Duke Translationa l M e d ic in e Institute

Transcription

1 Duke Translationa l M e d ic in e Institute Robert M. Califf, MD, MACC Vice Chancellor for Clinical Res ea r c h Direct or, Duke Translation al M edicine Ins t itute October 3, 2012 Ron Fitzmartin, PhD Office of Planning and Informatics Center for Drug Evaluation New Hampshire Ave., Bldg. 51, Room 1160 Silver Spring, MD RE: Docket No. FDA-2012-N-0780 Regulatory New Drug Review: Solutions for Study Data Exchange Standards Dear Dr. Fitzmartin: Duke University appreciates the opportunity to provide comment on the Advance Notice of Proposed Rulemaking entitled "Solutions for Study Data Exchange Standards. Duke University commends the FDA for moving forward and addressing the Industry s need for a common data exchange standard at this time of increasing workloads and uncertain funding. It is important that FDA select one data exchange standard and publish a calendar for moving towards that one standard. Until this happens the Industry must plan for and train people on multiple standards in case multiple standards are used. There is nothing reusable so it is very expensive. As one of the leading federally- funded academic research institutions in the country, Duke has extensive experience with the challenge the current environment of multiple standards and multiple versions of standards pose for contemporary medical product development research. Duke has also contributed significantly to the development of both the Health Level Seven (HL7) and Clinical Data Interchange Standards Consortium (CDISC) standards, so we feel that we can provide insight to the process on which the FDA has embarked. The national interest in and requirements for a Learning Health System dictates a transparent standard that is interoperable and supports research. HL7 is already interoperable. Any delay by the medical product development industry in moving to a compatible standard will delay the integration of information from these enterprises. A common standard will support the vision of relieving the burden on clinical investigator sites of duplicate data collection and transfer. The FY 2012 Budget Request for the Office of the National Commissioner (ONC) was $61.2 million and the FY 2013 is $66.3 million, including $40.0 million in Public Health Service (PHS) Evaluation Funds to support program activities and carry out Recovery Act responsibilities. This Budget request supports the implementation of the Federal Health IT Strategic Plan and HHS Strategic Plan and will contribute to advancing priorities which include Achieving Adoption and Information Exchange through Meaningful Use of Health IT, inspiring Confidence and Trust in Health IT and achieving Rapid Learning and Technological Advancement. Data

2 exchange standards development efforts of the FDA should be aligned with these Health Information Technology (HIT) goals to increase efficiency and reduce the likelihood of additional standards development and retooling requirements of the Industry in the future. Duke recognizes that some problems of standardization are inherent in the processand need to be addressed no matter what exchange standard is chosen. These include but are not limited to: HL7 and CDISC both lack the semantic specificity needed for data reuse. HL7 relies on binding of terminology most of which do not include concept definitions. The CDISC Submission Data Tabulation Model (SDTM) does not provide for unique mapping of some Case Report Form (CRF) data into the model, i.e., more than one mapping of fields from a CRF to the SDTM can be conformant. 1 This means that for some data in the SDTM today, v1.3, is underspecified. 1 In addition, the SDTM v1.3 primarily addresses data common across therapeutic areas, e.g., adverse events, demography, vital signs, physical exam. Thus, the SDTM standard today lacks coverage of clinical domainspecific data, i.e., efficacy data critical to drug evaluation and regulatory decision making. Statement of these problems should not be seen as an undervaluing of the strong SDTM body of work. Rather, the problem statement reflects the complex reality of semantic interoperability. Work is underway (see PAR ) to develop standard data elements with authoritative clinical definitions. Data elements will need to be mapped to the HL7 or CDISC standards, whichever is chosen. Today s software does not sufficiently support either HL7 or CDISC standards, i.e., does not leverage the standards to provide even partial automation. Thus, we are far from realizing process improvements and cost savings that standards promise. Because of this, today, use of standards on a clinical study is more costly for the sponsor than not. This will be the case until software and organizational processes use the standards to provide facilitation and automation. Standards development takes time and money and invites risk associated with decisions by others via the consensus process. Further, standards need to be changed over time to keep up with scientific, business, technology or other growth. Standards need to be upgraded and either backwards compatible or with migration pathways over time. The docket presents 10 specific questions for consideration. Duke has elected to provide general commentary on broader challenges and opportunities related to health information exchange related to these questions. These follow: 1. What are the most pressing challenges that industry faces with regard to study data management? Please address each of the following areas: What opportunities/solutions exist to meet each challenge? The cost of conducting clinical research in the United States is outpacing our capacity. 2,3 Redundant collection of data places a large burden on investigators. 4,5 Enhanced IT systems have been cited as one of the top four ways to improve the efficiency of the research enterprise in the

