IT Roundtable October 23-24, 2014 Meeting Summary V

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1 IT Roundtable October 23-24, 2014 Meeting Summary V

2 Contents Background... 1 Need for Clinical Data in Clinical Research Information Technology Roundtable White Paper IT Roundtable Meeting... 1 Structure of this White Paper... 2 Governance Issues... 2 Data Transparency... 2 Data Sharing... 3 Privacy and Access... 3 Data Quality... 4 Gaps in Clinical Research IT Governance... 4 Recruitment Using EHRs... 4 Top Issues... 4 Other Important Issues... 5 Clinical Research Registries... 6 Registry-Related Concepts... 7 Highest-Impact and Most Actionable Concepts... 8 Examples of Best Practices... 8 Reuse of Data and Data Sharing... 9 Concepts Pertaining to the Reuse and Sharing of Clinical Data... 9 Next Steps Top Priorities Other Potential Action Steps Appendix A: Participants ii

3 Use of Clinical Data in Clinical Research Background Need for Clinical Data in Clinical Research Clinical information technology (IT), including electronic health records (EHRs), plays an increasingly important role in supporting clinical research as clinical research protocols become more complex, involving more and more interventions and study arms. Clinical IT is particularly critical for teams that support growing numbers of research protocols, including those that are multicenter. IT technical capacity in EHRs is rapidly progressing, and the latest EHRs provide unprecedented opportunities to support medical discovery. The next steps are to: Develop standards and manage interfaces to link data from EHRs and other resources Use EHRs to engage patients in research Ensure that the decisions of academic health center (AHC) leaders about EHR functionality are driven by both clinical and research needs 2013 Information Technology Roundtable White Paper In 2013, the Information Technology (IT) Roundtable of the Clinical Research Forum sponsored a conference, EHRs in Support of Clinical Research, in Washington, DC, to develop recommendations on using EHRs for clinical research. IT Roundtable leaders summarized the recommendations from this conference in a white paper, The Use of Electronic Health Records for Clinical Research: Recommendations to Advance the Field. As a follow-up to the 2013 meeting and white paper, the IT Roundtable held a second conference on October 23 24, 2014, in Washington, DC. This conference focused more deeply and narrowly on three key topics identified at the 2013 gathering: use of clinical data for study recruitment, registries to support clinical research, and reuse of clinical data for research IT Roundtable Meeting The 2014 meeting included 95 participants with expertise in clinical research, health IT, and bioinformatics representing 42 academic health centers (AHC). Other attendees were 13 representatives of electronic health record (EHR) and clinical trial management system (CTMS) vendors, 6 representatives of non profit groups, 1 government official and 1 pharmaceutical company representative. Appendix A contains a list of participants. The meeting began with an overview of challenges in using clinical data for clinical research and a presentation on the requirements of IT governance for clinical research. Participants then joined one of three breakout sessions: Recruitment 1

4 Registries Reuse of data Each breakout session met three times so that all attendees could discuss all three topics. During these sessions, the members of each group used stickers to indicate the three concepts within the breakout session theme with the greatest importance or impact and the three that were most actionable. Appendices B, C, and D contain summaries of the discussions of each breakout session, including the tallies of votes for impact and actionability of each concept. At plenary sessions that followed the breakout group sessions the breakout session facilitators summarized all the three discussions of their theme. All participants then jointly identified the most important and actionable concepts among of the many concepts identified during the conference. Structure of this White Paper This white paper begins with an overview of governance issues presented at the meeting. The paper then summarizes the concepts discussed, organized by breakout session theme. The final section describes the most important and actionable concepts and a plan to address these concepts. The appendix provides a participants list. Detailed summaries of the three breakout sessions are still undergoing review and edits. Governance Issues Dr. Robert M. Califf, Chairman of the Clinical Research Forum and Vice Chancellor of Clinical and Translational Research and Director of the Duke Translational Medicine Institute at Duke University Medical Center, gave a presentation on clinical IT governance, an overarching theme in the use of clinical IT for clinical research. Using specific examples from ongoing projects, his major point was that we are entering an age in which access to data is increasing exponentially, but our human systems of governance and analysis are evolving slowly, but common principles are developing. Data Transparency A major governance issue now is the transparency of human studies across the spectrum, but with a focus on clinical trials. Sponsors of clinical trials are required to post their data from applicable clinical trials on Clinicaltrials.gov. The Food and Drug Administration (FDA) has access to much of the data generated by industry, but industry owns these data. However, the European Medicines Agency is developing policies to widen access to individual patient data from clinical trials that lead to new indications or products. Many large pharmaceutical companies are also making their clinical trial data available to qualified users. Furthermore, an industry clinical trials enclave in development will allow qualified investigators to analyze industry clinical trials data behind a firewall using modern analytic tools. This structure will need a governance mechanism to oversee the access of qualified investigators to this treasure trove of data. Policies soon to be announced by NIH will ensure transparency of federally funded trials 2

