McDermott Will & Emery LLP Webinar Series Digital Health: The New Dynamics

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1 McDermott Will & Emery LLP Webinar Series Digital Health: The New Dynamics Big Data Part II: Data-Driven Changes to Care Delivery and Payment Models February 10, 2015 Bernadette M. Broccolo McDermott Will & Emery LLP

2 TABLE OF CONTENTS Page I. BIG DATA ARRANGEMENTS: CURRENT CATALYSTS... 1 II. GLOSSARY OF KEY TERMS... 2 III. RECENT BIG DATA COLLABORATIONS... 2 A. American Society of Clinical Oncologists (ASCO) Information/Evidence-Based Medicine, Clinical Decision Support, Personalized Medicine... 2 B. NYU Langone Medical Center (NYULMC), Cleveland Clinic and University HealthSystem Consortium (UHSC) Automated Data Intake Program... 2 C. Dignity Health and Optum Insights Collaboration Optum360 Revenue Cycle Management Joint Venture... 3 D. Mayo Clinic and UnitedHealth Group Create Optum Labs Data Aggregation and Analytics to Support Research... 3 E. Illinois Hospital Association (IHA) and Missouri Hospital Association (MHA) Data Analytics to Produce Actionable Data for Trade Association Members... 4 F. Blue Cross Blue Shield of Massachusetts (BCBSMA) Business Intelligence Tools for Predictive Health Care Data Analytics... 4 G. Mayo Clinic Collaboration with IBM Information/Evidence-Based Medicine, Translational Research and Personalized Medicine... 4 H. IBM (Watson) Information/Evidence-Based Medicine... 5 I. Research Data Sharing Networks and Personalized Medicine... 6 IV. ELECTRONIC HEALTH RECORDS (EHR)... 7 A. EHR Implementation... 7 B. Leveraging EHRs... 8 V. HEALTH INFORMATION EXCHANGES... 8 A. General... 8 B. Background... 9 VI. HIT INFRASTRUCTURE TO SUPPORT ACCOUNTABLE CARE... 9 A. Fundamental Tenets of ACO HIT Infrastructure and Data Strategy Development... 9 B. Collection, Extraction and Normalization of Data C. Analytics... 10

3 VII. DATA NETWORKS TO SUPPORT RESEARCH AND PERSONALIZED MEDICINE A. Research B. Personalized Medicine VIII. CLOUD COMPUTING A. Five Essential Characteristics B. Three Service Models C. Four Models of Accessibility D. Applications of Cloud Technology IX. KEY PRIVACY AND SECURITY COMPLIANCE CONSIDERATIONS A. HITECH Act and Final Rule B. Other Relevant HIPAA Considerations C. Other Federal Privacy Laws and State Laws Protecting the Confidentiality of Sensitive Health Information X. EU and Other Foreign Data Protection Laws A. EU Data Privacy Directive B. Laws of Non-EU Countries XI. ANTITRUST XII. A FTC/DOJ Guidance on Exchange of Information among Competitors in Health Industry B FTC/DOJ Policy Statement on MSSP ACOs OWNERSHIP OF NETWORKS/EXCHANGES, REPOSITORIES AND THEIR CONTENTS A. Generally B. Current Law C. Practical Planning Implications The law in this regard remains somewhat unsettled XIII. WHITE HOUSE REPORTS ON BIG DATA A. PCAST Report B. White House Report XIV. PLANNING, DUE DILIGENCE AND CONTRACTING CONSIDERATIONS AND STRATEGIES A. Business Associate Agreement Considerations B. Special HIT Vendor Contract Considerations

4 C. Special Considerations in Cloud Computing Agreements D. Special Considerations in Collaborations for EHR Networks and HIEs E. Special Considerations in ACOs (a/k/a Clinically Integrated Networks ) F. Special Considerations in Repository Collaboration Contracting G. Key General Planning Considerations XV. DATA GOVERNANCE A. Background B. Emphasis on Data Governance in Recent Big Data Initiatives C. Scope and Approach XVI. RESOURCES AND REFERENCES A. General Data Strategy Resources B. Accountable Care Generally C. Health Information Exchanges D. Data Governance ATTACHMENT A GLOSSARY OF KEY TERMS

