Transformation. Healthcare Data. Also: Harnessing Data to Reduce Fraud

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1 Volume 12 Number 1 September 2012 Healthcare Data Transformation Data Sources and Stakeholders Toward a Data Value Chain Aggregation and Interoperability Also: Harnessing Data to Reduce Fraud

2 Leading the future with innovation, imagination, and results. Noblis uses science, technology, and strategy to help clients solve complex scientific systems, process, and infrastructure problems in ways that benefit the public. noblis.org

3 FROM THE EDITOR-IN-CHIEF From Big Data to Healthcare Quality H. Gilbert Miller Corporate Vice President and Chief Technology Officer Noblis Arguably, providing affordable quality healthcare to all individuals is one of society s grand challenges that requires extraordinary effort from clinical research to bedside practice. The past decade has seen a shift toward greater accountability in providing care, as mandates such as the Patient Protection and Affordable Care Act combine with technological advances and social awareness to redefine quality-of-care standards. As always, technology and evolving user requirements continue to expand both data volume and variety. Waiting rooms are giving way to sensors that stream data to practitioners as patients move about their daily regimens, augmented reality enables doctors to see inside a moving patient without surgery, hospital systems are replacing paper with electronic forms, social networking is creating stakeholder communities to address specific healthcare issues. In short, data is coming from every imaginable source, often in real time, and the demand for quality care has never been higher. Given this perfect storm of outcome-based mandates, enabling technology, and demanding stakeholders, the healthcare industry is faced with a pressing task: how to blaze a path from data to healthcare quality. Other industries are enthusiastically embracing the idea of big data analytics sophisticated methods to measure quality in terms of data volume, velocity, variety, and veracity (is the data trustworthy?) and use the results to channel the right data to the right stakeholders at the right time. In the healthcare industry, however, big data has a somewhat fuzzier role because value is in terms of less quantifiable ideas such as patient satisfaction with treatment outcome. In other words, larger, faster, and more varied does not automatically translate to more useful. In this edition, we look at efforts to find that path to healthcare quality. An essential first step is to define data sources as well as new and evolving stakeholders all of which are forming novel connections that have the potential to dismantle long maintained data silos. Efforts such as the data value chain a framework to manage data holistically from capture to decision making aim to promote collaboration and data sharing as the way to quality. Applications of such a structure are nascent but steadily growing, as the examples in this Sigma edition illustrate. Understanding big data s potential and exploiting its role in healthcare delivery are commissions that demand the breadth of multidisciplinary approaches. To that end, we have opened this edition to colleagues who have been working with Noblis on healthcare-related projects. In an environment in which collaboration and shared data are key elements, it is fitting to offer our own microcosm of blended contributions, which provide a more wellrounded look at both the opportunities and obstacles ahead. HEALTHCARE DATA TRANSFORMATION 1

4 Editor-in-Chief H. Gilbert Miller, PhD Healthcare Transformation Edition Editors Catherine H. Veum Jacqueline M. Paranzino Editorial Staff Nancy Talbert S. Denise Murphy Production Staff Carolina Cabanillas, Lead Designer Jennie Doran, Designer Kenneth Arevalo, Designer Sigma is a publication of Noblis Noblis: We are the impartial people who use the best of science and technology for the best of reasons to do what s right and what works for our clients and for the public good. Noblis publishes Sigma to further its work in the public interest. Sigma seeks to inform public interest decision makers of recent advancements and accomplishments in a wide variety of scientific and technological areas, including information technology, networking, and life and chemical sciences. Noblis authors base the Sigma articles on experiences and knowledge gained from our public interest client work program and our Noblis Research program. For more information about Noblis, please visit our website, noblis.org, or call Corporate Communications at Volume 12 Number 1 September 2012 Approved for public release, distribution unlimited 3150 Fairview Park Drive South Falls Church, VA Copyright 2012 Noblis, Inc., All Rights Reserved

5 Contents Features 4 What Does Data-Driven Healthcare Look Like? John M. Young Biomedical research advances, rapid technology adoption, and care delivery innovation are combining with consumers thirst to actively engage in their healthcare, creating an explosion of disparate structured and unstructured datasets. 11 Turning Data into Information Jacqueline M. Paranzino, Peter Mork, and Catherine H. Veum Increased data does not automatically yield deeper insights, particularly in a culture where stakeholders do not readily integrate data. A data value chain from collection to decision making can lead to greater information sharing, novel care-delivery and cost-reduction solutions. 21 The Art and Science of Data Aggregation Joseph C. Nichols, MD Quality data does not automatically translate to useful information. Data aggregation is an essential interim step that must be as accurate and reliable as the data itself. 28 Linking Data from Discovery to Understanding M. Elizabeth Avila, Christian M. Curtis, and Peter Mork With increasing pressures to improve outcomes and maximize efficiency, no healthcare enterprise can remain a silo. But interoperability is more than just connecting enterprises. It must enable data understanding as well. 33 Harnessing Big Data in the Fight Against Fraud William J. Mahon Big data has the potential to improve the public and private sectors management of healthcare fraud, but a variety of challenges are involved in realizing that potential. 38 In Depth: Value-Based Healthcare Purchasing: Will Data Growth Be the Catalyst? John M. Young and Paul T. Breslin Value stems from high-quality care that is consumer-centered and low cost. Bigger data brings the opportunity to realize value-based payment reform, which depends critically on making diverse, disparate data sources transparent to a variety of stakeholders. Departments 20 Sigma In Addition More from Noblis authors 44 Sigma Spotlight Big Data and Predictive Modeling David C. Roberts A Noblis team is part of an open competition to predict patient hospitalization rates based on prior years medical claims. Data analysis is already revealing interesting patterns.

6 Data Growth and Healthcare Transformation What Does Data-Driven Healthcare Look Like? John M. Young Biomedical research advances, rapid technology adoption, and care delivery innovation are combining with consumers thirst to actively engage in their healthcare, creating an explosion of disparate structured and unstructured datasets. With the knowledge age has come a proliferation of new data types and an escalation in data volume and speed. The energy, defense, aeronautics, space, and transportation industries have always had datasets and computational problems that require highperformance computing using the largest available clusters and supercomputers. The healthcare industry, however, has been a late adopter, in part because it is distributed as thousands of micro healthcare-delivery systems with incongruent, fragmented technology platforms across the United States. This picture is changing with the increasing use of widely distributed healthcare information systems. Advances in biomedical research and the rapid adoption of interoperable electronic medical records (EMRs) and telemedicine will make it easier to provide access to healthcare while reducing healthcare costs. New payment and care delivery innovations, such as accountable care organizations (ACOs) and bundled payment models are combining with consumers thirst for healthcare information to deliver precise patient-centered medicine, effective ways to engage patients, and data-driven approaches to identify wasteful spending patterns and prevent fraud. This expansion of digital medical technology is converging with other powerful trends in disciplines such as housing, politics, epidemiology, and economics to create an explosion of data in every aspect of healthcare. Indeed, in 2007, biomedicine and healthcare made up a sixth of the U.S. gross domestic product 4 A NOBLIS PUBLICATION