5 data. The landscape thus includes the full spectrum of funded research with governance policies requiring transparency from government and industry funded trials. Data Sharing The NIH Health Care Systems Research Collaboratory is sharing clinical data across integrated health systems to answer pragmatic questions about the most effective clinical practices. Its goal is to improve the way clinical trials are conducted through a new infrastructure for collaborative research. The Collaboratory supports several high-impact pragmatic clinical trial demonstration projects that are addressing questions of major public health importance. Each project requires at least two health systems to share EHR data. These studies are much less costly than standard trials and, if they succeed, will provide a transformative way to answer critical questions. A major success of the Collaboratory is the sharing of information across competing health systems. Privacy and Access The National Patient-Centered Outcomes Research Clinical Network (PCORnet) is a large, highly representative, national clinical research network, bringing together 29 large individual networks into a coordinated whole, designed to improve the efficiency and timeliness of research. The networks will be conducting multicenter comparative effectiveness research using a combination of claims data, EHR data and patient reported outcomes, curated into a common data model.. There is promise that among PCORNet, the Collaboratory, the FDA s Sentinel project and the ongoing CTSA effort, a national data resource that will be open to all qualified investigators with common, standardized data elements. But the governance structure for this resource needs to be determined. For example, regulations need to protect patient privacy without preventing investigators from using this resource. The Google Baseline Study is designed to establish a basic understanding of a healthy physiology by collecting data highly intensive data, including genetic and genomic information, continuous physiological information from wearable devices and patient reported outcomes, from thousands of healthy volunteers. The data will be stored in a cloud based system and after the enrolling sites have had early access for analysis, the database will be available to the global community of qualified researchers. Governance systems will be critical because the information will be identifiable so that it must remain behind a highly regulated firewall.. Such resources raise the question of how to manage public access to these types of data. Advances in clinical research based on EHRs and claims data derived from traditional medical encounters are highly valuable. However, people spend very small proportions of their time in a health-care setting, and enterprise data warehouses need a great deal of data on other factors that affect people s health and well-being. The integration of traditional medical data with environmental, social and geospatial data is another area in rapid development, but in much need of tenable governance structures. For example combining medical data with consumer preference data mapped to a person s physical location is no longer limited by technology, but these data are highly sensitive and could be used for good or evil purposes. 3

6 Data Quality Data need to be curated to be useful for research. Clinical data repositories need intensive data janitor cleaning to provide data that researchers can trust for answering questions critical to health and medical care. Gaps in Clinical Research IT Governance The CR Forum s recent survey of clinical research IT governance issues found that most AHCs do not have a chief research information officer and most believe that clinical research IT does not receive adequate priority compared to clinical care. Furthermore, most institutions have not invested in adequate resources to analyze data in the institution s data warehouse for research. The rudimentary systems of clinical research data governance is a bigger barrier than technology to advancing discoveries to the clinic and from the clinic to public health. Reasons for the lack of clinical research governance include concerns by leaders of health system networks that Patients might not want clinical researchers to have automatic access to patient data The costs of Health Insurance Portability and Accountability (HIPAA) breaches and threats to their business reputation of such breaches Patient data showing that certain processes of care are not of high quality An important role of governance in research IT is to create oversight structures that balance the clinical needs of the health delivery organizations with the mission to create new knowledge. Given the overwhelming demands on health systems, this balance can be lost easily. Effective governance systems can provide the appropriate forum for policies that protect the vital role of clinical research as an essential element their institutions mission and benefits patients and the institution. A request from a health system to patients to allow their data to be used in research could promote patient trust in the system. Recruitment Using EHRs Top Issues The most high-impact and actionable concepts pertaining to recruitment of clinical research participants using EHRs are described below. Informed Consent Issues pertaining to reuse of EHR data for clinical research include how to obtain institutional or universal consent and the need for systems that collect data on patient eligibility for or enrollment in clinical studies. Universal consent means that investigators may contact any patient of a health system about participating in a clinical study without going through the patient s physician. Whether this approach is as effective as a request from a treating physician is not known. This type of issue needs to be tested empirically. 4