5 I. BIG DATA ARRANGEMENTS: CURRENT CATALYSTS Health industry stakeholders of every shape and size are scrambling to develop and implement big data capabilities. While the underlying strategic business needs vary, the ultimate goal is the same to develop and/or gain access to an electronic infrastructure that will support the robust collection, storage, exchange, aggregation and analysis of all types of clinical, claims and other financial and operational data. 1 Arrangements using robust and reliable electronic health information warehouses, registries, and networks will be essential to accommodate accelerating public and private sector demands for care coordination, quality and outcomes measurement and reporting, comparative effectiveness research and evidence-based medicine that will reduce cost, improve quality, enhance patient experience and improve population health. For academic medical centers, universities and research institutes complex data aggregation and exchange strategies will also be essential to qualify for future federal research funding directed to translational research and personalized medicine. Clinical research support organizations are rapidly realizing how such health information technology (HIT) resources can diversify and enhance the scope and quality of their services. Pharmaceutical and device manufacturers need them now and in the future to support expanded regulatory requirements for mandated post-market surveillance, inclusion in product approval applications submitted to the FDA of a risk evaluation and mitigation strategies (REMS) for ensuring that the benefits of the drug or biologics outweigh the risks, and to adapt product reimbursement and development strategies to respond to the Comparative Effectiveness Research (CER) and to the personalized medicine movements. 2 Governmental agencies such as the FDA will need massive electronic data repositories that are built, in part, using Electronic Health Records (EHRs) and Health Information Exchanges (HIEs). The design of and participants in the electronic HIT infrastructure for complex health data sharing arrangements will vary, as will the pathways followed in the development process. There is no one size fits all solution. Some but probably very few will be able to go it alone. New and different players and collaborations among them are emerging on the scene at a furious pace, but few of them have yet to be fully pressure tested for success and sustainability. No solution or approach will be free of legal or business risk. This paper (1) addresses the who, why, what, when, where and how of the current quest for big data exchange, capture, aggregation, integration and analytics to meet key strategic and operational needs, such as improvements in care delivery quality and cost effectiveness, population health management, accountable care networks, revenue cycle management, and enhanced biomedical research and discovery; 1 For a detailed discussion of these various catalysts, see Broccolo, Bernadette M. and Divarco, Sandra, Health IT: An Essential Ingredient in the New Health Reform Recipe, BNA s Health Law Reporter, 19 HLR, 995 (July 15, 2010). 2 In particular, Section 905 of the Federal Food, Drug and Cosmetic Act Amendments (FDAAA) requires the FDA to develop methods to obtain access to different data sources (including, public, private and academic entities, many of which are likely to be hospitals, health system and some of which will be HIEs) and validated methods to link and analyze safety data of at least 25 million patients by 2010 and 100 million patients by July FDAAA 905(a), adding 505(k) to the, amending 21 U.S.C These methods would then be used to establish procedures for a post-market risk identification and analysis system in the near future. 1

6 and (2) provides practical insights for identifying and managing the complex web of business and legal risks presented by evolving data collaboration models, including early phase due diligence of potential data partners and IT, cloud and data analytics/infrastructure support vendors, risk management and allocation of liability in contracts with partners and vendors, and ongoing monitoring and oversight of the arrangements. II. GLOSSARY OF KEY TERMS Attachment A provides a selective glossary of key terms relating to the development and implementation of complex health information data exchange relationships. III. RECENT BIG DATA COLLABORATIONS Essential components or phases of big data initiatives include (a) establishing connectivity to facilitate the exchange and collection of data; (b) extracting, normalizing and validating data; and (c) both retrospective, comparative data analyses and predictive data analysis. EHRs and HIEs are two of the primary technological solutions being used to establish and proliferate connectivity of health care data. As the illustrated by the following representative data collaborations, the most significant progress to date appears to be on that first phase and the least on the analytics phase. 3 Completion of all three phases will be essential to move from big data and data for data sake to actionable data rather than just data for data sake. A. American Society of Clinical Oncologists (ASCO) Information/Evidence-Based Medicine, Clinical Decision Support, Personalized Medicine 1. ASCO launched a project involving the collection of hundreds of thousands of cancer patients that will generate comparative information for use by physicians in guiding treatment of patients across the health system nationwide. 2. The underlying technology infrastructure includes ASCO-developed software that can accept clinical information from almost any electronic health record. As with other initiatives, the premise is that clinical data can accelerate clinical decision-making and research because it encompasses vastly more patients than hands-on clinical trials. 3. The system will accommodate data entry by patients, clinicians and researchers and use realtime queries for clinical decision-making which offers the search ease of Google. 4. As of March 2013, ASCO predicted a 12 to 18 month timeframe for implementation of the data analytics capabilities. 4 B. NYU Langone Medical Center (NYULMC), Cleveland Clinic and University HealthSystem Consortium (UHSC) Automated Data Intake Program 1. NYULMC, Cleveland Clinic and UHC (118 AMCs and nearly 300 affiliated hospitals) are collaborating under the Automated Data Intake Program in which NYULMC and Cleveland Clinic will transfer data to UHC for data integration and analysis. 3 See, Ruoff, Alex, Big Data Promise of Electronic Health Records Remains Unrealized, Avalere Says, Bloomberg BNA Health Care Daily Report (February 19, 2014). 4 Ron Winslow, Big Data for Cancer Care, The Wall Street Journal (March 26, 2013), available at 2