3 United States. 5 Data re-collection and chart abstraction costs our institution an estimated 2 million dollars annually, while exceedingly large amounts of relevant information reside untapped in healthcare data systems. In most healthcare institutions, the data collected as a by-product of care is not easily re-purposed for secondary uses, especially research. 4 While progress on individual clinical data warehouses, data standards, and the application of data governance methods within healthcare has been made, a scalable and sustainable model for secondary data re-use has not been demonstrated. 4 With the goal of transforming the US clinical research enterprise, the Institute of Medicine has called for a learning healthcare system to accelerate cost-effective generation of new evidence directly from and applicable to patient care processes. 6,7 This new model envisions conducting clinical research as part of patient care. 8 We applaud this effort of the FDA and believe solving the problem of disparate clinical data standards enabling integration of clinical and research activities will yield significant operational and scientific benefit. Specific comments on the requested topics are included below: a) Study design/set-up: Protocol documents lack sufficient structure in the study design details to drive study set-up. Protocols must be interpreted by each discipline (site management, monitoring, data management, statistics etc.) and independently translated into implementation specifications within that specialty area. These are instantiated in the variety of research information systems for the purpose of operationalizing that protocol and its specified analysis. Current study set-up practices yield a set of inflexible siloed data systems that lack interoperability creating an environment unable to efficiently and effectively support evolving scientific questions and adaptive trial designs. Opportunity: Current study-specific database setups are not scalable beyond their original purpose. There is significant opportunity for structured representation of protocols and standardized shared libraries of clinical assessments to enable metadata driven tooling that will increase consistency and reduce costs of study design and set-up. Platforms that can be extended and dynamically changed in response to evolving scientific understanding and adaptive study designs integrated with clinical workflows are necessary. Also necessary are agile software development practices that don t increase (real or perceived) risks of non-compliance with computer systems validation expectations. Research and patient-care related data standards must be utilized for this to be achieved. b) Capture: Data capture at the original source is not often discussed in routine clinical studies; focus is on transcription to the EDC system or integration from 3 rd party research systems into the study database. Generally, EDC software is oriented toward end-user functionality and inadequately supports direct (without custom manipulation/rerepresentation) use of data for intended analysis. Current data capture processes are mature; opportunity exists for more tightly integrated processes with data generation and usage. Opportunity: Significant efficiencies will be achieved for clinical research, and other secondary reporting purposes, when study data capture becomes the same act as data capture from clinical care processes. If metadata driven study setup is realized, new or revised protocol data requirements will be easily pushed into the EHR-based data