7 Health systems might not be legally required to obtain consent before contacting patients about research participation, but obtaining consent is often a best practice. What is the best way to obtain this consent? Action Step: Create a library of informed consent forms and practices at AHCs Metrics Different recruitment approaches are needed for different populations in different environments. In one setting, for example, point-of-care alerts might be effective, whereas contacting potentially eligible patients who come to the clinic might be the best method in another setting. Metrics are needed to assess the success rates of different recruitment approaches using EHRs, including speed of accrual and acceptance rates. This type of research on research will enhance the understanding of current recruitment approaches to make these approaches evidence driven and improve them in the future. Governance Questions about governance with respect to recruitment include: What are the enabling factors of participant recruitment using EHRs? Which structures can be used to choose the right tools to use and when to apply them? How can the use of decision support for recruitment be balanced in the context of many other decision support activities? How can resource allocation (to decide what to use when) be centrally managed? How should priorities be set to determine which studies will receive more resources and attention for recruitment through EHRs? Governance is particularly needed to harmonize the increased numbers of clinical and research user alerts and decision support tools. Models are needed to track the stage of maturity of each AHC in recruiting clinical research participants using research IT. These models would be similar to the HIMMSS Analytics maturity models for EHR adoption and use, which determine the extent to which institutions have adopted a given tool. Different maturity models could focus on different aspects of the research enterprise, such as clinical IT and research IT. Maturity models would not focus on levels of innovation but, rather, on how well institutions support their research. Action Steps: Assess the maturity stages of AHCs. Share AHC governance models so that guidelines can be developed. Other Important Issues Exchanging Information between Systems 5

8 Other data systems in addition to EHRs have information that is critical for clinical research. Standard ways are needed to exchange information between these systems for assessing patient eligibility for clinical studies. Participant Engagement Systems and technologies are needed to increase engagement with stakeholders, including clinicians and patients. Such approaches give stakeholders control over the information they receive and their level of involvement in research. Eligibility Criteria Eligibility criteria must not only be scientifically valid but also computable, in the sense that they include data points available in information systems. Such systems make it possible to match patients to studies more precisely. Targeted Recruitment Strategies Targeting recruitment strategies can improve experiences and outcomes. Before developing eligibility criteria, investigators need to conduct a feasibility analysis to determine whether the right patients are available and can be retained. They also need to ensure that recruitment and retention are possible. The order in which studies are offered is important because patients are likely to accept whichever study is offered first. Some physicians develop alert fatigue and stop clicking on alerts, whereas others continue to review alerts. Recruitment approaches must therefore be tailored to each physician. Ideally, tools for personalizing recruitment strategies are managed centrally. Action Step: Create a recruitment strategy toolbox that includes resources, policies, procedures, tools, and technologies. Electronic Portals One recruitment tool is an institutional or national website with information on clinical research participation opportunities. Social media offer another way for patients to find studies and approach researchers. However, one size does not fit all; some patients do not want to choose a study to participate in without guidance from a physician. Clinical Research Registries The Agency for Healthcare Research and Quality defines patient registries as: organized systems that collect data for scientific, clinical, or policy purposes. Registries are a valuable complement to randomized controlled trials in determining realworld outcomes in the practice of medicine. They do not generally have restrictive 6