7 2. The goals of the program are to eliminate the manual transfer of data and afford more timely submission and receipt of clinically relevant, actionable data and access to more accurate patient outcome benchmarks to further enhance the quality and efficiency of care delivery. 5 C. Dignity Health and Optum Insights Collaboration Optum360 Revenue Cycle Management Joint Venture 1. Dignity Health (37 hospitals in 3 states and $9.4 billion in annual patient revenue) and Optum Insights (subsidiary and consulting and analytics arm of UnitedHealth Group) created a joint venture company known as Optum360. Optum Insights is majority owner. Optum360 will employ 3000 employees drawn from the venture partner. Optum360 will provide the full spectrum of revenue cycle management services, including registration, insurance preauthorization, clinical documentation/coding for complete and compliant medical records, and collections from payers and patients. 2. Dignity brings equipment and revenue cycle management function IP. Dignity is the first customer under a 10 year contract ($250 million a year). Optum Insights brings complementary technology. 6 D. Mayo Clinic and UnitedHealth Group Create Optum Labs Data Aggregation and Analytics to Support Research 1. UnitedHealth Group and Mayo Clinic created a joint venture research initiative designed to draw on millions of health-insurance claims (claims records for more than 109 million people over 19 years) and in-depth clinical patient records (5.0 million clinical records) to focus on research into best outcomes for patients at lower costs (e.g., analyzing how to improve the diagnosis of hepatitis C and assessing the relative cost-effectiveness of certain medical devices). 2. The venture is built on the increasingly prevalent view that clinical care and claims data are complementary. Electronic medical records offer an in-depth picture of a patient, with details such as test results and family history that are lacking in claims records. Claims data includes information about care a patient receives from another health system, but lack the granularity of the clinical record. 3. With regard to use of database findings in clinical trials, some see database findings as only complementary to clinical trials" that more narrowly focus on the effects of particular interventions The data will be purged of information that would identify individual patients. 5 Joseph Conn, UHC, NYU Langone, Cleveland Clinic collaborate on data transfer, Modern Healthcare (September 9, 2013), available at 6 Melanie Evans, Dignity, Optum form revenue cycle management company, Modern Healthcare (October 14, 2103), available at 7 Veronique L. Roger, director of the Mayo Clinic Center for the Science of Health Care Delivery. Anne Wilde Mathews, Researchers Mine Data from Clinic, Big Insurer, The Wall Street Journal (January 15, 2013), available at 3

8 5. The Optum Labs facility and its servers that house the health data are located in the academic and research center of Cambridge, Mass, even though both UnitedHealth and Optum are located in Minnesota. 6. The Optum Labs data, analytics tools and expertise will be available to other entities such as health systems and drug companies that will work with Optum Labs on specific projects, ideally bringing their own data pools to use. E. Illinois Hospital Association (IHA) and Missouri Hospital Association (MHA) Data Analytics to Produce Actionable Data for Trade Association Members IHA and MHA, two health industry trade associations representing more than 370 hospitals and health systems, are collaborating to help their hospital and health system members to accelerate the transformation of health care delivery by providing data analytics needed to produce data that goes beyond administrative claim and fee-forservice data to improve continuity-of-care data and patient outcomes. 8 F. Blue Cross Blue Shield of Massachusetts (BCBSMA) Business Intelligence Tools for Predictive Health Care Data Analytics 1. BCBSMA, which provides health coverage for nearly 3.0 million members across Massachusetts and works with over 20,000 participating HMO physicians and 77 HMO acute care hospitals, has embedded into its business processes a combination of business intelligence and business analytics that accelerate the analysis of medical and claims data to produce reports on clinical and financial risk (e.g., medical loss ratios by disease category and provider), operational efficiency and help to identify opportunities for strategic and competitive advantage (e.g., through identification of trends and prediction of future developments). The solution uses a single, centralized data warehouse Benefits include: (a) enabling medical directors to identify high-risk disease groups and take action to minimize risk and improve patient outcomes, (b) responding to high risk groups by setting up programs to engage patients in disease management and improve patient outcomes, (c) accelerating the ability to service large clients effectively increasing the speed of creating complex health informatics reports by 300%, and (d) increase availability of useful information to more business users. G. Mayo Clinic Collaboration with IBM Information/Evidence-Based Medicine, Translational Research and Personalized Medicine Collaboration to build an integrated clinical genomics information infrastructure containing demographic, diagnostic, physiological and genomic data. 1. Early adoption of EHR and the open architecture enables Mayo to easily deploy new analytical and clinical decision-support tools. 8 Press Release: New IHA/MHA Partnership to Accelerate Health Care Transformation through Data Analytics, available at 9 IBM Case Studies: BCBS Massachusetts breaks information barriers (April 15, 2011), available at 01.ibm.com/software/success/cssdb.nsf/CS/STRD-8FWKSQ?OpenDocument&Site=default&cty=en_us 4