7 ( and data scale and scope continue to grow accordingly. As the sidebar Defining Big Data in Healthcare on p. 8 describes, data s sheer volume is not the only concern. Within that volume are valuable relationships among datasets, implying that data integration can expose new information that was not discoverable in the past. However, integrating increasingly more data types raises computational complexity, which in turn demands a corresponding increase in information technology support beyond what is required to handle the raw datasets. Thus, big data captures the idea that the aggregate size and complexity of these datasets (both structured and unstructured) exceeds the capacity of typical database software tools to capture, store, integrate, report, and analyze them. Extracting information from big data requires new approaches and enhanced tools. And developing these resources requires answers to some key questions: Who are the stakeholders the data producers and consumers? What kinds of data are there? Will integration of new types of data facilitate precision in care delivery? How does the legislative environment promote standardized data collection, reporting, and interoperability? In short, how will data-driven healthcare be different? Stakeholders From government agencies that develop healthcare regulations, to individuals who require evidence-based information for treatment decisions, the kinds of healthcare stakeholders are many and diverse. The sidebar Stakeholders and Their Data Concerns on p. 7 describes seven broad classes of stakeholders, all of which have a defining interest in how healthcare impacts an individual, community, state, or country. Although the sidebar lists seven classes, there is no universally accepted stakeholder grouping. Arguably, any group with an identifiable common ground can be a stakeholder class, but in most contexts, these seven stakeholder classes suffice: individuals, providers, payers, employers, government, technology vendors, and researchers. Viewing the healthcare enterprise from the position of each of these stakeholder classes is essential to understanding how to improve system processes and service delivery. Clearly, a hospital s view of the healthcare system differs considerably from a patient s or an insurer s view, for example. Regardless of their context or role in the healthcare ecosystem, all stakeholders share a persistent need for data data that must be transformed into knowledge. Each stakeholder class can generate, store, and consume numerous data types, and each stakeholder will have data that is both the focus of its mission and required to properly execute its mission. Figure 1 depicts three of the seven stakeholder classes with examples of data they generate, consume, and exchange with other stakeholder classes. Providers Insurance Coverage Demographics Symptoms Family History Medications Taken Viewing data in terms of stakeholders underlines the relationships among data types. For example, data can have a second, sometimes unanticipated use aside from its original purpose: one stakeholder s administrative data could be another stakeholder s raw input data. Providers consider Medicare claims records to be administrative data, while comparative effectiveness researchers reanalyze the same data to discover efficacy differences among treatments. A physician will transfer relevant data from a patient s medical record to a hospital or diagnosis data to a payer, while the hospital receives eligibility data from a payer. Inside Track Treatment Plan Appointment Time Specialist Referral Individuals BIG DATA Encounter / Clients HEDIS Unique Identifier Prescribing Patterns Eligibility Data Payment Remittance Provider Report Cards Health Literature Provider Network Covered Benefits Dependents Patient Experience Information Payers Figure 1. Stakeholders exchanging information. Each stakeholder class may generate, store, and consume numerous types of data, but each stakeholder will also have data that is required to execute its mission, even though the data is not the mission s focus. Thus, the same data can support both a primary mission, such as patient care, and a secondary mission, such as billing. HEDIS: Healthcare Effectiveness Data and Information Set. Stakeholders collect, integrate, and analyze a variety of datasets to identify new patterns and realize efficiencies that can be leveraged in emerging research, epidemiological surveillance, health homes, and policy development. The complexity of integrating disparate healthcare, social and public health data at unprecedented volumes will drive development of new technological tools and strategies that facilitate more precise healthcare decision making. Global health data, synthesized with big data from many sources including electronic health records, genomic/biological data, social networks and many other clinical and nonclinical knowledge bases will emerge as a catalyst to achieve personalized medicine and improved public health in communities. As the types of healthcare data continue to grow exponentially, healthcare data taxonomies and classification systems will facilitate data transparency, standards development, and stakeholder data sharing. HEALTHCARE DATA TRANSFORMATION 5

8 Basic Research Clinical Trials Surveillance, CER and Population Research Basic Research Data Genomic Data Metabolic Pathways Molecular Interactions 3-D Protein Structure Phase 1 Safety Data Pharmacodynamics (how the drug affects the patient) Pharmacokinetics (how the body deals with the drug) Phase 2 Safety and efficacy data. Depending on protocol, any of a wide range of data relevant to the treatment is collected. Phase 3 Wide range of safety and efficacy data is collected to support labeling and regulatory compliance. A wide range of datasets collected as part of healthcare delivery Food and Drug Administration (FDA) Adverse Event Reporting System (drugs/biologics) FDA Manufacturing and User Facility Device Experience (devices) Insurer claims databases Medicare claims databases Centers for Disease Control and Prevention epidemiological and population datasets EMR for Clinical Research EMR for Clinical Research Administrative Data (each part of the biomedicine/healthcare has its own data) EMR (MHS, VA, Civilian) Figure 2. Data along the translational continuum. The continuum reflects the scope of the NIH s original bench-to-bedside translational medicine initiative in which dimensions such as staff needs and computational requirements feed into administrative data. After basic research, data enters the EMR, which serves to support healthcare delivery and patient management. Data along the translational continuum Even the few examples of information exchange in Figure 1 illustrate the complexity of healthcare data types and their possible applications. To understand the immense span of data types, it helps to view the healthcare landscape as a continuum, such as that in Figure 2. The National Institutes of Health s (NIH s) translational medicine initiative, which describes benchto-bedside healthcare research, connects biomedical researchers with patients being treated in clinical research. The National Cancer Institute s Translational Research Working Group extends the continuum all the way to community and population health to reflect the period after a treatment s wide adoption. The idea behind translational medicine is to use patients data (when feasible) as research inputs and then feed the emerging research results back into patient care. As the figure shows, an expansive range of data types are under the translational medicine umbrella, including a variety of research, clinical, and administrative data for all seven stakeholder classes. As the continuum extends beyond the scope of controlled research, valuable data comes from surveillance and comparative effectiveness research (CER), including pharmacovigilance and other efforts to improve efficacy and reduce costs. Faced with this imposing research and delivery continuum, efforts are turning to the creation of data standards to support interoperability, reuse, and integration. Among these standards are requirements for specific metadata and the use of controlled terminologies. Traditionally, researchers established medical terminology systems primarily to facilitate record keeping, but the continuum vision, particularly translational medicine, requires that data generated in clinical settings be usable as biomedical research input. At the same time, the use of controlled vocabularies and ontologies has exploded in biomedical research, spurred by the arrival of high-throughput molecular biology and digital instrumentation. Clinical trials data can be sizable but generally involve relatively few participants. The bench-to-bedside philosophy is changing that trend, transforming every willing person into a longitudinal study participant who provides EMR data to researchers. Consequently, enormous volumes of data are becoming available for a translational study. In the continuum s surveillance and CER stage, the emphasis is on approved drugs, devices, and procedures used in care delivery and disease management. To support this stage, researchers are experimenting with social networking technologies to connect physicians to patients. Physicians use software that acts as a personal coach as well as sensors attached to patients, both of which gather data and stream it to the physician data that eventually becomes part of the patient s EMR. As it matures, telemedicine will see broad adoption in rural, urban, and military healthcare settings, enabling virtual office visits for certain types of conditions. These virtual episodes of care will dramatically increase the amount of claims data, clinical record notations, and public health data, thus further extending the boundaries of data aggregation and data analysis. Data size and complexity Healthcare-related data is growing so rapidly and in so many ways that estimating its size would be impractical if not impossible. And when global health data is integrated into the equation for analysis to improve community health and mitigate emerging epidemics, the size and complexity of the data is unfathomable. Only a decade ago, whole genome sequencing was an enormously expensive scientific research project. But 6 A NOBLIS PUBLICATION