9 inclusion or exclusion criteria, nor do they specify what therapy the health care provider must adhere to. They can be used to evaluate outcomes for diverse purposes ranging from the natural history of a disease, to the safety of drugs or devices, to the real-world effectiveness of therapies. 1 For the purposes of this discussion, a research registry is an organized system that uses observational study methods to collect uniform data. Research registries are used to evaluate specified outcomes for populations with a particular disease, condition, or exposure. Registry-Related Concepts Concepts related to the use of clinical data in research registries are summarized below. Technology Concepts Link registries to multimedia, omics, and other rich data sources that cannot be stored in text-based registries Enrich/enable registries through natural language processing Adopt and implement the Retrieve Form for Data Capture standard for research data collection in the EHR (to help separate EHR and registry data) Achieve computable and understandable (electronic) consent Use data extraction to initiate registries Meta-Registries Create searchable, public-facing registries of clinical studies (and registries) Create master participant registries with information on whether each patient has opted in or out of being contacted about research participation and a contact management system Governance, Policy, and Operations Systematize the creation and operations of clinical and research registries at the local and national levels (e.g., through standard operating procedures and policies for creating registries) Build an institutional commitment to data governance and data stewardship Build registries from reference-quality data (from data warehouses) Parts of the registry might be governed by HIPAA regulations and parts by institutional review board (IRB) rules. How to blend data that fall under different HIIPAA or informed consent rules is not clear. As technologies become more mature, the need for governance becomes more obvious. New tools are only useful if appropriate governance structures are in place. Use of Registries to Enhance Engagement Create registries with provider and participant feedback 1 Gliklich RE, Dreyer NA, eds. Registries for Evaluating Patient Outcomes: A User's Guide. Prepared by Outcome DEcIDE Center. AHRQ Publication No. 07-EHC Rockville, MD: Agency for Healthcare Research and Quality,

10 Provide transparency and added value in registries (clinical and research) to generate institutional support Create engagement opportunities for research participants through technology Personnel Create multidisciplinary data management teams whose members understand EHRs, metadata, and clinical issues to build registries Highest-Impact and Most Actionable Concepts The registry-related concepts with the highest impact that are most actionable are: Systematize the creation and operations of clinical and research registries at the local and national level Build an institutional commitment to data governance and data stewardship Other concepts with slightly lower levels of impact and actionability are: Achieve computable and understandable (electronic) consent Create master participant registries and contact management systems Examples of Best Practices Many research teams that are developing registries share certain desires regarding functionality and data. They also have unique needs, some of which are specific to a specialty or disease. Ideally, the institution s data stewardship and governance process ensures that users begin to develop a registry by identifying the common data elements they need because such registries require minimal customization. Registry kits can begin populating new registries with demographic, laboratory, medications, and other data from the institution s data warehouse. All data undergo normalization before they are entered into the warehouse. At the same time, legacy registries can be refreshed with new software and a revised architecture so that these registries can inherit the warehouse s governance. Data from research registries can be aggregated in de-identified form, analyzed, and disseminated to providers and patients. Registry data can also be used to create diagnostic algorithms that help physicians identify rare diseases. If the appropriate governance structure is in place, the EHR can be modified to capture certain data elements needed for research during routine care. REDCap can then be used to collect research-specific data elements, map them to a common data model, and provide them to researchers to integrate into their registries using an interface developed by the IT department. Institutions should create offices that can help investigators who have no database experience and plan to join a national or international registry. Such an office could provide one-stop shopping. 8