9 2. Relative contributions: (a) Mayo s early deployment of EHR, its base of 4.4 million electronic patient records and other data sources such as lab results and billing/claims data, and its long tradition of innovation; and (b) IBM s world-class technology and integration skills and its strong process level expertise. 3. Together, they created a user-friendly data virtualization engine and query tool that enables users to build either menu-based or natural language queries through which they can specify parameters like symptoms, diagnostic codes or test result ranges to generate results in real time Key Benefits include: (a) identifying potential study recruits in seconds rather than months and thereby filling studies more quickly and shortening the study lifecycle; (b) real-time access that enables rapid correlation of integrated clinical, genomic and proteomic data that in turn enables clinicians to prescribe more targeted, effective treatments (i.e., personalized medicine) 11 and researchers to correlate genetic data with treatment effectiveness; and (c) seamless links with very large external sources of genomic and proteomic data (e.g., the National Cancer Institute). 5. Challenges include: (a) pulling together a massive volume of records and data that exist in different formats and are dispersed across the enterprise into a format usable by researchers and clinicians, (b) establishing a security infrastructure with granular level of authentication of a broad number of users, (c) establishing the links to external data sources, (d) deployment beyond the Rochester, Minnesota campus, and (e) building a system that has sufficient flexibility to adapt to new data inputs and specialized analytical and clinical decision-support tools as they become available. H. IBM (Watson) Information/Evidence-Based Medicine IBM s Watson is a cognitive technology that processes information more like a human than a computer by understanding natural language, generating hypotheses based on evidence and learning as it goes. Physicians can use Watson to assist in diagnosing and treating patients by having it analyze large amounts of unstructured text and develop hypotheses based on that analysis. See Various leading providers and payors have collaborated with IBM to apply the Watson technology to their clinical care and business needs. 1. Memorial Sloan Kettering Cancer Center trained Watson to synthesize vast amount of data, such as physicians notes and reports, lab results and clinical research data, to help physicians identify treatment options for cancer patients. See 2. MD Anderson Cancer Center uses Watson s cognitive computing power to help clinicians uncover insights from its patient and research information. See 03.ibm.com/innovation/us/watson/pdf/MD_Anderson_Case_Study.pdf 10 Mayo Clinic Takes a Giant Step Toward Information-Based Medicine, available at: ftp://ftp.software.ibm.com/software/solutions/pdfs/mayofinal10-18.pdf 11 Broccolo, Bernadette M., Gottlieb, Daniel F., Ortman, Randall J., Bauer, Andreas, Chapter 9: Personalized Medicine, 2012 Health Law and Compliance Update, John Steiner, Editor, Wolters Kluwer Law & Business (2012). 5

10 3. Cleveland Clinic is collaborating with IBM in IBM s development of a cognitive computing tool designed to help physicians and medical students make more informed and accurate decisions faster and to cull new insights from electronic health records. See work&ct=usbrb301&cn=s1healthcare 4. Wellpoint, Inc., an Indianapolis-based health benefits company with affiliated health plans serving more than 3.3 million members, trained Watson with 18,000 historical cases and now uses hypothesis generation and evidence-based learning to generate confidence-scored recommendations that help nurses make decisions about utilization management. See I. Research Data Sharing Networks and Personalized Medicine 1. The National Cancer Biomedical Informatics Grid (cabig ) Cancer Research cabig is the national cancer biomedical informatics grid initiative launched by the National Institutes of Health (NIH) in March 2008 to establish an information network enabling all constituencies in the cancer community researchers, physicians, and patients to share data and knowledge so as to accelerates the discovery of new approaches for the detection, diagnosis, treatment, and prevention of cancer, ultimately improving patient outcomes. The specific goals of cabig are to connect scientists and practitioners through a shareable and interoperable infrastructure; develop standard rules and a common language to more easily share information; and build or adapt tools for collecting, analyzing, integrating, and disseminating information associated with cancer research and care. 2. Patient-Centered Clinical Research Network (PCORnet) PCORnet, formed by the Patient-Centered Outcome Research Institute (PCORI) (which was formed under the Affordable Care Act), is being developed by PCORI as a national network that will conduct clinical outcomes research and serve as a resource for storing and gathering realtime data collected by researchers and clinicians at various health-care organizations. PCORnet intends to share data with 11 clinical data networks, health care systems that have agreed to share EHR data with researchers, and 18 patient-powered networks that are operated and governed by groups of consumers willing to share their own health information, so as to streamline the initiation of clinical studies and expand the wealth of information available to researchers Health Data Exploration Project - Use of Data from Personal Devices and Consumer Medical Devices This is a network of researchers, scientists and health-care organizations that collects and stores personal health data captured through wearable devices, smartphone applications and social media. It is funded by a Robert Wood Johnson Foundation grant and is being spearheaded by University of California (San Diego and Irvine). The project is driven by the Foundation s high hopes that personal health data will provide a unique window onto [sic] the many factors that influence health on a daily basis and its ultimate goal of... addressing the barriers to using these 12 Ruoff, Alex, Clinical Researchers Exploring New Sources of Patient Health Data, Bloomburg BNA Health IT Law & Industry Report, Vol. 6, No. 23 (June 9, 2014) 6

11 new forms of data in research, [and] put us on a path to a better understanding of health and how we can build a national culture of health Merck-Regenstrief Institute Big Data Partnership Academic-Industry Collaboration to Support Personalized Medicine Formed in 2012 by Merck and Regenstrief (an informatics and health care research organization affiliated with the Indiana University School of medicine), this partnership is leveraging electronic medical records through the development of an infrastructure to integrate various medical record systems to support a range of research studies that use clinical data to inform personalized health care. The partnership has funded 50 projects to date that rely on data from more than 11 million patients in a HIE called the Indiana Network for Patient Care. Industry commentators have observed that such partnerships between industry and academia, and between and among other payers, are essential as neither sector alone can undertake such projects: Working together, governments, health plans, academic delivery systems, electronic medical record vendors, and private sector companies have the potential to analyze data to improve care and enhance the sophistication of this research. Over time, these collaborations can extend beyond clinical practice information to include genomic, behavioral, and environmental data. 14 IV. ELECTRONIC HEALTH RECORDS (EHR) The drive to improve the efficiency and effectiveness of the health care system has resulted in the creation of the electronic medical environment, with health care providers accessing computer systems to record the most relevant and timely facts about a patient's health during an office visit. A. EHR Implementation While the the design of and participants in the HIT infrastructure underlying a complex, big data data sharing arrangement will vary, and EHR infrastructure and data alone will not be sufficient to meet the needs such arrangements are formed to address, EHRs will be a foundational component of, and often a catalyst prompting creation of, any such arrangement. Therefore, EHR implementation is essential and should focus on: 1. achieving the Three stages of Meaningful Use 2. enterprise-wide systems (i.e., institutional provider Inpatient and Ambulatory Systems) 3. donation/roll-out of EHR Technology to Physicians/Clinics 4. Portals for physicians, employers, payors, patients, etc. 5. Integration of Disparate provider EHR systems for clinical care 13 Id. 14 Baumann, Jeannie, Authors Say Academic-Industry Partnerships In Big Data Can Answer Important Questions, Boomberg BNA Health IT Law & Industry Report: News Archive, 6 HITR 19. 7