4 capture tools. Additionally, metadata specifications for data capture should be expected to carry process information, such as triggering a new clinical assessment or notification of protocol-specified requirements, and data quality conformance requirements. This metadata must be specified independent of use case, enabling support for multiple scenarios and eventual different representations of data based on business requirements for its use. c) Integration: The primary endpoint data for clinical studies is decreasingly captured in case report forms and increasingly the result of clinical event classification, core laboratories, medical devices and other decentralized information systems. Custom data integration is a significant effort and most integrations are implemented for individual studies with limited reusability; most of the time spent is on defining and managing the content (semantics) of the data for each study while the mechanical technical challenges more routine but still costly. These integrations require costly technology and skilled resources both difficult to scale, and like data capture the post go-live inflexibility presents a significant challenge. The exchange of health information from the clinical setting to a study database, then a study database to a regulatory agency utilize distinct formats, tools and resources with different applied knowledge. A data model specified via complete and stable standards will make data integration significantly more efficient. Opportunity: Data, both the semantics and the structure, must be specified once and implemented in various use cases. Transferring data from one use case to another should be a data sharing/business process centric task, not a software development task. A common health data standard will reduce the need for organizations managing study databases to build distinct capabilities (tools, processes and workforce) serving both sides of the data flow. Additionally, innovations representing both stages in the data flow such as the IHE RFD 9 and FDA Mini- Sentinel program 10 should be expected to converge. Secondary integrations, such as the FDA Data Warehouse (JANUS) or research organizations performing metaanalysis can gain capacity by focusing scientific resources on research questions rather than obtaining appropriately merged datasets. Accessible integrated data is the cornerstone of future safety surveillance and evaluation programs. d) Analysis and reporting: Similar to the items above, analysis and reporting are typically project-specific activities due to the underlying unique structure and content of the study dataset. Differences in adoption of data standards prevent significant reusability, particularly for organizations that support multiple sponsoring organizations or receive data from multiple organizations. When investment in merging of data for secondary analysis is made there are different statistical and interpretation skills to be applied than necessary for primary endpoint analysis. Opportunity: As barriers to data sharing are reduced there is opportunity to utilize larger integrated datasets merging observational data streams and randomized clinical trials. The potential for multi-disciplinary teams clinical investigators, statisticians, policy makers and others with visibility to the available evidence system presents the true opportunity to achieve a learning healthcare system. e) Regulatory submission: Specifically in the context of data standards, the challenge to organizations supporting regulatory submissions from multiple data sources is the

5 variable interpretation of standards by sponsoring companies which necessitates reviewers learning the data content and format for each study. Impacting these interpretations is the evolving landscape of expectations for adoption of data standards, highlighting the need for stability and scheduled versioning. Opportunity: We have yet to fully realize the promise of data standards to reduce research costs and barriers to data reuse. FDA s efforts to address the root causes of lacking semantic specificity in existing standards and need for stable standards is appreciated. 2. How could FDA s regulatory requirements make the study data management process more efficient? It is important that the FDA reduce the barriers to transfer of data directly from the electronic health records to medical product development research. Regulatory requirements should not continue to encourage transcribing and re-entry of data for research use. Requiring 21CFR11 compliance, as currently interpreted by many, for the Electronic Health Record industry is not appropriate EHRs are covered by HIPAA security and ONC certification requirements. FDA Regulatory requirements that enable the appropriate professional organizations to define computer systems, software, data quality and appropriate statistical methods to enable direct use of the EHR information is needed. Duke does not see the efficiency of the study data management process as an FDA responsibility. The E2B 11 requirements and Society of Clinical Data Management Good Clinical Data Management Practice are sufficient. The FDA can enable efficiency in this area through regulatory requirements of interoperability/interoperations of Health Interchange Standards. The FDA should mandate the clinical data model they expect to receive. Presently the FDA suggests using the CDISC models but it is not mandated. Also, because of the variations in interpretation of the CDISC SDTM and ADaM models the FDA would need to cleanly define their interpretation of the models so the industry could adopt that. 3. What does industry need to make clinical trials data management more effective and efficient? Please describe the tools, techniques, and processes that would help as well as the regulatory guidance documents that would be useful in this area. There are a few things that could be done to make clinical trials data management more effective and efficient. There needs to be a mandated industry-wide clinical data standard to follow to improve efficiency. We need processes for targeted clinical data management that focus labor on the data of scientific importance. Finally, we need a regulatory environment that encourages innovation and improved efficiency in clinical data management work processes. Clinical trials data management is made inefficient due to the lack of a mandated clinical data standard. Every pharmaceutical company and supporting CRO is either working with proprietary clinical data models or they have their own variable interpretation of an industry standard such as