11 Action Step: Review efforts by others, such as the Agency for Healthcare Research and Quality (AHRQ), whose monograph 2 contains several case studies. Reuse of Data and Data Sharing Concepts Pertaining to the Reuse and Sharing of Clinical Data Resources to Support Data Reuse for Research Health system leaders need to understand the advantages of subsidizing the reuse of data for research. Potential cases that can be made to health system leaders include the following: Return on investment Influence of high-quality research on the institution s ranking among research institutions with National Institutes of Health Funding or best colleges listed by U.S. News & World Report Potential to generate large research grants Ability to reduce the amount of money lost by clinical trials due to inefficiencies Standardization Most health systems have data that are useful for operational decisions and other data that are necessary for research. Standardizing all of these data is probably too ambitious. Ideally, institutions should provide investigators with minimal sets of clinical or other data in a standard format to reuse in their research. These minimal datasets might be in line with common data models such as PCORNet or the Clinical and Translational Science Awards. This approach would prevent the need for customized data pulls. The use of operational data has some limitations. Major vendors have different strategies to address the use of such data for research. Convincing a health system s leader to invest in making EHRs more useful for research can be difficult. However, when the EHR provides linkages that help a researcher produce an outcome that is important for the institution, the CEO will arrange for staff to help the investigator improve his or her data to support clinical efforts. If an institution invests in developing a standardized system for clinical research IT, investigators must use that model and infrastructure. Researchers sometimes do not know what a federated or centralized model is, and the model must be described in ways that researchers can understand. High-Value Use Cases 2 Gliklich RE, Dreyer NA, eds. Registries for Evaluating Patient Outcomes: A User's Guide. Prepared by Outcome DEcIDE Center. AHRQ Publication No. 07-EHC Rockville, MD: Agency for Healthcare Research and Quality,

12 Self-service tools, such as Informatics for Integrating Biology and the Bedside (i2b2), can be used to identify a study cohort for inclusion in a grant application. A slightly more complex use case is identifying patients to recruit to a clinical trial. Large observational datasets could be used for observational studies, such as comparative effective research, but these datasets require highquality data. Different use cases require different levels of data quality. If a use case simply involves screening patients for a trial and these patients are to be screened further at a later time, the data quality might not need to be high. However, if the data will be used in a regression, a single outlier could lead to incorrect answers. A taxonomy is needed of use cases and the data quality required for each one. Metrics are also needed to measure data quality. Adoption of a National Data Model There is no shortage of data models. Meaningful use standards have pushed investigators to adopt standards that they might not have otherwise. The National Library of Medicine has a library of common data elements that could be used to standardize the ways in which data are collected for clinical research. Best Practices for Secure Management and Use of Research Data Virtual desktop environments, including REDCap, are useful for managing data securely. Investigators need to be made more aware of the need for secure data management and the prevention of data privacy breaches. Qualified Personnel Finding people with the appropriate skill set to navigate complex databases is challenging. These data wranglers must understand both clinical and technical concepts. Often, investigators make data-related requests that are much too ambitious for the available staff. One approach is to identify tasks that investigators can do themselves. Multi-Institutional Consortia Such consortia can assemble datasets or analyze data from several institutions. These consortia raise many governance challenges, including the need to identify best practices. Best practices for multi-institutional data analysis need to be synthesized. Although de-identified or limited datasets can be shared among institutions without violating HIPAA, health system leaders are concerned about the potential for unfair competitive intelligence or reputational damage if a study shows that their institution s performance is not as good as another institution s. Data Quality Not enough attention is paid to data quality, and not enough systems are assessing and correcting data quality. A major reason why so many investigators build their own datasets is that they do 10

13 not trust the quality of clinical and other data in their institution. Therefore, the data in warehouses must be of reliable quality to ensure that researchers use this resource. Threats to data quality due to processing are easier to detect and correct than problems related to how data were originally collected. These collection issues are the cause of many data quality problems. In some institutions, information in a registry cannot be used to correct information in an EHR. Therefore, patients need to provide feedback on errors in their EHR data. Training and Education Staff need a culture of data quality, rewards for behaviors that ensure data quality, and an understanding of the secondary uses of the data they are entering. Data literacy education could also be helpful for researchers, clinicians, and students. Uses of EHR Data in Research EHR data can be used to support grant applications or accrue patients to clinical trials, identify adverse events, and populate research registries with data of appropriate quality. However, investigators do not know how to use EHR data in their research, and IT departments need to provide assistance in this area. Next Steps Top Priorities The Clinical Research Forum will form working groups to address the topics below. Each working group will Identify suitable partners for its activities Determine existing standards and evidence to ensure that they base their activities on what is already known Focus on areas within their topic in which the Clinical Research Forum can make a unique contribution Develop activities that are manageable Give progress reports at future IT Roundtable meetings 1. Clinical Research IT Maturity Models Focus: Determine whether existing maturity models are applicable to clinical research IT. If not, the group will develop, publish, and promote clinical research IT maturity models. This activity 11