12 B. Leveraging EHRs 1. EHRs are becoming increasingly more than mere electronic copies of paper medical records. Clinical data housed in EHRs can be organized, shuffled, sorted, catalogued, coded and shared in interactive ways that are impossible with paper records The evolution of an EHR from its primary use to a key component of a data aggregation and analytics infrastructure often begins with a single provider s conversion from paper medical records to an EHR system, and continues with that provider s collaboration with other providers (directly or through public or private, regional or state-wide HIEs) to achieve meaningful use of interoperable EHR systems, its implementation of a clinical trial management system and integration of that system with the EHR system, and ultimately, collaboration between and among the provider and other stakeholders such as universities, payors, manufacturers, research institutes or research support organizations, and governmental entities for the creation and use of a robust, multi-disciplinary electronic information repository. 16 V. HEALTH INFORMATION EXCHANGES A. General While the implementation of HIEs generally has been slower than expected, the advent of Accountable Care Organizations (ACOs) is expected to strengthen the business case for HIEs as a mechanism for exchanging and in some cases aggregating the data needed to coordinate care across the continuum and achieve provider accountability. 17 ACOs and others will depend upon the ability to capture and analyze data from varied and disparate sources across the care continuum as well as from public and private payors, registries, etc. This will require an operational HIE to transfer real-time data. An HIE capable of bidirectional exchange provides the ability to gather information and to make decisions based on current and up-to-date patient data, rather than on historical or outdated data which can cause inaccurate diagnoses or treatments See, e.g., Peter Jaret, Mining Electronic Records for Revealing Health Data, New York Times (January 14, 2013), which discusses the use of electronic records to create databases for various secondary use purposes. 16 The 2007 HHS report on Personalized Medicine emphasized the collaboration across all stakeholders in both the public and private sectors as being at the heart of the project. U.S. Dep t of Health & Human Servs., Personalized Health Care: Opportunities, Pathways, Resources. The 2008 report includes informative descriptions of case studies of collaboration initiations that have emerged since the first report. U.S. Dep t of Health & Human Servs., Personalized Health Care: Pioneers, Partnerships, Progress. See also Moffitt, Merck join forces: One of the world's largest drug makers forms a research venture with the Tampa institute to adapt cancer treatments to individuals, ST. PETERSBURG TIMES (Dec. 19, 2006), available at Tampabay/Moffitt Merck_join_f.shtml. 17 Designing the Health IT Backbone for ACOs, PricewaterhouseCoopers Health Research Institute, available at (2010 PWC Report). 18 Vo, Nam D., Reforming Health Care through Technology: The intersection of Accountable Care Organizations and Health Information Technology, Oracle, available at (Oracle Report) 8

13 B. Background 1. The Health Information Technology for Economic and Clinical Health Act (HITECH) allocated $300 million to support regional or sub-national efforts toward establishing and maintaining HIEs, whether government-initiated or privately-initiated. The HITECH Act specifically outlines how the federal stimulus money will be used to advance the design, development and operation of a nationwide HIE infrastructure that promotes the electronic use and exchange of information. 2. HIEs are intended to enable hospitals, physicians and clinicians to exchange the information needed to improve the quality and efficiency of patient care through the electronic sharing of patient records HIEs are typically comprised of multi-stakeholder organizations responsible for motivating and causing integration and secure exchange of patient information for treatment purposes. The geographic footprint of existing HIEs range from a local community to a larger multistate region. Interoperability among these various systems is essential in moving toward the ultimate goal of a national health information network. 4. The number of live HIEs more than doubled to 228 between the beginning of 2010 and mid- 2011, with many systems incorporating cloud-based technologies (discussed further below) HIEs can be both public/government controlled/initiated and privately controlled/initiated. Privately controlled HIEs can be closed to a network within a particular locality or go beyond a closed network to include a broad range of network and non-network providers in a locality or a region. 6. Whether to build v. buy HIE infrastructure capability will be a key decision. VI. HIT INFRASTRUCTURE TO SUPPORT ACCOUNTABLE CARE A. Fundamental Tenets of ACO HIT Infrastructure and Data Strategy Development The following are widely accepted tenets underlying any ACO HIT infrastructure and data strategy: 1. an accountable care organization s success will hinge, at least in part, on its ability to share patient data at the point of care and rely on historical and longitudinal data for use in managing population health ; 2. healthcare providers must prepare to explore a variety of options for designing the health information technology backbone for ACOs ; 3. the data necessary to manage a patient s whole health isn t widely available today [rather] [i]t s locked in a myriad of different provider and payer databases or paper records [and] ACOs will need to unlock and use that data ; Live public HIEs increased from 37 in early-2010 to 67 in mid-2011, and private HIEs grew from 52 to 161 during the same period. 9