9 Stakeholders and Their Data Concerns Bernardo G. Gonzales III The past century has seen a shaping and reshaping of the healthcare model. Doctors, once the single contact point for managing and delivering medical care, are now one of many parties involved in providing healthcare services, joining employers, insurance companies, pharmacies, researchers, and oversight organizations. Narrowing this variety of stakeholders into specific classes is difficult, but these seven classes capture the nature of the people, organizations, and entities that make up healthcare. Individuals: Traditionally, anyone who has accessed healthcare or clinical services. Viewed in total, individuals can include anyone with health to manage (in other words, everyone). Providers: Individuals, groups, and organizations that offer direct clinical service, including but not limited to physicians, hospitals, medical laboratories or testing sites, nursing homes, and home health agencies. Payers: Those entities that support the market as financial conduits, including managed care organizations and the Medicare/Medicaid entitlement programs. Employers: Unique to the U.S. market because a large portion of healthcare insurance is derived through employment. This class includes employers that offer healthcare as a benefit. Government: Groups and organizations who are responsible for the oversight and monitoring of healthcare delivery to particular standards. This class includes government regulators and quality assurance organizations. Technology vendors: Industry components that provide the healthcare industry with a technology and information systems infrastructure. Technology vendors include software and hardware vendors, as well as information technology service organizations. Researchers: Those who provide research studies and analytics specifically for improving healthcare, including academic institutions and think tanks. These information brokers comprise educators, students, and scientists. Each stakeholder class comes with a different perspective, but all stakeholders have a common interest in how data can improve their value to the healthcare system and how to best use and generate the information within that data. Table A lists these needs and concerns for the seven stakeholder classes. As the table implies, in addition to each stakeholder s desire to be compensated, stakeholders across the industry are interested in streamlining their systems and increasing their effectiveness. Their data needs clearly show that stakeholders are interested in effectively managing their resources. With accountable care and value-based medicine now a reality, transactions are coming under increased scrutiny. Are certain procedures necessary? What is the value of prevention versus cure? What types of care should be covered under healthcare policies? These questions are critical to healthcare reform, and the answers will come from data points collected, collated, and condensed, such that each stakeholder, at the highest level, can make informed decisions on how to move forward. Bernardo G. Gonzales III is a principal at Noblis, where his experience includes Medicaid managed care programs, healthcare effectiveness data and information set methodology and analysis, report development, and data warehousing and analytics. Contact him at bernardo. gonzales@noblis.org. Table A. Healthcare stakeholder classes and their data needs and concerns. Stakeholder Class Health-Related Data Needs Concerns about Health-Related Data Individuals Understandable clinical information, improved data mobility, Access to care, affordable care, security and privacy of personal better decision-making and care coordination data Providers Payers Performance-based payments, reduced administrative paperwork, better care coordination, and improved patient compliance with treatment plans Rational reimbursement rate, ability to measure value and quality, and improved cost management Additional regulations and paperwork and increased uncompensated care Administrative burden Employers Data mobility and affordable health Cost control and worker health Government Technology Vendors Program integrity, increased efficiency, compliance policies, quality measures Better health outcomes and lower healthcare costs Protocols for data systems development and data exchange Budget for infrastructure change, prioritizing resources, quality of care Data system interoperability, increasing storage capacity, and cloud technology Researchers Analytical information and data access Data integrity HEALTHCARE DATA TRANSFORMATION 7

10 Defining 'Big Data' in Healthcare The phrase big data is common to many industries, all of which acknowledge that the aggregate size and complexity of disparate datasets (both structured and unstructured) exceed the capacity of traditional database technology to capture, archive, and analyze them. The exact qualification of big is more elusive. As hardware and software increase computational power and storage capacity, the expanding limits of these resources provide the only practical and reliable sense of what that word means. Indeed, some industry analysts suggest that big data is more a philosophy than a quantification of size: an organizational culture that embraces the complexities of integrating, analyzing, and transforming vast amounts of data into a valued organizational asset. In the last 20 years, computers have moved out of institutions, onto people s desks and into their handbags and pockets creating more than a billion computational devices generating and consuming data of increasing variety. Although each individual is typically generating or consuming a modest amount of data, collectively a billion small computers represent enormous computational capacity. Aggregating millions of such data sources requires powerful computational resources, such as clouds or supercomputers. While an aggregated dataset can indeed be large, the analytical questions about it often involve the interactions of the people or sensors generating the data. The complex nature of these interactions is what makes an already large dataset big data. In the context of this Sigma edition, big data refers to data of any type or from any source that is large and complex enough to require the most powerful computing resources to manage and process it productively. A key part of this definition is data of any type or from any source. Big data poses challenges that industries such as defense, transportation, and banking do not face. Patient data takes many forms, from individual sensors to formal records, much of which cannot be so easily collected and freely shared; there are all sorts of technical, ethical and public policy barriers to making such liquid data liquid. 1 It is an impossible task to accurately count the number of patient encounters and transactions because of the current fragmentation of the care delivery system and the abundance of information technology platforms that the industry uses. Some healthcare industry analysts even go so far as to suggest that big data applies only to life and biomedical sciences research, not healthcare delivery. In the latter area, gleaning actionable information from data exchanges in each patient healthcare episode is the focus not volume in petabytes. The three V s In February 2009, President Barack Obama signed the American Recovery and Reinvestment Act, which includes subtitle XIII Health Information Technology for Economic and Clinical Health Act, which sets forth clinical quality reporting requirements and offers financial incentives to physicians for the meaningful use of electronic health records (EHRs). The increasing popularity of EHRs and telemedicine has made it easier to stream highresolution medical images. This streaming has combined with an epidemic of chronic illnesses stemming from increased life expectancy, tobacco use and exposure, lack of exercise, and metabolic risk factors. Together, this data forms the three V s of healthcare big data: 2 exponential volume growth and an increase in variety and velocity. Big data s transformative power The promise and vision of big data in healthcare is regarded as a credible disruptive innovation. Big data can ultimately transform polarizing evidenced-based medicine to achieve patient-centered precision medicine that leads to optimal healthcare outcomes for individuals and entire communities. The transformative vision includes big data as the driver for healthcare business decisions for integrated delivery systems, data-driven policy development in government, and factual value-based purchasing by individuals and employers. The sophistication of healthcare intelligence desired by all stakeholders is key to reforming the U.S. healthcare system. Thus, a fourth V value will be the ultimate product of effective organizational big data strategies. References 1. D. Bollier, The Promise and Peril of Big Data, 2010; 2. IBM, What Is Big Data?, 2011; www-01.ibm.com/software/data/bigdata. with the advances in sequencing technology, manufacturers claim that personalized genome sequencing is now within the price range of other medical assays, which means that patients can expect personal genomic data to become part of their EMRs. Raw whole-genome sequence data for a single person can be tens of terabytes. Even though such enormous raw datasets are not likely to sit in storage for long, the amount of derived genomic information is still daunting. But size is not the main concern. Size is manageable because of medical data s distributed nature, which makes it possible to keep an individual s data locally in an EMR and a picture archiving and communication system. However, translational medicine s vision is to link many disparate datasets, which will push both medicine and research to a new level. The complexity of data interconnections will be so great that knowing where to look, what to combine, and how to extract potentially valuable information will easily become one of big data s grand challenges. Legislative environment Legislation designed to transform an inefficient healthcare delivery system into a 21st century accessible, coordinated, costeffective industry has been at the forefront of U.S. politics for decades. In 1993, when then President Clinton crafted polarizing managed care reform, enrollment in commercial, Medicare, 8 A NOBLIS PUBLICATION

