Health Data Accessibility Priorities for the U.S. Health Care System

Transcription

1 Health Data Accessibility Priorities for the U.S. Health Care System Health Data Leadership Summit Presented by: January 2015 Nashville, TN Disclaimer This paper is the output of the Health Data Consortium (HDC) and does not necessarily represent views of all participants or organizations involved.

2 Letter from the Chief Executive Officer Dear Friends and Fellow Data Hippies, The Health Data Consortium (HDC) sits at the intersection of health data, innovation and public policy. HDC is a public-private consortium focused on promoting the accessibility, availability and responsible use of health data and encourages collaboration among health data users and stakeholders to ignite innovation, drive down rising health care costs and improve patient outcomes. We bring together a diverse group of stakeholders including patient advocates, providers, researchers, industry representatives, innovators, and policymakers to advance the national dialogue surrounding the key barriers and opportunities in using health data. Since its formation, HDC has focused on a number of important cultural, technical, and public policy issues concerning health data, including data governance, data accessibility, privacy and security, and consumer engagement. In January 2015, HDC convened its second annual Health Data Leadership Summit. Attendees represented diverse constituencies and interests including: the federal government, non-profit organizations, payers, consultants, foundations, providers, vendors and researchers. The purpose of the Summit was to have a thoughtful dialogue about the health, public policy and technology trends impacting data accessibility, to generate a shared vision of success in data accessibility, and to develop strategies or action plans to overcome some of the obstacles associated with the shared vision of data accessibility. In the coming weeks, HDC will host its annual conference, Health Datapalooza. We welcome you to come and participate in these innovation-led conversations, focused on health data which brings together elite minds from the public and private sectors. This is an exciting time in health care the opportunities are great and the possibilities are endless. HDC remains committed to promoting the accessibility, liquidity and responsible use of health data to ignite disruptive innovation and transform health and health care. Thank you for your continued support, Dr. Christopher Boone, PhD, FACHE Chief Executive Officer Health Data Consortium 2

3 Introduction What does Big Data mean today in health care? In general, Big Data is described in terms of the three V s volume, velocity and variety. 1 When we think about the volume of data that is being generated today, data is often described in petabytes. 2 One petabyte is the equivalent of 1 quadrillion bytes that is enough to store the DNA of the entire population of the U.S. and clone it twice. 3 While much of this generated data comes from such sources as social media and GPS, health data is increasingly becoming a dominant source. 4 Today, many different types of health data are collected besides traditional electronic health record (EHR) information. These include: data from medical devices, clinical trials, clinical registries, public health surveillance data, genomic data and other omics related data. 5 The volume of health data is only expected to grow exponentially in the future. In fact, it is projected that an estimated 50 billion connected devices will be available globally by 2020 approximately six devices per person, many of which will have the ability to collect usable data. 6 Yet despite the significant technological advancements over the last decade to collect massive amounts of health data, the absence of a multi-stakeholder data governance model and associated infrastructure continues to impair our ability to access health data at the right time by the right person in the proper setting. In January 2015, the Health Data Consortium (HDC) convened its second annual Health Data Leadership Summit. Attendees represented diverse constituencies and interests including: the federal government, non-profit organizations, payers, consultants, foundations, providers, vendors, and researchers. The objectives of the meeting were as follows: (1) Develop a shared understanding of the context and current situation in health data accessibility including health, public policy and technology trends; 1 Sarasohn-Kahn, Jane. (2014) Here s Looking at You: How Personal Health Information is Being Tracked and Used, pdf 2 Sarasohn-Kahn, Here s Looking at You 3 Sarasohn-Kahn, Here s Looking at You 4 Sarasohn-Kahm, Here s Looking at You 5 United States. Agency for Healthcare Research and Quality (AHRQ). Data for Individual Health. Washington D.C.: AHRQ, Topol, E. J., Steinhubl, S. R., & Torkamani, A. (2015). Digital Medical Tools and Sensors. JAMA, 313(4), doi: /jama

