data collection into routine clinical care. Still, most clinical data are not collected at the point of care in an easily retrievable manner and most are organized in isolated silos that are difficult for many analysts to access. Even if a “data paradise” could be achieved with universally obtained and available clinical data, there is concern that policy leaders may place too much reliability on these largely observational datasets for generating evidence-based recommendations. Observational analyses of treatments must be recognized as inherently biased because of failure to take into account selection biases and unmeasured confounders. Modern statistical techniques and collection of more data elements may reduce these biases, but even with large numbers of observations, biases are still biases. I do accept the notion that a national priority for growing, sharing, and analyzing vast quantities of observational, clinical, and population data is an essential element toward reaching a vision of routinely practiced evidence-based medicine. This will only be true, though, if accompanied by a healthy dose of skepticism and recognition that, just as in Lord Kelvin’s day, well-designed experiments are also critical for building a scientific evidence base. HEALTH PRODUCT MARKETING DATA William D. Marder, Ph.D. Senior Vice President and General Manager, Thomson Healthcare Three major types of data are used by public and private entities to market healthcare products and services: health survey data, information about general consumption patterns, and administrative data generated by the healthcare delivery system. Private health survey data are patterned after government-sponsored surveys such as the National Health Interview Survey (NHIS) or the Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS). Analyses drawn from general consumption patterns and market segmentation data keyed to census tract data can guide modeling behavior and messaging strategies. Much of the information about patient/consumer attitudes comes from this source. The administrative data include retail store sales data, patient eligibility and medical claims data, and a growing availability of short- and long-term disability claims data as well as health risk appraisal data. This paper describes
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary use of these data assets by providers and pharmaceutical companies and the business models that support collection of the data. The administrative data assets are often used in retrospective database studies to examine the cost effectiveness of interventions in the general population (outside the context of clinical trials, where both providers and patients are strongly encouraged to be on their best behavior). The interaction of private data assets and academic research will be discussed, including how access to data can be provided for replication of results. The fundamentals of marketing are often described as a mix of “four Ps”: Product, Price, Place/Positioning, and Promotion. In health care, many entities conduct marketing efforts that blend these factors to strategic advantage. Healthcare entities that engage in marketing include physicians, hospitals, pharmaceutical companies, device suppliers, and government agencies. The range of marketing activities in which these entities engage can be vast and varied. Examples might include planning for a new ambulatory surgery center, gaining acceptance for a new antidepressant, introducing a generic version of an established drug, raising mammography rates, or increasing enrollment in Medicaid or the State Children’s Health Insurance Program. Historically, health surveys have relied on government-collected data, long considered the reliable gold standard. Such data are not particularly helpful as marketing data, however, in that they tend to be fairly old and not easily linkable to general marketing tools. Those circumstances create an opportunity for the private sector to develop marketing data that are more current and linkable to marketing tools. The marketing of health products is a thriving industry. Marketing to the public draws on lessons learned and information gained in the work of specific, targeted marketing such as the examples just cited, and also relies on information from additional sources, such as census data on population characteristics in small areas and customer buying habits. Increasingly, data compiled in support of the marketing of health products are being linked to health behaviors. Within the private sector, many such marketing surveys exist. One example is the Thomson PULSE Survey, a questionnaire modeled on the NHIS. Based on a random telephone survey of 100,000 households per year, with replicates of 10,000 per month, 10 months per year, the survey offers results that are available 3 weeks after the close of each month in the field, identifiable by the census tract of the respondent. The survey is linkable to other census tract data, including socioeconomic characteristics of a particular census area and lifestyle modeling done by general marketing firms. In addition to this type of survey, there are provider-funded customer satisfaction surveys modeled on or incorporating HCAHPS. The Thomson PULSE Survey models healthcare use as a function of household and neigh-
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary borhood characteristics. Such models can then be integrated into software products that can help drive marketing and planning decisions of entities such as hospitals, government agencies, contract research organizations, and pharmaceutical companies. As an example, assume that we want to find the best groups for clinical trial participation in “anytown,” “anystate,” ranked by clinical trial participation. The popular PRIZM (Potential Rating Index for ZIP Markets) system provides a standardized set of characteristics, known as clusters, for each U.S. ZIP Code. PRIZM is the nation’s leading marketing segmentation system. (See www.claritas.com for more details, which are on a PRIZM poster available from Medstat.) Medstat licenses the system from Claritas and puts its unique health information into the system, especially disease prevalence information. Claritas assigns each block group to a PRIZM lifestyle segmentation cluster based on numerous demographic and socioeconomic variables, including age, income, population density, education, occupation, homeownership, and household composition. Media data come from Simmons Media Research Bureau, which conducted a separate survey of 50,000 households by PRIZM cluster. Answers to the PULSE Survey also help create the clusters. The objective of the clusters is to separate the population into groups that have strong differences in purchasing and health behaviors. Using such an approach, we can, for example, pinpoint a target demographic group of blue-collar or farm couples, aged 35–54, who are high school graduates and owners of single-family dwelling units (our sample turns out to include a notable number of mobile homes). In terms of income, our group ranks at 45 out of 66 clusters. Mining the available data, we can determine that our group might be more likely than others to do crafts and needle work, go freshwater fishing, read Flower & Garden magazine, listen to country music, and own a Chevrolet Silverado. We can also differentiate that this given group has 7,069 patients who participated or seriously considered participating in a clinical trial, in contrast to a similar but slightly different group that has just 75 patients who were inclined to take part in a clinical trial. Marketing data analyses draw on a rich abundance of administrative data that offer both advantages and shortfalls. Retail store sales data, for example, can include information on pharmaceutical use; available quickly, such data can sometimes identify the prescribing physician. There are billing service or product-switch data. Although these data are quickly accessible, they can sometimes be incomplete; such data can provide information on medical and pharmaceutical claims. There are health plan data, which have information on eligibility and claims for covered services, but may miss carved-out services. Finally, there are employer-based data, which can include eligibility and claims for covered services, sometimes include health