14 4. [m]ost providers and medical settings currently lack the system infrastructure to support an ACO ; 5. [t]he transformation of the care delivery system necessitates integrated clinical, financial, administrative and research data from across the provider enterprise, as well as analytic capability not inherent in many EHR systems ; and 6. [t]he necessary HIT infrastructure includes the ability to create a longitudinal care record for patients that can seamlessly integrate data from multiple sources as well as the capacity to exchange that data between providers while maintaining its accuracy and integrity. 22 B. Collection, Extraction and Normalization of Data ACOs need to supplement the EHR data component of their HIT infrastructure with: 1. structured and standardized patient data that originated in other systems such as laboratory and radiology systems; 2. the ability to structure and capture data from clinician documentation created in care units, emergency departments, operating rooms, etc. using non-standardized tools such as e-forms and templates, which contains data not now housed in core hospital systems (such as admission, discharge and transfer systems) that utilize structured data; 3. the ability to capture and analyze data from varied and disparate sources across the care continuum as well as from public and private payors, registries, etc.; 4. information analytic capabilities that will enable the ACO providers to analyze data from a variety of sources and to turn the information into a comprehensive strategy to treat or manage a patient s condition; and 5. the ability to measure and report quality on individual providers and on groups of providers involved in coordinated. 23 C. Analytics The use of analytics in addition to data collection and exchange helps capture the integrated view of the clinical, financial, administrative and research elements that are all needed to measure accountability, performance and quality A comprehensive analytic solution would extract data from multiple sources, consolidate, integrate and validate it within an enterprise data model. 24 The enterprise wide data model would include analytics for clinical care, patient engagement, operational efficiency, financial management, and executive strategic management such as the following: 1. Clinical Care Analytics at the point of care to provide care management and decision support and to prevent adverse events and readmissions 21 See 2010 PWC Report See Oracle Report. 23 Id. 24 Id. 10

15 2. Patient Engagement Through patient portals, personal health records, telehealth and remote monitoring 3. Operational Efficiency Analytics used for human resources, staff scheduling, supply chain and operating room optimization 4. Performance measurement and improvement Alignment of hospital-provider and payerprovider health plan quality goals through financial incentives and new value-based reimbursement models of an ACO 5. Executive Strategic Management used to help administer the ACO, prepare budgets and assess the performance of each of the providers within the ACO model Population Health Management the ultimate goal and newest frontier VII. DATA NETWORKS TO SUPPORT RESEARCH AND PERSONALIZED MEDICINE A. Research Medical researchers have clearly identified data sharing as one of their top priorities, 26 as they recognize that we are only at the very beginning of trying to get a sharing of data among disparate users, that the the data itself are crude and they re not always what we want but it s the very beginning of saying that scientists need to be able to access the large and vast amounts of data that is out there. 27 B. Personalized Medicine In simple, personalized medicine refers to medical practices that use genetic tests and family history information to develop preventive, diagnostic, and therapeutic interventions that are tailored to individuals on the basis of their specific genetic code. The goal of personalized medicine is to improve health outcomes and quality of life. HHS has issued two extensive reports on personalized medicine in the last several years, in which it expresses its view of personalized medicine as a goal of health reform and a catalyst for a relationship shift between and among, providers, patients, drug manufacturers, drug development regulators and researchers. 28 Together, HHS two reports emphasize four essential building blocks of the personalized health care movement: (a) the development of gene-based knowledge involving an understanding of human biology at the molecular and genetic levels; (b) a transformation of health information technology that will provide data standardization and the infrastructure for robust, interoperable electronic information databases and networks that will facilitate the sharing of vast amounts of 25 Id. 26 Supra, note Id. 28 Personalized Health Care: Opportunities, Pathways, Resources, available at (last visited June 18, 2010); Personalized Health Care: Pioneers, Partnerships, Progress, available at (last visited June 18, 2010). See also, HHS Secretary Releases Second Report From Initiative on Personalized Healthcare, Life Sciences Law & Industry Report, 2 LSLR 1004, BNA (November 2008); Michael O. Leavitt and Raju Kucherlapati, The Great Promise of Personalized Medicine, boston.com at (December 26, 2008). 11

16 clinical care and research information about medical history, genetic variability and patient preferences between and among all industry sectors and the patient; (c) collaboration across the private and public sectors and across many disciplines and stakeholders; and (d) public trust. VIII. CLOUD COMPUTING Cloud computing is increasingly becoming integral to any complex data sharing strategy. By its very nature, it involves one or more third party support organizations. Particularly with regard to data center/server capacity and disaster recovery plans, a prime vendor often has relationships with various subcontractors, and such subcontractors from time to time are located off shore. A. Five Essential Characteristics Cloud computing is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the internet. Regardless of service or deployment model, there are five essential characteristics of cloud computing: on-demand self-service offerings that enable users to access cloud-based services at their convenience, without having to interact directly with the service provider; 2. broad network access, effectively allowing users to access cloud-based services from any internet-enabled device; 3. availability of pooled resources so that multiple consumers can be served, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand ; flexibility with respect to system capabilities, which may be rapidly and elastically provisioned to quickly scale out, and rapidly released to scale in ; 31 and 5. control and optimization of resources by leveraging a metering capability at some level of abstraction appropriate to the type of service. 32 B. Three Service Models While some overlap exists among the three principal cloud computing service models, each model possesses distinguishing characteristics relating to the services offered and varying levels of control of the parties over the technology involved. 1. Software as a Service (SaaS) SaaS currently occupies, and is expected to continue to occupy a majority of the global public cloud market. 33 With the SaaS model, consumers access the cloud provider s software applications from various client devices through a thin client interface such as a Id. 31 Id. 32 Id