4 (2) Construct a shared vision of success in data accessibility; and (3) Develop strategies or action plans to overcome identified barriers to the shared vision of success in data accessibility. This white paper will share highlights from the day s discussions. Background In the 2014 report, Data for Individual Health, the current state of health care was characterized as follows: Today, the delivery of health care moves in a linear fashion, proceeding from preventive medicine to diagnosis, to treatment, and ultimately to outcomes. (see figure 1). Clinical research has a well-established learning and discovery cycle between research, treatment and outcome [which] feeds into health care delivery. However, there is typically an open loop between health care outcomes and clinical research, resulting in a failure to effectively link research to treatment. An important step in achieving a learning health system is to close this loop and create a full learning cycle between clinical research and health care delivery. 7 Figure 1: Current relation between health care delivery and clinical research

5 That said, there are a set of activities that run parallel to traditional health care delivery that must be integrated to achieve a learning health system. 9 This includes: community engagement, public health surveillance, wellness programs and mobile health applications. As the Institute of Medicine (IOM) recently noted, our vision is for the development of a continuously learning health system in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation with best practices seamlessly embedded into the care process, patients and families are active participants in all elements, and new knowledge is captured as an integral by-product of the care experience. 10 (see figure 2). Figure 2: Integration of data required for a Learning Health System 11 To achieve this future state, tackling the challenges associated with data accessibility becomes a necessary objective because as the Data for Individual Health report notes, the concept of a learning health system, points to a level of data access, integration and scalability that goes well beyond the interoperability of EHR systems

6 What is Data Accessibility? Data accessibility generally refers to the collection, storage, and use of data as well as the standards associated with all types of data both now and in the future regardless of whether the data ends up in an EHR and regardless of the authorized user. The terms data liquidity, data portability, data flow, and interoperability are often synonymous with data accessibility. Current Initiatives There are a number of different initiatives in development today that are focused on improving data accessibility. 21 st Century Cures Initiative: Last year, the House Energy and Commerce Committee launched a bipartisan effort, the 21 st Century Cures initiative, to accelerate the discovery, development, and delivery of new cures and treatments. An objective of this initiative is to support the development of digital medicine by facilitating data sharing and the use of new technologies. The legislation currently includes language that intends to address interoperability concerns. ONC Interoperability Roadmap: In January 2015, the Office of the National Coordinator for Health IT (ONC) released, Connecting Health and Care for the Nation: A Shared Nationwide Interoperability Roadmap Draft Version 1.0. The Roadmap supports the principles ONC outlined in its 10 year strategic plan. The Roadmap focuses on a number of business and functional requirements to facilitate data accessibility including: rules of engagement and governance, the need for supportive business, clinical and cultural regulatory environments, privacy and security protections for health information, technical certification and testing of health IT products and services, and core technical standards and functions. Precision Medicine Initiative: President Obama s 2016 budget includes a $215 million investment for patient-powered research to accelerate biomedical cures and treatments and would provide clinicians with the tools and capabilities to select treatments that work best for their patients. As part of this initiative, the President has requested funding from Congress for the creation of a voluntary research cohort of one million or more Americans who have 6