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary risk assessment data, and offer information on short- and long-term disability and worker’s compensation claims. Claims data offer significant marketing uses. Such data can be applied tactically—retail and product-switch data can be used, for example, to identify the effect of marketing campaigns and for measuring sales force effectiveness. At a perhaps more strategic level, claims data can offer insights for evaluating unmet medical needs, understanding the cost of acquiring a drug in a broader context, pricing new products, gaining favorable formulary position, and convincing prescribers about the value of a drug. The development of healthcare marketing data is also informed by the FDA’s encouragement of peer review. Strategic marketing goals can be accomplished by publishing material that meets peer-review standards. A substantial group of researchers address this need. The International Society for Pharmacoeconomics and Outcomes Research (www.ispor.org), for example, promotes the science of pharmacoeconomics (health economics) and outcomes research (the scientific discipline that evaluates the effect of healthcare interventions on patient well-being, including clinical outcomes, economic outcomes, and patient-reported outcomes) and facilitates the translation of this research into useful information for healthcare decision makers to ensure that society allocates scarce healthcare resources wisely, fairly, and efficiently. A combination of private/public, not-for-profit/for-profit entities contribute to this literature. Overall, the process means that for-profit entities that contribute data and research must develop strategies that are consistent with academic standards. Given that the collection of such data can be expensive, there must be a revenue stream to offset data collection costs. In the case of the Thomson PULSE Survey, for example, the revenue stream comes from the use of the data in marketing and planning tools sold to providers and suppliers. Revenue also covers licensing of general marketing information. As for the funding of administrative data, the costs of retail and product-switch data are largely covered by pharma. Health plan and employer data are largely covered by the operations of payer organizations, with additional support from consultants serving many organizations, including pharma, government, benefits consultants, and reinsurance companies. Licensing data in a for-profit setting has considerable benefits. Licensing helps customers achieve their goals by making data easy to use and to be sorted based on their interests in particular aspects of marketing’s “four Ps.” For license holders, the process of licensing offers the capability to market assets developed at considerable expense, and to recoup some of the costs of developing the data. This area raises considerations about how to best manage the intersection and interaction of private data assets and academic research. Although there is an inherent challenge in balancing the costs and benefits of making data available to students and researchers,
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Clinical Data as the Basic Staple of Health Learning: Creating and Protecting a Public Good - Workshop Summary it is important that channels be maintained to make data available at no charge to academic audiences and to ensure access to data for replication of results. In terms of applications of clinical data for marketing, not many such data are now available. The best of what are currently available are lab-result data linked to claims. Health plans are in the best position to acquire data from national labs. The comprehensiveness of such data can be checked relative to claims. At the same time, however, population-based comprehensive clinical data are not “right around the corner,” as sometimes suggested, but are likely to be realized only at some point in the future. Registry data are not generally available for marketing purposes. In sum, marketing data can be seen as a synergy of inputs and interests from a variety of entities. The public sector provides raw material and models of data collection, at minimal cost. The private sector builds databases with clear commercial value that fill needs suggested by, but not covered by, public sources. As electronic medical record systems become more common, one can envision a blend of databases that draws on both public and private data sources—the mix will depend on government willingness to fund aggregation. REFERENCES Baigent, C., A. Keech, P. M. Kearney, L. Blackwell, G. Buck, C. Pollicino, A. Kirby, T. Sourjina, R. Peto, R. Collins, and R. Simes. 2005. Efficacy and safety of cholesterol-lowering treatment: Prospective meta-analysis of data from 90,056 participants in 14 randomized trials of statins. Lancet 366(9493):1267–1278. Barter, P. J., M. Caulfield, M. Eriksson, S. M. Grundy, J. J. Kastelein, M. Komajda, J. Lopez-Sendon, L. Mosca, J. C. Tardif, D. D. Waters, C. L. Shear, J. H. Revkin, K. A. Buhr, M. R. Fisher, A. R. Tall, and B. Brewer. 2007. Effects of torcetrapib in patients at high risk for coronary events. New England Journal of Medicine 357(21):2109–2122. Benson, K., and A. J. Hartz. 2000. A comparison of observational studies and randomized, controlled trials. New England Journal of Medicine 342(25):1878–1886. Berenson, A. 2007. Cardiologists question delay of data on two drugs. New York Times. 2007. http://www.nytimes.com/2007/11/21/business/21drug.html# (accessed August 10, 2008). Cannon, C. P., C. M. Gibson, C. T. Lambrew, D. A. Shoultz, D. Levy, W. J. French, J. M. Gore, W. D. Weaver, W. J. Rogers, and A. J. Tiefenbrunn. 2000. Relationship of symptom-onset-to-balloon time and door-to-balloon time with mortality in patients undergoing angioplasty for acute myocardial infarction. Journal of the American Medical Association 283(22):2941–2947. Caulfield, T., A. L. McGuire, M. Cho, J. A. Buchanan, M. M. Burgess, U. Danilczyk, C. M. Diaz, K. Fryer-Edwards, S. K. Green, M. A. Hodosh, E. T. Juengst, J. Kaye, L. Kedes, B. M. Knoppers, T. Lemmens, E. M. Meslin, J. Murphy, R. L. Nussbaum, M. Otlowski, D. Pullman, P. N. Ray, J. Sugarman, and M. Timmons. 2008. Research ethics recommendations for whole-genome research: Consensus statement. PLoS Biology 6(3):e73.
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