11 Medicaid, and managed care programs more than doubled, while increases in medical expenditures slowed. Since then, additional healthcare-related legislation has provided Medicare beneficiaries access to Part D drug plans, which provide prescription drug coverage to seniors at reduced costs, and the 1996 Health Insurance Portability and Accountability Act has standardized the electronic exchange of healthcare transactions among physicians, hospitals, and their business partners. Testing limits The 2010 Patient Protection and Affordable Care Act (Affordable Care Act) the most sweeping and polarizing healthcare legislation since Medicare was introduced 45 years ago will now pave the way for health system delivery improvements, including standardizing race and ethnicity data collection, adopting new payment models, testing seamless and coordinated care models, conditions, eliminating barriers to care for patients with preexisting conditions, and many other healthcare-delivery innovations and health insurance reforms. And the June 2012 Supreme Court ruling that upheld the central provision of the ACA will be the foundation to implement the vision of a new healthcare landscape that promises to provide accessible, high-quality, cost-effective care to millions of new beneficiaries in public entitlement programs. The resulting data deluge will test the limits of data collection, storage, security, retrieval, analysis, and reporting. It will be critical for stakeholders to manage and use big data in policy development, care delivery, clinical care decision making, and ACO operations. Supporting and classifying data will be the ultimate challenge in the quest to harness exponentially increasing data to transform healthcare. Healthcare consumers will find that information from increased data can help close the health literacy gap. Data availability Although the government has a long history of making biomedical science data available to the public, the Obama Administration s Open Government Initiative has motivated government departments and agencies to make a wider variety of data available at This website has unleashed data to create a secondary market for visionaries, researchers, and entrepreneurs to create new knowledge and applications for many healthcare stakeholders, including consumers who choose to provide open access to their personal health records. Expected trends Many powerful trends are behind the transformation in healthcare. With vast amounts of structured and unstructured data cascading daily into virtual and physical data warehouses, healthcare stakeholders, particularly individuals, will require access to data mediators that analyze and interpret the data for real-time decision making. Agency data sharing Federal agencies that work on related healthcare issues (the Centers for Medicare & Medicaid Services, Agency for Healthcare Research and Quality, and Health Resources and Services Administration, for example) will benefit from data-sharing initiatives that will provide data views that agencies cannot easily acquire on their own. Whether the task is to build consensus on retiring quality measures or adopt perinatal programs and interventions for lowincome pregnant women, data will support data-centric agreements that optimize interagency resources and knowledge. Metadata incorporation Although the promise of big data is exciting, it does not come without obstacles. The healthcare industry has long suffered from data quality issues, and if these issues are not quickly solved, big data will fall far short of its potential as a catalyst for transformational change. ACOs and prospective and retrospective bundled payment models will require higher data quality to achieve success. One fundamental way to improve data quality and reusability is to incorporate a variety of standard metadata and to provide accurate origination and exchange histories of the tagged data. Fortunately this strategy meshes well with ongoing efforts of the U.S. Department of Health and Human Services Office of the National Coordinator for Health Information Technology to establish data standards and formats that support integration, interoperability, and automation. Increase in mobile applications Whether the need is to track out-of-network hospital readmissions or to make informed executive decisions about government investments, rapidly increasing data will spawn a proliferation of mobile applications aimed at facilitating the quick visualization of results. As smartphone and tablet technology becomes widespread, health information services will expand their web presence, and more individuals will be able to conveniently access sites that help them answer specific questions. Healthcare consumers in particular will find that information from increased data can help close the health literacy gap, which ultimately impacts healthcare outcomes and costs. HEALTHCARE DATA TRANSFORMATION 9

12 Fueled by consumer expectations, increasing amounts of patient data are available online, boosting opportunities for patients to interact with healthcare professionals and permitting a community view of integrated private information, such as between nurse practitioners, researchers, and patients with the same disease. Mobile applications will offer an unprecedented range of capabilities that will interconnect various stakeholders, support data collection, and empower every individual by providing access to the knowledge possible only by combining and analyzing data from multiple sources. In this issue Coping with big data presents significant obstacles, but these are far outweighed by the opportunity to add tremendous value to the entire healthcare industry. The authors in this Sigma edition address some of these obstacles in the context of real data-driven healthcare problems that Noblis and its research partners are tackling from data aggregation and interoperability to fraud detection and prevention. Each article describes how the increasing size, complexity, and sources of data are affecting or will affect a particular aspect of healthcare transformation. To impose order on the chaos surrounding the rapidly expanding healthcare landscape, the authors of the next article, Turning Data into Information, propose a healthcare data value chain that serves as a framework for bringing disparate data together in an organized fashion and creating valuable information that can inform decision making at the enterprise level. Authors Jacqueline Paranzino, Peter Mork, and Catherine Veum examine the state of practice for each link in the chain from data collection and annotation to actions taken on the basis of results as well as the obstacles to realizing that link. Big data must provide intelligence while still preserving privacy. The next two articles deal with data management aspects that affect the chain s early links: aggregation and interoperability. In The Art and Science of Data Aggregation, Joseph Nichols explains how even high-quality data can yield poor results if aggregation is not sound. Aggregation must be supported and maintained as a standard with the same care given to the standards that define the underlying data elements. Linking Data from Discovery to Understanding by Elizabeth Avila, Christian Curtis, and Peter Mork continues the idea of unifying data to glean actionable information. True interoperability will demand semantic tools that can help automate understanding distributed across datasets, often where no single defining authority exists. Developments to build the semantic web promise to meet that demand by reducing integration challenges, as well as by enabling new, dynamic forms of interoperability. In Harnessing Big Data in the Fight Against Fraud, William Mahon explores how to bring big data to bear against schemes that individually can siphon more than $150 million from healthcare funding. Mahon describes the scope of fraudulent practices, the current limitations of antifraud efforts, and the potential of big data to detect and prevent fraud. The issue wraps up with In Depth, in which I join Paul Breslin in examining new value-based payment models. In Value-Based Healthcare Purchasing: Will Data Growth Be the Catalyst? we describe the characteristics and requirements of these models, including a greater need for shared data management and data transparency. These models are part of the overall migration to accountable, consumer-centered healthcare delivery in which dollars are awarded according to performance against quality measures. Healthcare is undergoing tremendous change and it is hard to predict exactly what policies, programs, and medicine itself will look like a decade from now, but one thing is certain: data will be an essential pillar in any future healthcare enterprise. Other industries such as energy, defense, aeronautics, space, and transportation also face data and computational complexities, but healthcare data management is uniquely challenged. Data sources are structured and unstructured, originating from and reused by myriad stakeholders across the healthcare continuum. All this data must be synthesized for real-time decision making, information sharing, and health system intelligence, while still preserving an acceptable level of privacy and security. If financially and clinically integrated entities such as ACOs are to thrive under a reformed healthcare system, effective data sharing and information access are central. Already new job titles are emerging in response to these needs. For example, a chief medical information officer a board-certified physician with training in clinical informatics and business has become an essential member of the care-delivery team. This new breed of scientist will be charged with realizing the uncharted depths and true potential of data in healthcare and determining just how much data and computational strength are enough to complete the industry s transformation. John M. Young is a senior principal at Noblis, where his experience includes federal portfolio management and knowledge management strategies. He received an Executive Masters in Leadership from Georgetown University McDonough School of Business. Contact him at john.young@noblis.org. 10 A NOBLIS PUBLICATION