7 volunteered to participate in a patient-powered research initiative where participants can contribute different types of data and engage in open and responsible data sharing. The President has also requested funding for ONC to develop, as part of this initiative, interoperability standards and requirements to ensure secure data exchange with patient consent. FHIR & Argonaut Project: Introduced by Health Seven International (HL7), Fast Healthcare Interoperability Resource or FHIR attempts to standardize the exchange of information through a set of modular components called Resources. 13 Each resource contains a human readable summary, a set of entries for agreed upon standard data such as a medical record number, name, or gender and the ability to tie this data to local workflow. 14 The benefit of FHIR as the Data for Individual Health report notes, is that FHIR makes it possible to parse different fields of a resource because the standard is clear about the meanings of the [different] entries. 15 In turn, this enables the extraction of a human readable summary which reports in readable form the patient s name and medical record number so that if a different EHR platform is being used, the patient can still be identified within the system in an interoperable fashion. 16 The reason why FHIR offers so much promise for data accessibility is that as the JASON Report notes,... with some additional work, FHIR could help facilitate the overall health data infrastructure to interoperate beyond just EHRs. 17 HL7, with the support of a number of major EHR vendors and health systems has also launched the Argonaut Project which intends to accelerate the development and adoption of FHIR. Issues Affecting Data Accessibility Attendees at the Leadership Summit were initially asked to describe the current state of data accessibility and identify the health care, public policy and technology trends and issues that impact data accessibility

8 The consensus of the group was that the current state of data accessibility in the health care ecosystem is complex. Depending on the context, health data is often accessible but sometimes not. For example, with respect to consumers, access to health data is limited. For a researcher, the flow of data is often inconsistent. In contrast, claims data is generally accessible to providers. That said, even in situations where the data is accessible, it is shared indiscriminately and providers often receive more data than what they need to care for a patient. For example, as one participant noted, in Federally Qualified Health Centers (FQHCs), there is a high degree of data accessibility. However, the data shared is not meaningful or useful to either the data generator or data acceptor. In other words, clinicians have little use for the data because it is not collected for a clear objective. The costs associated with data accessibility is also a contributing factor to the varying degrees of data flow. Additionally, when data is accessible, it is challenging to get the data into the workflow because it is not compatible with existing workflows. True clinical data is often fragmented, siloed, inaccessible, and of questionable quality and unknown provenance. Interestingly, while claims data is generally accessible, it can be problematic because it does not provide an accurate representation of the clinical setting. At the same time, true clinical data is often fragmented, siloed, inaccessible, and of questionable quality and unknown provenance. Furthermore, there is a current lack of attention to metadata. While in some instances metadata is being produced, it is not used in a productive manner. The result? An incomplete picture of an individual s health. Pervasively inconsistent and inconsistently applied standards also hinder data accessibility. Even if stakeholders have access to clinical data, EHR vendors often input the standards differently, preventing the free flow of data. The Health Insurance Portability and Accountability Act (HIPAA) also encumbers data accessibility and is often used by stakeholders as a pretext for not making data more accessible. 8

9 Health Care Issues Affecting Data Accessibility There are a number of health care issues that affect data accessibility. The current trend towards people-centered health care delivery and the movement away from the traditional hospital setting offers tremendous potential in improving data accessibility. As the trend continues to move towards a people-centered health care delivery model, it will accelerate the flow of data thereby empowering the patient and his or her caregiver, resulting in increased patient engagement. However, there are a number of cultural challenges within health care that impede data accessibility. For instance, organizational culture is a major obstacle because it encourages stakeholders to retain the data instead of sharing the information with those outside of the health system or the provider s practice. Skepticism by providers in sharing data with their own patients is another cultural challenge. Often, providers are unwilling to share the patient s own data with them. Today, 70 percent of doctors will not give their patients their clinical or office notes. One reason for this is need to protect oneself against future malpractice claims. Another is that providers often do not believe a patient will be able understand the results provided to him unless the notes have been contextualized by the provider first. Health literacy is another cultural challenge that hinders data accessibility. So engrained are these cultural challenges, that a number of participants expressed skepticism that even if the technical challenges associated with data accessibility are resolved, it may not be enough to overcome the cultural challenges. Public Policy Issues Impacting Data Accessibility The current trend towards peoplecentered health care delivery and the movement away from the traditional hospital setting offers tremendous potential in improving data accessibility. Several public policy issues impede data accessibility. A predominant concern is the lack of a sustainable business model to encourage data accessibility. Lack of clarity over who should have access to what types of data is another challenge affecting data accessibility. Because of this lack of clarity, questions often arise over who should have the right to access different types of health data. Patient consent to sharing their data with a third party is a concern. Studies have shown that patients are generally comfortable with their data being used by others provided it is not used for commercial purposes. In fact, according to a survey conducted by Consumer Reports National Testing and Research Center and PatientsLikeMe, 94 percent of social media users 9