14 will not be linked to accreditation or certification, at least initially. The group s deliverables could ultimately become part of the peer review and funding processes for AHCs. Deliverables: Survey instrument to collect information from AHCs on various aspects of research IT maturity, such as governance, recruitment, and reuse of data for research Mechanism to send survey results to AHCs so that they can determine their strengths and areas needing development Potential partners: HIMMS Analytics models and Educause Center for Applied Research maturity indices of higher education IT 2. Use of Governance to Build Trust Focus: Determine the policies and governance models that AHCs are using for different aspects of clinical research IT, such as data sharing and recruitment; in particular, address the role of governance in sharing information between systems to support clinical research Deliverables: Library of governance models and policies (including institutional consent forms, data use agreements, and other governance documents) used successfully by AHCs Potential partners: Industry 3. Ensure that Data Used in Clinical Research Are of High Quality Focus: Identify ways for organizations to implement best practices for their data warehouses to create decision-quality data that can serve as the basis for clinical research and operational decisions Other Potential Action Steps Although the issues below are a lower priority than those described above, the Clinical Research Forum might form small groups to address these issues or support other activities in these areas. 4. Identify Best Practices for Recruitment Using EHR Data Focus: Review the evidence base regarding which recruitment practices are and are not effective. Where gaps are identified, identify approaches to develop better recruitment tools and measure their effectiveness. One approach might be to collect data on recruitment strategies (by, for example, randomly assigning different sites to use different recruitment methods for the same protocol). 5. Develop Computable Institutional Consent Forms Focus: Collect successful computable institutional consent forms, use this information to identify features of computable institutional consent forms, and share this information with EHR vendors 12

15 Partners: Vendors 6. Educate and Train AHC Personnel Focus: Provide training (through short and long courses or short videos) in data literacy, stewardship, and related issues to clinical researchers and others who collect, enter, process, or analyze data used in clinical research 13

16 Appendix A: Participants Nicholas R. Anderson, Ph.D. Director of Informatics Research University of California, Davis Patrick Archdeacon, M.D. Medical Officer, Office of Medical Policy Center for Drug Evaluation and Research (CDER) Food and Drug Administration Raymond P. Bain, M.D. Vice President - Biostatistics & Research Decision Sciences Merck Research Laboratories Douglas Bell, M.D., Ph.D. Professor, General Internal Medicine Leader, Biomedical Informatics Program, Clinical and Translational Science Institute University of California Los Angeles Melody G. Bell Project Manager University of Texas Southwestern Medical Center Enriqueta Bond, Ph.D. QE Philanthropic Advisors Richard Bookman, Ph.D. Associate Professor of Molecular & Cellular Pharmacology University of Miami Sharon A. Bowser, M.B.A. Interim Associate Dean and Chief Information Officer for Information Services Assistant Dean and Deputy Chief Information Officer University of Maryland School of Medicine Kyle Brown CEO & Founder Patient Crossroads Michael Cain, M.D. Vice President for Health Sciences Dean, School of Medicine and Biomedical Sciences University at Buffalo 14