13 Evolving a Data Value Chain Turning Data into Information Jacqueline M. Paranzino, Peter Mork, and Catherine H. Veum Increased data does not automatically yield deeper insights, particularly in a culture where stakeholders do not readily integrate data. A data value chain from collection to decision making can lead to greater information sharing, novel care-delivery and cost-reduction solutions. If the healthcare industry is being shaped by exponential growth in data, as the previous article described, then to make the most of the vast data landscape, analysts and researchers must thoughtfully apply multiple technologies, carefully select key data for specific investigations, and tailor large integrated datasets to support specific queries and analyses. All these actions will flow from a healthcare data value chain, a framework for using raw data to make informed decisions that support a variety of stakeholders and their technologies. The proposed value chain addresses the need to push past the poor coordination and lack of stakeholder alliances that have stymied progress toward furthering care-delivery quality and lowering costs. In this environment, healthcare costs have merely shifted from insurer to patient, insurer to health system, and health system to care provider. For example, providers vie to be part of a payer s network by supplying significant discounts to payers and employers with large population bases. However, healthcare provision does not become more cost effective as Inside Track The current lack of data sharing across healthcare stakeholders, particularly in the competitive private sector, makes it difficult to meet new demands for better quality care at lower cost. In the face of new legislative directions that emphasize care-delivery accountability, stakeholders can no longer afford to ignore valuebased solutions. Data standards, rich metadata, and data quality norms are prerequisites to reliable analytical studies and the confident benchmarking of healthcare quality and improvement in patient safety. By taking a holistic view of stakeholder needs and encouraging collaboration, the data value chain empowers stakeholders to determine if processes are meeting quality of care and cost metrics and to adjust any that are not. the number of patients increases, so when the provider offers discounts to large groups, more of the cost burden shifts to smaller groups. 1 Lack of transparency in cost data perpetuates the cost-shifting pattern. The absence of trust-building stakeholder alliances translates to a lack of interest in data sharing. Regrettably, many healthcare incentives are more about garnering bargaining power than raising the quality of care delivery and lowering costs, and existing healthcare value chains do little to break this pattern. Value chains are supposed to be collaborative partnerships among stakeholders involved in an interconnected delivery of services or products. However, current healthcare incentives have not driven higher quality care and lower costs, and scant data is available to effectively measure a value-to-cost ratio among providers. Both these characteristics contribute to a sluggish motivation to share knowledge. Unfortunately, the lack of information sharing leads to inefficiencies and duplication of services. The silos between the value chain stakeholders and the services provided are unidirectional from provider to the patient. Consequently, the patient does not know enough to request the right services. The concern about healthcare litigation leads to an overconsumption of services and excess inventory. In the face of new legislative directions, stakeholders can no longer afford to ignore value-based solutions. With the emphasis on care-delivery accountability, the growth of telemedicine, and the increasing variety of data sources, the healthcare industry is ripe for implementing a data value chain. Although each stakeholder generates and stores specific data locally, data exchange can lead to integrated datasets that could be the catalyst for new insights, empowering stakeholders to answer novel questions about care delivery. The two case studies we include in our article illustrate progress in meeting the aims of two links in the proposed healthcare data value chain. First steps such as these will HEALTHCARE DATA TRANSFORMATION 11

14 DATA DISCOVERY DATA INTEGRATION DATA EXPLOITATION Collect and Annotate Prepare Organize Integrate Analyze Visualize Make Decisions Create an inventory of data sources and the metadata that describe them. Enable access to sources and set up access-control rules. Identify syntax, structure, and semantics for each data source. Establish a common data representation of the data. Maintain data provenance. Analyze integrated data. Present analytic results to a decision maker as an interactive application that supports exploration and refinement. Determine what actions (if any) to take on the basis of the interpreted results. Figure 1. The healthcare data value chain. The chain provides a framework with which to examine how to bring disparate data together in an organized fashion and create valuable information that can inform decision making at the enterprise level. bring the healthcare industry in line with the potential that existing and maturing data management technologies offer and encourage organizations to seek the advantages of collaboration. Defining a data value chain More than three decades ago, Michael E. Porter introduced the value chain concept, describing it as a series of activities that create and build value. Eventually, these activities culminate in total value, which the organization then delivers. 2 Figure 1 shows a proposed healthcare data value chain. The purpose of the chain is to manage and coordinate data across the entire care and payment continuums from researchers to manufacturers, distributors to healthcare providers, and patients to insurers; form a collaborative partnership and coordinate data collection from various healthcare stakeholders and analyze that data to optimize care delivery and quality of care; streamline healthcare data management activities to enable positive outcomes for all relevant stakeholders; and establish a portfolio management approach to invest in people, process, and technology that maximizes the combined data and informs decisions that provide value and enhance organizations performance. Collect and annotate The first link in the chain involves creating an inventory of available data sources and the metadata that describe the quality of those sources in terms of completeness, validity, consistency, timeliness, and accuracy. This description is a critical part of complying with the Patient Protection and Affordable Care Act (informally, the Affordable Care Act, or ACA). Providers and payers must collect and report numerous datasets, such as claims, eligibility, and third-party payer data, as well as data external to the healthcare industry, such as tax status, demographics, and law enforcement statistics. New legislation and regulations create new data sources and ultimately new demands on how to effectively store and use that data. Table 1 lists a few of the ACA s initiatives related to patient-safety data. Such initiatives will require collecting additional data, including rate of hospital-acquired infections, hospital length of stay, number of deaths during a 30-day period, costs of personal protective equipment, and the frequency of adverse events, such as patient falls and pressure ulcers. Uniting these typically disparate data sources will further the exchange of health information, making it much easier to quickly access meaningful data and thereby improve the quality and efficiency of healthcare delivery. State of practice Overall, the healthcare industry s state of practice is relatively poor in this link. Some efforts are making headway, such as the annual data assets index published by Nucleic Acids Research indexing services which find data sources with patient information, given a few demographics on that patient. These efforts are likely to be the trend for this link, although they involve granular retrieval, not bulk analyses. Two techniques suitable for data collection and annotation are Dublin Core and the Department of Defense Discovery Metadata Specification (DDMS). Dublin Core s metadata vocabulary terms supplement existing methods to describe, search, and index Web-based metadata, such as video, images, and webpages, as well as physical resources such as books and objects. Dublin Core metadata is suitable for annotation tasks from simple resource description, to combining metadata vocabularies to providing interoperability for metadata vocabularies in the linked data cloud and across semantic web implementations. The Dublin Core standard includes two levels. The Simple Dublin Core comprises 15 elements; Qualified Dublin Core includes three additional elements as well as a group of element refinements, or qualifiers, that refine element semantics in ways that can aid resource discovery. 12 A NOBLIS PUBLICATION