10 with medical conditions would be willing to share their anonymized health data to improve health care. Confusion over the use and disclosure of identifiable health data for health care operations versus research under HIPAA and the Common Rule is another public policy issue affecting data accessibility. Under HIPAA, uses and disclosures of identifiable health data for health care operations does not require individual consent or authorization to share the data. 18 Alternatively, if obtaining generalizable knowledge is the primary purpose of these activities, it is considered research and therefore individual consent or authorization to share the data is required. 19 The Common Rule applies the same definition for research. These regulations effectively draw an arbitrary line in the sand based on whether data is shared and serves as a deterrent to the free flow of data throughout the health care ecosystem. There are also a number of public policy challenges associated with patient generated data. Not only are stakeholders presently faced with the task of integrating this data into EHRs, but there is the associated challenge of enabling clinicians to access the patient generated data themselves provided they have patient consent. Finally, attendees noted that there is a lack of proper incentives within the federal government to release additional government data sets or open data. Not only is this due to budgetary constraints but also fear of backlash from Congress and the public with the release of more federal data sets. Technology Issues Influencing Data Accessibility Different technology issues and trends also influence data accessibility. One trend that concerns many stakeholders is the lack of standardized data standards because it hinders data liquidity. At the same time, there is a valid concern that should health data become too structured, providers may lose the value of prose in a patient s story. Furthermore, as health data becomes more structured and voluminous, there is a risk that providers may lose the patient s narrative, creating a loss of signal. 18 Health care operations includes, conducting quality assessment and improvement activities including outcome evaluations and development of clinical guidelines, provided that the obtaining of generalizable knowledge is not the primary purpose of any studies resulting from such activities. Operations also includes, population based activities related to improving health or reducing health care costs [and] protocol development. (45 C.F.R ) C.F.R

11 One summit leader noted, should health data become too structured, providers may "lose the value of prose in a patient s story. Another technology trend affecting data accessibility is the current deal activity in health care. A recent PwC analysis reported that in 2014, the pace of deal activity was up 16.3 percent overall from Similar deal activity is expected for 2015 provided there are no substantial changes to the landscape due to legislative or regulatory action. Consequently, as larger health systems continue to merge and be acquired, these larger systems may not be able to handle additional volumes of data particularly as some systems already struggle with managing the current volume of data. Many also view the health care industry as technologically behind compared to other industries (such as the banking industry) when thinking about a robust data architecture. For example, the health care industry still relies on dated technologies such as faxes. That said, the health care industry is increasingly shifting towards a person-centric data architecture. For instance, there has been progress with respect to data accessibility in the wake of the JASON reports notably from a number of EHR vendors who recognize the need to standardize standards and open up their data architecture. However, costly transaction fees by EHR vendors remains a concern. Such pricing can encourage the CFO of a health system to align him or herself with an EHR vendor creating a disincentive to share data outside of its system. Digital health innovation is also taking place within certain areas of the health care industry. By mid-2014, digital health funding approached $2.3 billion, a 170 percent increase over This trend is anticipated to continue with the market for digital health expected to surpass $200 billion by That said, many entrepreneurs still struggle with developing a sustainable business model in digital health. Regulatory uncertainty surrounding mobile health devices, software, and platforms adds an additional layer of complexity for entrepreneurs trying to develop a sustainable business model. Shared Vision of Data Accessibility What would a shared vision of success in data accessibility look like? Despite the complexities of the current state of health data accessibility and the health, public policy, and technology trends affecting it, participants at the Health Data Leadership Summit agreed that a shared vision of success in data accessibility is possible. In general, the shared vision described by the group rested on a number of assumptions including: (1) continued adoption of new technologies by the health care industry, (2) greater regulatory clarity around health data, and (3) properly aligned incentives. 11