17 Robert Califf, M.D. Director, Duke Translational Medicine Institute Donald F. Fortin, M.D. Professor of Cardiology Vice Chancellor for Clinical and Translational Research Duke University Thomas Campion, Ph.D. Assistant Professor of Healthcare Policy and Research in Pediatrics Weill Cornell Medical College Kari Cassel, M.B.A. Senior Vice President and Chief Information Officer University of Florida Academic Health Center Christopher G. Chute, M.D., Dr.P.H. Professor, Biomedical Informatics Mayo Clinic Elaine Collier, M.D. Senior Advisor to the Director for Informatics and Clinical Research National Center for Advancing Translational Sciences Lawrence Cornett, Ph.D. Director, Office of Grants & Scientific Publications Professor, Department of Physiology and Biophysicist Vice Chancellor for Research University of Arkansas for Medical Sciences Anne Curtis, M.D. Charles and Mary Bauer Professor and Chair and Distinguished Professor, Department of Medicine University at Buffalo Mahasweta Dutt, M.S. Clinical Research Navigator University of Pennsylvania David Dworaczyk, Ph.D. Life and Health Sciences Strategic Development Oracle Corporation Bari Dzomba Project and Program Management Research Informatics Penn State University 15

18 Peter Embi, M.D., M.S. Vice-Chair, Department of Biomedical Informatics Chief Research Information Officer The Ohio State University Medical Center Maurizio Fava, M.D. Director, Clinical Research Program Executive Vice Chair, Department of Psychiatry Executive Director, Clinical Trials Network & Institute Massachusetts General Hospital Barbara Ferry Clinical Research Programs Manager & Investigator Advocate Northwestern University Clinical and Translational Sciences Institute Edmund Finnerty University of North Carolina Chapel Hill Robin Fiore, Ph.D. Associate Professor University of Miami Ethics Programs and Miller School of Medicine Daniel E. Ford, M.D., M.P.H Vice Dean for Clinical Investigation and David M. Levine Professor of Medicine and Psychiatry Johns Hopkins University School of Medicine Matthew Forman Epic Systems Gregg Fromell, M.D. Vice President of Science Operations and Transformation, Office of Science and Research New York University Medical Center William J. Fultz Vice President for Research Information Technology Icahn School of Medicine at Mount Sinai Elaine Gallin, PhD QE Philanthropic Advisors Geoffrey Gordon IT Director Health System Information Services University of Alabama at Birmingham School of Medicine 16

19 Riva Gottesman, M.P.A. Manager of Hospital Information Systems Rockefeller University Sarah Greene, M.P.H. Associate Director, Science Patient Centered Outcomes Research Institute Beau Grignon Director, Product Development Forte Research Systems Diana Gumas Director of Clinical and Clinical Research, Information Systems Johns Hopkins School of Medicine Michael Halaas Chief Information Officer, School of Medicine Stanford University David Hanauer, M.D. Clinical Assistant Professor, Pediatrics University of Michigan Health System Paul Harris, Ph.D. Director, Office of Research Informatics Professor of Biomedical Informatics Vanderbilt University School of Medicine Judith Hochman, M.D., M.A. Harold Snyder Family Professor of Cardiology Associate Director, Leon H. Charney Division of Cardiology Senior Associate Dean for Clinical Sciences New York University Medical Center William R. Hogan, M.D., M.S. Director, Biomedical Informatics University of Florida Margaret Hogue Product Manager Allscripts 17

20 Allen Hsiao, M.D. Associate Professor of Pediatrics and of Emergency Medicine Yale University Chief Medical Information Officer Yale-New Haven Hospital Stephen B. Johnson, Ph.D. Professor of Healthcare Policy and Research Direcotr of Biomedical Informatics, Clinical and Translational Science Center Weill Cornell Medical College Tesheia Johnson, M.B.A., M.H.S. Chief Operating Officer, Center for Clinical Investigation Associate Director, Clinical Research, School of Medicine Yale University Ummey Fatema Johra Technical Project Lead Rockefeller University Michael Kahn, M.D., Ph.D. Associate Professor, Department of Pediatrics University of Colorado, Denver Co-Director, Colorado Clinical and Translational Sciences Institute Biomedical Informatics Director of Clinical Informatics, Department of Quality & Patient Safety The Children s Hospital Srini Kalluri Founder, President, CEO, and Chief Customer Officer Forte Research Systems Michael Kamerick TriNetX, Inc. Saravanan Kanakasabai, Ph.D. Director, Clinical Research Systems Indiana University Diane Keogh Corporate Director Research Computing, Information Systems Partners HealthCare, Systems, Inc. Karen Kimura Velos 18

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