17 web browser. 34 While the computing, processing and storage capabilities of the application are perceived by consumers to exist in the cloud, these processes actually take place in the cloud provider s data center. The user does not manage or control the underlying cloud infrastructure. 35 Examples include Google s Gmail and Google Docs. 2. Platform as a Service (PaaS) The PaaS model allows consumers to use the cloud network to create, deliver and deploy software applications on the cloud infrastructure. As with the SaaS model, PaaS users do not control the underlying cloud infrastructure. However, in contrast with the SaaS model, PaaS users maintain a significant level of control over the applications that are deployed into the cloud. While SaaS applications are routinely used by even the most novice consumers, PaaS services generally appeal to individuals with technical expertise. The PaaS consumer-base is likely to include application developers, administrators, testers and deployers. 36 Once a PaaS consumer deploys an application into the cloud, that application will be perceived by endusers and rightly so as a SaaS application. Thus, PaaS services can function as a step in the process of developing and deploying SaaS applications. Examples include Google Apps, Force.com. 3. Infrastructure as a Service (IaaS) The IaaS model provides consumers with access to additional computing resources, such as processing, storage and other fundamental computing resources on an as-needed basis. 37 In simplest terms, IaaS services function as an alternative to purchasing new hardware. The consumer does not have control over the underlying cloud infrastructure, but maintains control over operating systems, deployed applications, storage and some network features, such as firewalls. Examples include Rackspace, IBM, and Amazon Web Services. C. Four Models of Accessibility Generally, there are four deployment models: (i) Private Clouds; (ii) Community Clouds; (iii) Public Clouds; and (iv) Hybrid Clouds. In large part, the appropriate deployment model depends on the relationship between the service provider and the end-user and among the endusers. The deployment model selected affects which users may access the cloud, who manages the cloud and where the cloud is located. 38 D. Applications of Cloud Technology 1. HIEs and ACOs HIEs are likely to use Cloud Technology and service providers as a component of their information exchange infrastructure Id. 36 Id. 37 Id. 38 Id. 13

18 2. Clinical Decision Support Cloud computing is also being used to assist with clinical decision support. For example, it is being used to facilitate consultation concerning radiation oncology images among independent medical practitioners specialized in a variety of different aspects of breast cancer treatment. 39 Clinicians working in the cloud can perform radiation treatment planning by contouring of images generated by various programs. 40 Processors in the cloud system convert the augmented pictures into a format that can be read by a linear accelerator, which tells the irradiation device where and how much radiation to deliver to a patient. 41 A woman at one location can receive a mammogram, and those images and physician annotations are made available to the other locations via the cloud. 42 Timely access to results correlates with the timeliness of informed treatment, thereby improving the quality of patient care Research Cloud-based applications are being developed to assist clinicians and researchers with computation-intensive projects. 44 For example, researchers at the Johns Hopkins Bloomberg School of Public Health use an internally developed open-source cloud computing pipeline called Myrna for calculating gene expression in large RNA-sequencing datasets. 45 Without the cloud, an analysis for a single RNA sequence on one laptop could take up to three weeks to complete on a local computer network 46 compared to the two hours needed when using the computational capabilities of the cloud Personal Health Records a. Cloud technology is also used to deploy PHRs by allowing patients and providers to access and update information across multiple locations. An early example, Microsoft HealthVault, 48 maintained personal accounts on the cloud that allowed users to access their records via the internet. 49 Consumers are purportedly in control of their personal health information, with functionality to manage access rights among various users, including health care providers. 50 Consumers can input their own data, or health Id. 41 Id. 42 Id Id. 45 Id. 46 Id. 47 Id Google previously launched a similar product, Google Health. However, perhaps indicative of the challenges inherent in convincing consumers that the cloud is safe for storing sensitive health information, Google Health was closed to new customers effective January 1, 2012 and will be retired at the end of the year. Responding to this development, Microsoft has released functionality to allow Google Health users to transfer their health information to Microsoft HealthVault Id. 14