12 First, the shared future vision in data accessibility relies on the adoption of new technologies by the health care industry. This includes interoperable EHRs, uniform code sets and metadata models that hinge on generally accepted standardized standards. The future vision also includes EHR platforms that allow stakeholders to have access to open APIs on which innovators can build apps for specific applications and communities. In the shared vision, there would also be a number of ways in which new technologies would play out including cloud computing and storage, use of real world data and real-time sensors (including alerts for providers), and unique patient identifiers. A second assumption of the shared vision depends on greater regulatory clarity. In the future state, clear guidelines would be provided for the sharing and use of health data including standards for privacy and security. Data governance would be coherent and developed by a multi-stakeholder governing body. Clearer regulatory guidance including modernization of HIPAA would occur to reflect the current state of the health ecosystem and bring clarity to questions surrounding data ownership. Additional regulatory reforms would include reduced meaningful use requirements under the HITECH Act, tort reform, improved access to direct-toconsumer tests and results (such as 23andMe) and evaluation and management (E&M) payment reform. Finally, a shared vision of success in data accessibility assumes an environment where incentives are properly aligned. A strategic framework would be developed and implemented that provides clear data accessibility goals for different stakeholders including useful metrics and data segmentation for health information exchange. This framework would provide an opportunity to align incentives and create the capacity to establish a multi-stakeholder, value based, shared gain model within and across health care delivery models. Provided these three assumptions are met, a future health care ecosystem would exist where EHR vendors have platforms that are evaluated on the basis of data accessibility. Additionally, vendors would be required to make data accessible as a requirement of EHR certification under the Meaningful Use program. Larger aggregations of data could take place in different and useful ways and these EHR platforms could enable the delivery of the right data to the right person at the right time. Large aggregations of data could enable the delivery of the right data to the right person at the right time. Further integration of knowledge, individual access to data, improved data flows with patient consent, and greater data accessibility across the care continuum would become the norm in this future state. A cultural change would begin to take place where universal sharing of data is 12

13 the norm rather than the exception. Data accessibility would facilitate improved population health management and stimulate the development of a range of population and public health applications that inform not only population health policy but encourage efficient allocation of evidence-based resources at the state, local, and community level. Additionally, improved data liquidity would drive better health and health care resulting in fewer mistakes, lower costs, and better quality outcomes. Improved data flows would also foster the development of a range of quality and outcome measures and the consolidation of real-time pharmacy data and associated risks. Universal data sharing would encourage the introduction and implementation of specific measures to reduce health disparities. Consumers would be empowered to access their own health data, becoming more engaged with their care delivery resulting in a better consumer experience and improved outcomes. Overall, if the shared vision of data accessibility is successful, it would lead to a learning health system envisioned in the JASON Report and by the IOM that is knowledge-generated, where data can be disseminated and applied in a number of different contexts and result in a better educated, better informed, healthier population. Findings and Recommendations Participants at the summit identified a number of barriers that impede the shared vision of data accessibility. However, the group focused on six key barriers which, if addressed, would have the largest impact on achieving the shared vision and be easy to tackle through collective stakeholder action. The barriers identified by attendees included: (1) lack of standardized standards, (2) excessive compliance and confusion with HIPAA, (3) failure to articulate the value proposition associated with data accessibility, (4) the current fee for service payment model, (5) insufficient quality measures and (6) failure to articulate and prioritize data accessibility objectives and goals. Below are a number of findings and recommendations proposed by attendees that would help eliminate these barriers and achieve the shared vision. Finding: A common set of agreed-upon standards that are standardized are needed to improve data accessibility. Recommendations: A public-private effort should be undertaken for the development, testing, monitoring and adjudication of standardized standards. This effort must include EHR vendor and developer conformance to the standards as well as standard development 13