15 DDMS is a net-centric enterprise services metadata initiative that builds on the Dublin Core vocabulary. Like the Dublin Core, DDMS focuses both on the process of developing a central taxonomy for metadata and defining a way to discover resources through the use of that taxonomy. Challenges in moving ahead Historically, the healthcare industry has only haphazardly adopted data standards to organize, represent, and encode clinical information. The lack of metadata (or annotation) standards has inhibited insurers and providers from being able to understand or analyze relevant data. Within the healthcare organization, the lack of common data standards has prevented information sharing between commercial clinical laboratories and healthcare facilities, pharmacies and healthcare providers regarding prescriptions, and healthcare organizations and payers for reimbursement. The lack of standards has also prevented the reuse of clinical data to meet the broad range of patient safety and quality reporting requirements. One significant obstacle is the varying data quality at multiple sites. The sidebar Reducing Healthcare-Associated Infections on p. 14 describes how the Yale New Haven Health System Center for Healthcare Solutions, University of Maryland, University of Iowa, and The Joint Commission partnered with the Agency for Healthcare Research and Quality to prepare data to understand the impact that gloving and gowning procedures have on hospital-acquired infections. The lack of standards in data representation adds to the work that healthcare and regulatory organizations must perform to prepare, transmit, and use what amount to custom reports a problem that the federal government has recognized. 3 President Obama s 2011 digital strategy calls for enhancing the government s data.gov website to support real-time data from websites such as Medicare s, which maintains a number of quality performance data repositories. To align with this strategy, federal agencies will need to deliver information using web-based and mobile technologies, establish security and privacy protocols, and adopt standards to make the information readable, open, and structured. The emphasis is on turning unstructured data into structured data associated with valid metadata. Relative to organizations in other industries and in healthcare clinical research, metadata standards within healthcare operations are fairly immature. Healthcare organizations could draw from the technology other industries use to summarize textual corpora (extracting important keywords from each corpus). Adapting this technology to summarize biomedical data resources could greatly reduce the effort to identify relevant data sources during analysis, particularly if keywords align with a controlled vocabulary such as the Systemized Nomenclature of Medicine. Prepare Once data collection and annotation is complete, the next task is to establish access to the data sources by copying them into a shared system, such as a relational database or a distributed file system like the Hadoop Distributed File System (HDFS) or by creating or identifying application programming interfaces (APIs) that enable access. Another task is to determine access-control rules security and privacy restrictions for data use. State of practice With a shared system and appropriate access safeguards, each new user must negotiate data access. A role- or attributebased access control policy makes it easier to manage a larger user group. Using public clouds is an effective way to share biomedical data across a very wide user base because all users are managed in a single location. Table 1. Examples of ACA sections aimed at improving patient safety, which will generate the need to collect additional data. Initiative and Section Initiative Aim Impacted Stakeholders 2702 Payment adjustment for healthcare-acquired conditions 3008 Payment adjustment for conditions acquired in hospitals 3590 Paying hospitals for improving care, including reducing hospital-acquired infections Track and report adverse events in hospitals. Use benchmarks and measures to promote provider responsibility for care delivered and care outcomes. Inpatient hospitals, state Medicaid agencies Inpatient hospitals 3025 Hospital readmissions reduction program 3026 Community-based care transitions program Track and report on adverse events in hospitals. Identify and implement best practices to reform the way care is administered through care coordination and care integration (Medicaid-Medicare enrollees, for example). Inpatient hospitals, Medicare HEALTHCARE DATA TRANSFORMATION 13

16 Reducing Healthcare-Associated Infections Beverly M. Belton and Elaine Forte Health-associated infections (HAIs) are a formidable threat to patient safety. In response to the growing prevalence of these infections, both the Affordable Care Act (ACA) and the 2009 American Recovery and Reinvestment Act (ARRA) include provisions aimed at reducing these infections and stipulate financial penalties for providers treating Medicare or Medicaid patients with high HAI occurrence rates. In addition to requirements for the increased measurement and reporting of such infections, the ARRA includes $50 million in funding to support state-level activities to reduce and prevent HAIs. Recognizing the critical need to combat HAIs, Congress for the past three years has provided annual funding of $34 million to the Agency for Healthcare Research and Quality (AHRQ) to carry out a robust HAI program that supports HAI research and promotes wide-scale adoption of proven methods for HAI prevention. One of these provisions concerns HAI intervention at the bedside, or patient-contact, level. The current standard of care as recommended by the Centers for Disease Control and Prevention is to use precautions such as wearing gloves and a gown for all contact with patients that are known to have a HAI or those infected with or known to carry Methicillin-Resistant Staphylococcus Aureus (MRSA) or Vancomycin-Resistant Enterococci (VRE) two multidrug-resistant organisms that can frequently cause HAIs. However, this practice does not apply to patients with no such definitive diagnosis. Could also wearing gloves and a gown for contact with these no status patients reduce or prevent HAIs? Universal glove and gowning procedure? In 2010, the AHRQ funded a trial to answer that question: Would wearing gloves and gown for contact with all patients reduce the rates at which patients in adult ICUs acquire MRSA or VRE organisms or a HAI? To conduct the trial and evaluate its results, the Yale New Haven Health System Center for Healthcare Solutions partnered with the University of Maryland, The Joint Commission, and the University of Iowa to determine how a universal glove and gowning procedure compares to the current standard of care in reducing the occurrence of MRSA and VRE or in lowering the HAI condition rate. The Joint Commission is the primary accreditation and certification body for healthcare organizations in the United States. The trial, which is scheduled to end September 2012, uses a clusterrandomized design and is only the third such trial of antibiotic-resistant organisms and HAIs conducted in the United States. The universal glove and gowning procedure addresses the question of HAI prevention, which is a key ACA concern. Also, infection control and hospital epidemiology practices are rarely supported by evidence from clinical trials of this design, which means that results have the potential to drive meaningful change in implementing HAI prevention at the bedside. Standardizing data collection The trial requires the rigorous collection and analysis of large datasets. Each ICU is amassing a variety of data to determine the intervention s effectiveness, including collecting nasal and perirectal specimens on all patients. Data types include, among others, clinical laboratory results, mortality, morbidity, adverse events, length of stay, HAI rates, frequency of healthcare worker visits to the patient s room, compliance with the universal use of gloves and gown, compliance with hand washing, and the financial costs of implementing universal glove and gowning. The study team realized that providing statistically significant results would require standard data collection from multiple ICUs as well as scientifically rigorous data integration. The first requirement proved challenging because the 20 ICUs selected for the trial used different collection tools and techniques, and the tools reliability and quality varied considerably. For example, each site used different electronic medical records, administrative and clinical databases, and methods of providing the needed information. Figure A shows the steps the study team took to ensure standardized data collection. The first step was to have all site coordinators begin using OpenClinica ( for electronic data capture and clinical data management. In addition, the team created specialized training that site coordinators relied on to guide them in collecting adverse event data using a variation of the Institute for Healthcare Improvement s Adverse Event Trigger tool ( ICUAdverseEventTriggerTool.aspx). To demonstrate competency in the identification of adverse events, each site coordinator had to complete a post-training assessment. The site coordinators completed the modified Adverse Event Trigger tool training in a relatively short time using a combination of instructor-led and self-study methods. The team also provided training in how to use the Centers for Disease Control and Prevention s National Healthcare Safety Network surveillance tool, which added to collection reliability across sites. Finally, they designed a Microsoft Access database to manage clinical laboratory data and the related administrative data gleaned from different systems at the sites. Standardized Use of Existing Tools Training to Ensure Correct Tool Use Post-Training Test Design of Database to Manage All Site Data Use Openclinica for clinical data entry, capture, and management Track adverse events using modified Adverse Event Tool Have site coordinators complete self-study and instructor-led classroom course Administer graded assessment of coordinators' ability to use the data collection tool Generate computer and data reports using a Microsoft Access database with clinical and administrative data Review paper record or electronic medical records and manually document results Review results with physician for feedback Figure A. Steps to ensure standardized data collection in the AHRQ clinical trial. Standardized tool use, training of coordinators at data collection sites, and assessment of tool use competency are critical to obtaining meaningful results. 14 A NOBLIS PUBLICATION