6 the CDISC SDTM. Because there is no true industry clinical data standard, every clinical trial remains a one-off exercise where little can be reused. Collection forms must be repeatedly redesigned, data exchange must be renegotiated, metadata must be reentered, and staff must learn new data structures for new projects. We need a FDA mandated stable or backwards compatible industry clinical data standard in which software and processes can be standardized around it to improve clinical data management efficiency. Clinical data management processes need to focus effort on the data that has scientific importance. Too often the data collection tool is developed without proper statistical input. Clinical measures defined in the study protocol may not be collected in a way that lends itself to be analyzed either because the data collected is not measurable (free text), inconsistent (collected twice), or collected in a way that cannot be linked properly with the other trial data. Finally, clinical data management needs to focus data collection and cleaning efforts more precisely on the data elements that are critical for analysis purposes. To make clinical trials data management more effective and efficient we need to reconsider the current regulatory and business environment to stimulate innovation. Innovation in clinical data management has been stifled since the implementation of CFR 21 Part 11 and the subsequent associated bureaucratic processes adopted by the industry. Clinical data management system development has gotten bogged down and needs the ability to achieve the nimble and agile development seen in other modern technologies. We need regulation that fosters innovation in these tools while still protecting public health. 4. What data standards are you currently using for the conduct of regulated research studies? Regulated clinical research activities are conducted throughout our large academic medical center and health system, including multi-center phase I-IV clinical trials. We recognize patient data begins in the clinical environment and traverses a variety of clinical information systems, integrated using HL7 standards, before this becomes designated as clinical trial subject data (often when medical records are transcribed into an EDC system). Related subject data from laboratory, imaging, IVRS and similar systems are integrated with the subject data in a study database. The same process is typical on our small academic studies and large international Phase III mega trials. When viewed holistically, the standards used in the conduct of regulated research studies are many. A sampling of data standards we use from the perspective of the central data management center for regulated studies includes: HL7 (aecg), CDISC (Terminology, CDASH, ODM, SDTM, ADaM, CRT-DDS), ICH (E2B) plus various terminologies (e.g. MedDRA, WHO Drug) and standard data element sets. 5. Would Health Level Seven v3 (e.g., messages, structured documents and Clinical Data Architecture) be a viable study data exchange standard? Please explain advantages and

7 disadvantages. What would be the impact (e.g., financial, technical, or in terms of implementation or change in business processes)? HL7 is a viable study exchange standard. HL7 has over a decade of established track record in healthcare information exchange via version 2.x messages. Version 3 is an improvement on the early experience with version 2, version 3 made a much needed architecture change to technical specifications that all derive from constraints on the Reference Information Model (RIM). Version 3 provides the increased semantic specificity and consistency in representation not achievable in Version 2. Although uptake of version 3 has been slow, this is to be expected with significant architecture changes, and will allow HL7 standards to grow and support health IT for years to come. We provide our assessment on HL7 as an exchange standard for clinical study data in terms of advantages, disadvantages, and challenges to e expected with any approach. Advantages of Using HL7 as the study data exchange standard: The use of HL7 in healthcare and the EHR significantly enhances the ability to use healthcare data in support of regulated clinical studies, i.e., to integrate research and care. Exchange of data between healthcare providers is a similar problem, the correct data from a clinical encounter have to be identified and transferred to another provider or to a Health Information Exchange. The HL7 Clinical Document Architecture (CDA) is relied upon by the Meaningful Use requirements (Stage 1, Eligible provider core measure 14) to do this. Further, Meaningful Use requires use of certified EHR products. The government relies on the HL7 EHR Functional Model as a basis for certification. HL7 terminology has been selected and recommended by several federal committees and taskforces, and HL7 messages are used for claims. Health care information exchange is largely based on HL7 standards for information exchange. The optimal solution will leverage this existing infrastructure for standards development & maintenance and the existing infrastructure in healthcare facilities for data exchange.