14 organizations (SDOs) collaboration with the federal government to help publish a list of standards. Establishment of a common data model and message standard. The common data model must be flexible and adaptable to enable innovation and to allow it to evolve as the industry technologically advances. To encourage the use of this common data model and the use of standards, properly aligned incentives must be instituted. Public APIs should also be made available to encourage different systems to interoperate with each other and to encourage entrepreneurs to enter the market and develop new and innovative products that accelerate data accessibility and move us closer to a learning health system. Public APIs should be consistent and standardized to allow loose coupling so that a system can access the data of another system even though the same functions are not performed identically. Privacy and security challenges must also be addressed including a security model that reflects a patient s reasonable expectation of privacy. The model must also include a zero-tolerance policy for data breaches. The challenges associated with patient matching must also be addressed as part of this effort. Because many individuals do not receive health care services in a single health care institution, inaccurate patient matching continues to pose a risk to patient safety. Inaccurate patient matching also raises concerns over inefficiencies in the delivery of care and data integrity and compliance in the handling and use of sensitive patient information. Developing a strategy to address patient matching would improve patient safety and prevent the siloing of health information thereby improving data liquidity. Finding: Excessive compliance and confusion over what HIPAA legally requires inhibits data accessibility and innovation in the marketplace. HIPAA should be modernized or a HIPAA 2.0 a new vision for HIPAA that would help improve data flows throughout the health ecosystem and provide a clear, shared understanding of HIPAA s privacy framework. Recommendations: A public-private sector initiative should be undertaken to provide greater guidance and clarity to HIPAA as it currently exists in law. 14

15 This initiative should explore how HIPAA could be modernized, including more safe harbors and better guidance that is not only applicable to the rapidly evolving environment of health information technology but also understood by a new generation of technologists, lawyers, and compliance teams. Concerns over the interpretation of health care research versus operations under HIPAA and the Common Rule must also be addressed in exploring a modernization of HIPAA. Stakeholders should make certain that regulators have the necessary tools and capabilities to ensure that the law or a modernized version of it is applied properly to the technology in question. This effort should ensure that any guidance or clarity given to HIPAA as it exists in current law, or any modernization of the law itself, does not substantively erode individual privacy rights. Finding: Stakeholders have failed to articulate the value proposition associated with data accessibility. A clear articulation of the value proposition is critical because stakeholders will not otherwise make their data accessible. Data accessibility is often viewed as a public good no single stakeholder can do it on their own and the benefits associated with improving data accessibility do not always come back to those who invested in making their data accessible. Recommendations: Establish a multi-stakeholder work group to develop case studies that demonstrate the value proposition of data accessibility. Whether the shared value proposition demonstrated is valid should be measured by cost, outcomes, and efficiency metrics. Other measures of success should include whether there is a broad return on investment and widespread engagement by stakeholders. Proper oversight over these metrics may require establishment of an organization that actively monitors these metrics. Convene a multi-stakeholder group to establish a shared governance structure that would help immunize against the public goods problem. Constant monitoring of the governance framework by stakeholders or an independent arbiter may help ensure trust across partners engaged in data sharing. Facilitate pilot projects in the hospital and academic health center settings with publicprivate funding that focuses on developing best metrics and implementing 15