19 information can be imported from connected doctors, hospitals and retail pharmacies. 51 HealthVault accounts are accessible from mobile devices and, when accessed from mobile platforms, users automatically see their information in a layout specificallydesigned for quick access during a health encounter. 52 Other vendors have since entered the market with comparable offerings of PHRs and internet-enabled kiosks with capabilities to collect and monitor health data and track health statistics. Such consumerfacing PHRs allow patients to collect comprehensive data from multiple organizations, 53 and because the data is managed exclusively by the patient, it can be utilized by any health care organization where the patient receives treatment provided the health care provider is granted appropriate access. 54 b. PHRs may also be health care organization-specific (rather than consumer-facing) and offer unique features that seek to increase patient involvement in the management of care. These services can also exist in the cloud and be accessed via the internet, but they do not offer patients the ability to incorporate outside health encounter information. 55 For example, Kaiser Permanente s PHR product, My Health Manager, allows patients to access their medical records and test results over the internet, 56 their doctors, refill prescriptions and schedule, review or cancel appointments online. 57 Beginning in 2009, over three millions Kaiser members were able to access the service, and during the same year, 6,854,722 prescriptions were refilled, 1,852,178 appointments were requested and over 8.5 million s were sent using My Health Manager. 58 A recent Kaiser study observed 35,423 patients with diabetes, hypertension, or both. 59 In any two-month period, patient use of secured patient-physician messaging through Kaiser s My Health Manager was associated with statistically significant improvements in various health care effectiveness measurements. 60 IX. KEY PRIVACY AND SECURITY COMPLIANCE CONSIDERATIONS A. HITECH Act and Final Rule Both the HITECH Act and the Patient Protection and Affordable Care Act (ACA) incentivize investment in HIT infrastructure that will support widespread electronic exchange and analysis of healthcare information. Recognizing that this health reform policy also elevates the privacy and security risks regulated by the Health Insurance Portability and Accountability Act of 1993 and accompanying regulations 61 (HIPAA), however, the HITECH Act strengthened existing HIPAA privacy and security requirements in several significant respects. In particular, the HITECH Act extended the applicability of the HIPAA security standards and penalties for security and privacy 51 Id Id. 57 Id. 58 Kaiser Permanente HealthConnect Electronic Health Record Frequently Asked Questions, 59 Use of Health Information Technology Leads to Improved Care Quality, News Center Press Releases: National (July 7, 2010), 60 Id C.F.R. 160, 162 and

20 violations directly to business associates; established rigorous data security breach notification requirements; extended the accounting for disclosures requirement to treatment, payment and healthcare operations; imposed an express prohibition on the sale of data other than in limited circumstances; and significantly modified the categories of HIPAA violations, the range of civil money penalty amounts and the available defenses to a HIPAA action. On January 25, 2013, the Office for Civil Rights (OCR) of the U.S. Department of Health and Human Services (HHS) published a final rule (Final Rule) 62 containing modifications to the privacy standards (Privacy Rule), security standards (Security Rule), interim final security breach notification standards (Breach Notification Rule) and enforcement regulations (Enforcement Rule) under HIPAA and the HITECH Act. The final modifications include both changes required by the HITECH Act and other changes deemed appropriate by OCR in order to strengthen the privacy and security of health information. 63 The Final Rule contains several provisions that will affect complex data sharing arrangements involving protected health information (PHI): 1. The definition of business associate is expanded to include a subcontractor of a business associate so that subcontractors of a business associate are also liable for violations of the Privacy Rule and Security Rule. 2. The preamble to the Final Rule and the Final Rule s modification of the definition of Business Associate together make clear that entities that maintain PHI on behalf of a covered entity, such as data storage vendors and cloud service vendors, are business associates. 62 See McDermott Will & Emery White Paper, OCR Issues Final Modifications to the HIPAA Privacy, Security, Breach Notification and Enforcement Rules to Implement the HITECH Act, for a detailed overview of and commentary on the provisions of the modifications made by the Final Rule to the HIPAA privacy, security, breach notification and enforcement Rules to Implement the HITECH Act, and a comparison of the provisions of the Final Rule to the requirements and standards in effect prior to its promulgation. available at 63 On February 17, 2009, Congress adopted the HITECH Act, which requires certain modifications to those rules and imposes new requirements for notification of breaches of unsecured PHI. See McDermott Will & Emery White Paper, entitled Economic Stimulus Package: Policy Implications of the Financial Incentives to Promote Health IT and New Privacy and Security Protections, available at regarding the HITECH Act. OCR published the Breach Notification Rule on August 24, 2009 to implement the breach notification requirements effective September 23, See McDermott Will & Emery White Paper, entitled, Regulatory Update: HITECH's HHS and FTC Security Breach Requirements, available at regarding the Breach Notification Rule. In addition, to conform the Enforcement Rule to the HITECH Act s stepped up enforcement provisions, OCR published an interim final enforcement rule on October 30, 2009 (Interim Enforcement Rule). See McDermott Will & Emery On the Subject publication entitled, HHS Issues Interim Final Rule Conforming HIPAA Civil Money Penalties to HITECH Act Requirements, available at b-e95d-4f19-819a-f0bb5170ab6d. On July 14, 2010, OCR published a notice of proposed rule making to implement most of the HITECH Act s privacy, security and enforcement provisions that were not already implemented through the Breach Notification Rule and the Interim Enforcement Rule and to make other changes that OCR deemed appropriate. On May 31, 2011, OCR published a notice of proposed rule making to implement the HITECH Act s accounting of disclosures requirement. See McDermott Will & Emery White Paper entitled, OCR Issues Proposed Modifications to HIPAA Privacy and Security Rules to Implement HITECH Act, available at regarding the proposed modifications to the Privacy Rule s accounting of disclosures standard. 16

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