8 Integration of healthcare and research streamlines the process through which data are collected for studies. Fewer steps and collection of data closer to the occurrence of the event of interest provides better quality data. Health IT staff for payers and providers are already familiar with HL7, therefore HL7 use leverages existing health IT infrastructure rather than requiring software or process changes. Pharmaceutical companies are already attending HL7 and participating in standards development due to development of standards such as the ECG Waveform standard, Structured Product Label (SPL), and the Individual Case Safety Report (ICSR) thus, they are familiar and involved. Today, some companies are piloting extracting data from a local Health Information Exchange everyone will have this option in the future which implies that HL7 would be a better long term solution. Federal requirements to use open and publicly available standards in guidance and regulation. 12 HL7 has recently committed to make their standards publically available. 13 Already serving healthcare, HL7 is a scalable and sustainable standards development infrastructure. Using HL7 standards taps into a great deal of money already invested and provides reliable standards maintenance. Research is one domain among many in the broader healthcare context. Research can benefit from the broader community that is working to address the same problems (data specification, reusability, metadata-based transfers, metadata libraries, provenance, etc.); attempting to solve it isolated within the domain is expensive. The HL7 clinical statement pattern is common backbone for both the CDA and messages, thus it doesn t matter what technical specification is chosen because the clinical statement pattern enables use of both while maintaining consistency with healthcare data. Use of standard data elements with concept unique identifiers will provide the semantic specificity required for data reuse. Disadvantages Use of HL7 as the study data exchange standard will require re-tooling for the medical product development industry in US. Many companies have invested significantly in competing transfer standards, e.g., CDISC ODM. Although Pharmaceutical companies are attending HL7, to our knowledge there is no widespread use of HL7 in internal information architectures. Further, clinical study vendors (software and CROs) are largely absent in HL7. Thus, the time to adoption and software that leverages the standards to facilitate and expedite drug development will likely be longer with HL7 standards. HL7 technical specifications rely on the underlying RIM and are harder for clinical research data managers, statisticians, and IT personnel to understand. Thus, HL7 will require re-training for positions responsible for semantic mapping and data exchange design and architecture. This training/re-training will ultimately be needed for smooth transition.

9 Use of HL7 for data exchange may require limited extension to carry the audit trail with the data. 6. Would CDISC Operational Data Model 2 be a viable study data exchange standard? Please explain advantages and disadvantages. What would be the impact (e.g., financial, technical, or in terms of implementation or change in business processes)? CDISC ODM can be a viable study exchange standard. The Industry and FDA have worked collaboratively for over a decade to develop a series of connected standards: SDTM, LAB, ODM, and others. ODM is simply a file format change from the currently used SAS XPORT format. Many companies have invested either in the standards development, or in standards use or adoption processes and procedures. Advantages of using CDISC ODM as the study data exchange standard: While not embraced by all vendors, many stakeholders participated in CDISC development and currently utilize this standard in their clinical trials data systems. Thirty-two global companies are CDISC charter sponsors and fifteen are listed as ODM registered solution providers ( ). CDISC ODM has the audit trail. It is a best practice for in the medical product development industry for the audit trail to ride with the data. It is important that this audit trail be available for any standard approved by the FDA. ODM is used as an exchange standard between clinical data vendors today. Disadvantages of using CDISC ODM as the study data exchange standard: The ODM standard improves data transfer, but was designed outside the EHR environment. It was not optimized for healthcare data management and flow. Thus, ODM may not be compatible with EHR. ODM is bulky / verbose. Further, it supports relationships among data but doesn t prescribe how they are implemented (allows for vendor custom extensions). This flexibility impedes automation of data exchange and reuse. There s major overlap and inconsistency with CDA. If this wasn t a legacy issue, CDA could just be extended to serve ODM s use cases but the core clinical data would stay in the same model. ODM lacks prescribed requirements. The flexibility of the CDISC system, which supports relationships among data and allows for custom vendor extensions, has led to inconsistent implementation at healthcare sites. CDISC offers an open standard, but no significant technical advantages. By focusing on restrictions inherent to the SAS-based XPORT file format which was commonly used in 1995, the CDISC standard does not incorporate technical and healthcare needs that developed in the last decade.