16 organizational changes to demonstrate the value proposition associated with data accessibility. Finding: The current fee for service payment model does not provide sufficient incentives to make data accessible. If anything, a payment model based on volume instead of value discourages data accessibility x-rays and lab tests are duplicated instead of results being shared across health systems. In turn, this results in inefficiencies and wasteful health care spending. In contrast, valuebased payment models recognize the importance of data liquidity access to quality information better informs clinical decision-making which means improved outcomes, lower costs, and increased patient satisfaction. Recommendations: Convene a number of stakeholders including federal and state governments, providers, payers, trade associations, vendors, and patient representatives, to develop a curriculum for value-based payment reform. Develop public-private sector funded pilot projects that focus on new, value-based approaches to care delivery and to advance data accessibility. The pilot projects should include scale and spread mechanisms as well as mechanisms to foster the growth of trusted entities. Develop standards for data collection, submission, and sharing to encourage the adoption of value-based payment models. Standards must ensure improved data liquidity, better data quality, and the ability to move actionable data without risk. Transparency in the rules associated with value-based payments should be required as well as the creation of a trusted entity for measurement of value. Finding: Quality measures must be properly aligned. This means fewer but more meaningful measures. Should the challenge of quality measures be properly addressed, it would result in innovation in the marketplace and decisions based on performance including appropriate resource allocation. 16

17 Recommendations: Key stakeholders should develop a strategy to provide multi-stakeholder input into developing quality measures. This strategy must include validation of the quality measures as well as sufficient pilot testing. Improved outcomes must be associated with each measure identified. The revised measures would not require manual data entry unrelated to care and would be applicable to a larger population. Furthermore, in developing the quality measures, a valid method for risk adjustment should be adopted. The multi-stakeholder group should also help develop data on the costs of conditions and identify actionable data that can improve outcomes. Additional research on what factors can improve outcomes should also be performed. Finding: A national strategy should be crafted that identifies and prioritizes data accessibility goals. Should these priorities be identified and prioritized, innovation in the marketplace would accelerate and a vibrant analytics-driven health care system reflected by usable interoperability and quality metrics would be realized. Recommendation: A multi-stakeholder effort should be undertaken to adopt a strategy that identifies and prioritizes data accessibility goals. This strategy must include a clear articulation of incremental goals associated with data accessibility based on the current requirements and anticipated future demands of stakeholders. The strategy must also consider welldefined privacy and content standards. Conclusion and Next Steps Data accessibility is crucial to enabling the health ecosystem to reap the rewards associated with health data. There are a number of trends in health care, public policy and technology that affect the current state of data accessibility including a number of barriers. However, a shared vision of success, envisioned here by a diverse group of stakeholders, is possible. HDC is committed to carrying these actions forward and in bringing together disparate stakeholders from across the health ecosystem to improve health data accessibility and the use of health data in order to lower health care costs, foster innovation, and improve patient outcomes. By convening and disseminating this report, HDC has identified the current state of data accessibility, a shared vision of success in data accessibility, and has offered a number of 17

18 different recommendations to improve data accessibility. In the coming weeks and months, HDC will be operationalizing a number of different workgroups that will focus on these recommendations to advance the shared vision of data accessibility. We encourage other organizations to work with HDC to advance these recommendations and to continue this important dialogue. 18

19 Appendix Participating Organizations Below is a list of the organizations in attendance at the 2015 Health Data Consortium Leadership Summit: Butler Snow California Health Care Foundation (CHCF) Center for Improving Value in Health Care Center for Medicare and Medicaid Services (CMS) Emdeon FTI Consulting Forward Health Group HCA Health Care Cost Institute Healthcare Leadership Council Health Level 7 International Informatics Corporation of America (ICA) Kaiser Permanente of Southern California Manatt, Phelps & Phillips, LLP MEDHOST Nashville Health Care Council National Association for Trusted Exchange NextGen Oak Ridge National Laboratory Office of the National Coordinator for Health IT (ONC) Oscar Insurance Company Robert Wood Johnson Foundation University of Oklahoma, School of Community Medicine U.S. Department of Health and Human Services Vanderbilt University Wellpartner Acknowledgements The Health Data Consortium wishes to thank Butler Snow and the Nashville Health Care Council for their support in helping host the Health Data Leadership Summit. HDC would also like to thank Robert Mittman for moderating the day and Lisa Arora for providing visual representation of the discussion. 19