17 Through site coordinator training, the team is able to obtain highly reliable reports of adverse events across the 20 sites and to compare results even though the sites normally vary in their methods for collecting data and identifying these events. Immediate outcomes The primary outcomes of interest are changes in acquisition rates for MRSA and VRE. The secondary outcomes of interest are changes in HAI rates, hospital length of stay, number of deaths during a 30 day period, costs of personal protective equipment, and the frequency of adverse events, such as patient falls and pressure ulcers. The collected data will provide a toolkit of actionable recommendations on how to implement a novel patient safety intervention in a clinical setting as well as ways to use and manage large amounts of disparate data across diverse sites. The cluster-randomized trial design is known for its ability to avoid bias when allocating interventions to trial sites. Consequently, it supports strong inferences about cause and effect that are not justifiable with other trial designs. Analyses of all study aims will be performed according to the intention-to-treat paradigm at the ICU level and will accommodate the matched-pairs design. Intention-to-treat analyses aim to preserve the randomized groups and address hypotheses about the intervention s clinical utility. As a result, the trial will provide a realistic estimate of the benefit of using a universal glove and gowning procedure and real evidence to support the implementation of specific HAI prevention activities across the continuum of care. Perhaps the most important outcome will be evidence-based answers to key questions about patient safety, healthcare quality, and costs that might eventually eliminate the spread of multidrug-resistant organisms and HAIs. Should this intervention be successful, it is expected to positively impact the current estimated $28 billion to $33 billion spent each year to address HAIs and HAI-related issues. recommendations gleaned from the trial are based on evidence that clinical staff have generated in actual clinical settings during the routine provision of care. Thus, clinicians should be able to easily translate the results, which will be disseminated in a peer-reviewed journal, at conferences, and through poster presentations, webinars, and the networks available to the Yale New Haven Health System Center for Healthcare Solutions and its partners. Remaining challenges Many challenges are associated with the successful design and completion of cluster-randomized trials of antibiotic-resistance and HAIs across multiple sites that provide healthcare. Beyond the technical issues associated with the design of a cluster-randomized trial are the challenges of data collection and translation of the data into meaningful information useful for transforming care at the bedside. This study will provide a blueprint for future studies examining novel ways to prevent the spread of HAIs and address other patient safety issues. It will also provide concrete examples of ways to address the impact of working with large datasets from disparate sources across multiple sites. By developing a common framework to distribute and collect data, healthcare providers can more accurately and rapidly assess their interventions impact on patient safety whether that is taking a defective product out of the care-delivery path as quickly as possible or adopting a universal glove and gowning procedure. Continued progress toward the standardization of data sharing and collecting will help drive the development of common practices to improve care, decrease costs, and save lives. Beverly M. Belton, RN, MSN, is the program manager of Health Reform Projects at the Yale New Haven Health System Center for Healthcare Solutions, where she manages and provides subject matter expertise to a variety of projects and offerings. She received an MS in nursing management, policy, and leadership from Yale University. Contact her at beverly. belton@ynhh.org. Disseminating results The Yale New Haven Health System Center for Healthcare Solutions and its partners will widely disseminate the trial results to help guide implementation of its findings and decision-making at the bedside. The Elaine Forte is a director at the Yale New Haven Health System Center for Healthcare Solutions, where her experience includes designing, developing, implementing, and evaluating information technology solutions, education and training programs, and healthcare transformation projects. She received a BS in biology and medical technology. Contact her at elaine.forte@ynhh.org. HDFS, Big Table, and MongoDB enable the storage of terabytes or more of data regardless of structure. Tools for providing data access include representational state transfer, APIs, Web Services Description Language, and Open Database Connectivity/Java Database Connectivity. Tools for specifying security and privacy policies include the extensible Access Control Markup Language and Kairon. Policy specification allows patients to individually specify and manage their security and privacy preferences, relative to the perceived sensitivity of their information a need identified by the Office of the National Coordinator for Health Information (ONC) to support granular, patient-centric consent. Techniques for ensuring privacy include k-anonymity and l-diversity, which create binned results from raw data. Binned results describe several individuals, making it impossible to discern a particular identity. Languages for access-control policies have been around for decades and role-based access control is well understood. However, although roles are well-specified within an organization, defining these roles across enterprise boundaries is an open challenge. A trust mesh akin to the trust chains used to validate https connections is a possible solution, but unlike the Web, which relies strictly on hierarchical structures, the mesh will require peer-to-peer trust relationships. Challenges in moving ahead Attribute-based access-control policies are less understood, but relevant standards are emerging. Standards for expressing and enforcing privacy policies are lacking. Commercial packages that focus on enforcing privacy policies tend to be tailored to specific environments with a particular set of laws. General- HEALTHCARE DATA TRANSFORMATION 15

18 purpose tools enforcing privacy policies do not exist. ONC is taking the lead in defining languages to specify granular patient-centric consent. These proposals need to move out of the planning stages and into development, facilitating the creation of consent management systems that enable healthcare organizations to verify that they have the patient s consent to share sensitive health information. Organize The data source developer makes deliberate organizational choices about the data s syntax, structure, and semantics and makes that information available either from schemata or a metadata repository. Either mechanism can provide the basis for tracking the shared semantics needed to organize the data before integrating it. State of practice The most common methods for organizing data are the use of a metadata repository and model management. Metadata repositories are commercially available, and numerous generic metamodels exist, many of which rely on Extensible Markup Language Metadata Interchange. However, because this metamodel is underspecified, each tool provides customized extensions, which can lead to vendor locking, problems sharing schemata among participants, and other tool interoperability issues. Stakeholders can take steps to maintain data quality across domains, such as identifying authoritative data sources; building data dictionaries to supplement datasets; and explaining data sources, guidelines, formats, timelines, precision, and completeness. Taking action to enhance data quality will assist an organization with strategic initiatives aimed at managing and using data sources effectively, such as data warehousing and analytics; master data management, in which an organization links all its critical data into one file, thereby streamlining data sharing among personnel and departments; data governance that proactively identifies data quality issues and ensures that all stakeholders have a role in governing data; and enterprise application development. Challenges in moving ahead Analysts often skip formal data organization because they are more focused on their own data needs than on considering how to share data. However, sharing knowledge about internal data organization can streamline analysis across domains, and tools are mature enough to support such sharing. More seamless integration with data providers environments (upstream) and data consumers environments (downstream) would encourage these parties to communicate more through formal data models and less through textual data summaries. Integrate Properly organized data is ready to be combined into a common representation that suits a particular analysis. Each integration effort constitutes mappings that define how the data sources relate to the common representation. A metadata repository should be able to track these mappings to facilitate future analyses. Regardless of the particular representation whether a community website or a formal repository, such as a data warehouse combining disparate data sources delivers new, undiscovered information. Analysts can discover novel relationships between stakeholders, or patterns that can point to abuses, such as fraud. State of practice Integration can be either virtual, such as through a federated model, or physical, such as through a data warehouse. Traditional data federation technologies and emerging semantic web technologies will support the integration and querying of combined data resources. By enabling virtual integration, these technologies allow source databases to remain in place while providing a new query layer, which gives the impression that the user is querying a single data store. Relational databases are suitable for most kinds of tabular data, while the semantic web is more compatible with nontabular, nonnumeric data, defined by a rich set of networked relations. Combining the two technologies will give data analysts a comprehensive toolkit for dataset exploration and for discovering the knowledge within integrated datasets. The Web has created many opportunities to integrate data incidentally. Social networking, for example, has become a powerful data integration tool, creating myriad data sources in a single day. Because stakeholders can add content or comment on existing entries, information becomes more robust. Indeed, uniting communities with a common desire to share information, resources, and expertise has the potential to solve complex system and organizational problems. The sidebar Building a Product Recall Management Community describes how social networking can further the aims of the data integration link in the value chain. Such integration is a flexible way to derive solutions from massive amounts of data. In the example in the sidebar, a social networking portal provides alert content and product-specific information to users and becomes a vehicle for members to 16 A NOBLIS PUBLICATION

19 Building a Product Recall Management Community For the most part, relaying product alert and recall notifications is a manual process that is largely unreliable. Pharmaceutical, medical device, and healthcare product manufacturers and distributors typically rely on mail, , or fax to send pertinent information, and in many cases, either the hospital never receives the notices or the information does not reach the appropriate department. In 2003, Noblis began studying the manual recall management process in a large academic medical center in the Northeast. In one instance, a recall notice went out about a bronchoscope medical device that could not be sterilized effectively. The center s loading dock personnel had received the notice, but because the surgical department had never reviewed it, the defective product remained in the clinical pathway. The result was a threefold increase in patient infections. To address these dangerous inefficiencies, Noblis developed RASMAS, an automated recall management prototype service based on the company s earlier work building knowledge management software and web-based tools to better manage product alerts. Figure A shows how the service works. The recall notices impact approximately 5,750 U.S. hospitals. Because recall data is parsed into relevant areas of focus within a healthcare organization and the record of actions taken on each recalled item is stored within RASMAS, as many as 50 people within that hospital can take action to address the product alert or notice of its recall. Multiplying that 50 by the number of hospitals affected can yield a sizable amount of information on recall management. Healthcare providers that join the RASMAS recall management community can check off recalled items that they removed from hospital and facility shelves, as well as contact other RASMAS members from across the nation to ask about how they have handled a particular recall or read posted advice and comments about a particular alert. Thus, users across the healthcare supply chain warehouse, pharmacy, emergency room, hospital receiving dock, shipping area, or nursing station become part of a nationwide network of 29,000 people exchanging recall management best practices and tips. Figure A. Automated recall management with RASMAS. Noblis staff and recall analysts monitor sources, categorize and standardize alerts and notices, and send them to appropriate users in the RASMAS community. Focused electronic alerts have enabled hospitals to shave 15 days off the typical time to receive and address a product alert or recall notice. discuss recall challenges and develop solutions. Members can take part in educational forums that enhance specific job knowledge and support product recall management. Formal tools that aid integration include data warehouses with extract, transform, and load (ETL) scripts and federated databases with executable mappings. Canonical models and data standards can streamline the process by providing a common vocabulary. However, as the user community grows, maintaining shared semantics becomes increasingly difficult if the scope of the shared vocabulary is not limited. The National Information Exchange Model (NIEM) uses a core/corona approach to manage scope. NIEM Core defines HEALTHCARE DATA TRANSFORMATION 17

20 concepts that are shared across many domains and applications. NIEM then introduces petals that expand the core to include domain-specific concepts. 4 Finally, individual applications add application-specific concepts to the corona a constellation of unaligned concepts. The latter concepts might require integration when independently developed applications introduce similar concepts. Thus, unaligned concepts eventually move into a petal or the core as they become standardized. With this structure, NIEM participants can easily communicate common data elements among themselves, and add unanticipated data elements without breaking their existing information exchanges. Organizations rarely share mappings, but these artifacts are of great value to future analyses when the data formats must be similar. OpenII is an open-source integration tool that aids in identifying and integrating data schemas, and many commercially available tools can help enterprises build and execute ETL scripts or enable runtime transformations. Challenges in moving ahead Although integration tools and techniques are available, they suffer from two major gaps: how to integrate tools with metadata repositories and how to transition decades of schema-matching research into user-friendly tools. The current market offers little incentive for interoperation among metadata repositories. Tool vendors develop integration tools that work only in the context of that vendor s tool suite. Both these gaps are the consequence of market incentives. Tool vendors develop integration tools that work only in the context of that vendor s tool suite; they have little incentive to interoperate with other vendors metadata repositories. Similarly, enterprises cannot easily adapt integration techniques from academia for use in a particular (closed-source) vendor s environment. With increased government calls for data sharing, the market incentives for integration tools should begin to trend toward greater interoperability. Analyze Integrated data sources are ready for analysis, which includes maintaining the provenance between the input and results and maintaining metadata so that another analyst can re-create those results and thereby strengthen their validity. Analysts can project outcomes, costs, disease prevalence, and so on, as well as run what-if scenarios to determine the potential impact of a specific change on an element being measured. For example, to determine if more regular physicianpatient engagement affects how a patient manages diabetes, analysts can marry geographic and socioeconomic data, the prevalence of diabetes in that location and at that socioeconomic level, and the number of preventive or follow-up physician visits. State of practice Popular data analysis techniques such as MapReduce (via Hadoop, MongoDB, and so on) enable the creation of a programming model and associated implementations for processing and generating large datasets. Users specify a map function that processes a key-value pair to generate a set of intermediate key-value pairs, as well as a reduce function that merges all intermediate values associated with the same intermediate key. Frequent itemsets help analysts find interesting correlations and decision trees, both of which can help explain patient behavior, such as the frequency and patterns of patient visits. One study used this method to analyze visits to several specialties of the same healthcare facility in oneday, one-stop visits in Taiwan. 5 A variety of algorithms are available to analyze datasets, most of which assume a tabular data view, which requires amassing the raw data into a suitable format. Challenges in moving ahead At the heart of the value chain, the analyze link is perhaps the most mature in terms of available tools and techniques. Given this crowded marketplace, new offerings can more easily distinguish themselves not only on the basis of incremental analytic power, but also by providing strong integration with the links preceding and following analysis. By making it easier for analysts to access relevant data, for example, tool vendors provide differentiated value. To fulfill this integration goal, the information technology communities responsible for data discovery, information management, and data analytics need to communicate among themselves and their tools need to interoperate. Together these communities can develop an environment that lets analysts more quickly identify and use data appropriate to the task at hand. Visualize Visualization involves presenting analytic results to decision makers as a static report or an interactive application that supports the exploration and refinement of results. The goal is 18 A NOBLIS PUBLICATION