10 The ODM model generally includes a transcription step where data is re-coded at the healthcare site before submission to the FDA. This transcription process been inconsistently implemented, prompting CDER to release a Common Data Standards Issues Document to communicate the Agency s preferences and experiences regarding common errors. These inconsistencies lead to extra work and do not allow industry to realize efficiency. In contrast, HL7 has the potential to directly transfer data from the healthcare site to the regulatory agency. HL7 does not require transcription and so may improve efficiency. 7. Are there other open data exchange standards that should be evaluated? Please explain advantages and disadvantages. What would be the impact (e.g., financial, technical, or in terms of implementation or change in business processes)? HL7 is clearly the central player in healthcare data exchange standards. Other standards exist for complimentary use cases (e.g. DICOM, IEEE, ASTM), but none that require evaluation, in contrast to HL7 for healthcare data exchange. As new models, methods and tools are conceived they might begin as independent of (or competitive with) HL7, and if proven appropriate for mainstream adoption, are internalized and harmonized into the HL7 organization and products. Thus, HL7 serves as both the set of recognized standards and the forum for the vetting and management of those new or competing standards which make the overall standards more robust. There is value to the FDA for HL7 serving as the community forum for this vetting process. Additionally, interoperability underlies the effectiveness of a learning healthcare system and the integration of HL7 standards allows research to be an integrated, rather than adjunct, member of the community. There is financial, technical, implementation strategy and business process impact to transition the research domain and individual organizations to a new or revised set of data standards. Greatest impact will be on organizations that have fully internalized and hard-coded CDISC standards into their operations. However, it is not possible in these brief comments to detail these negative impacts or the benefits of selecting an alternate path toward standardized data exchange. Secondary benefits of research standards that are more tightly integrated with the healthcare environment and opportunity for the knowledge and tools from the broader healthcare data standards community directly applied to research must be also considered. The most significant investment in both development and implementation and tooling has been in SDTM. We see SDTM as a valuable format standard in terms of invested development, and because therapeutic data elements can be folded into the standard. Thus, we see the optimal path forward in terms of meeting data reuse requirements and in terms of minimizing impact to the regulated industry as using SDTM to format the data (as is currently done by many organizations) and using HL7 as the method of exchange. 8. What would be a reasonable phased implementation period for each recommended exchange standard?

11 We think that it would be best if the FDA adopted a single standard and not multiple data exchange standards. A single data exchange standard that is based on a single underlying clinical data standard would help to bring harmony to a fractured clinical data environment. We fear that if multiple exchange standards are adopted then that will lead to confusion and inefficiency in implementation of the standards. One clinical data exchange standard that everyone was mandated to comply with would be best. The FDA can help industry to comply with a new standard by committing to the data exchange standard for a specified time horizon. One problem that we have had in the past is that we have been unsure of the FDA s enduring commitment to any given standard. We as an industry need to be assured of a time window for the life of the standard so that we and our vendors can invest in that standard. Complying with new data standards is expensive, but the FDA can help the industry mitigate that financial risk by giving us timelines around their commitment to a given data exchange standard. Grandfathering retired clinical data exchange standards is also recommended, particularly within a drug or device development program. Costs of standards development and adoption should not be underestimated. Recent federal initiatives to advance Health IT are examples and may provide value in adopting a consistent framework for driving adoption. Due to the significant investment in and utility of SDTM, we see the optimal path forward as using SDTM to format the data (as is currently done by many organizations) and using HL7 as the method of exchange. 9. FDA encourages sponsors to design study data collection systems so that relationships between data elements, as well as relationships across data domains, can be captured at the point of data entry. Describe the challenges, to and opportunities for, accomplishing this goal. It is good that the FDA encourages sponsors to build data collection systems that capture data relationships across clinical data domains as these relationships can be critical to statistical analyses in terms of assessing covariates, statistical modeling, and other statistical inferences. In the industry today we have numerous clinical related data collection systems including but not limited to EHR, clinical trial database (EDC), coding, safety reporting, IVRS, clinical trial management, and various laboratory data. The data is often collected with varying levels of redundancy, quality, and fidelity which make it nearly impossible to assimilate with confidence on the back end. We need the clinical data collected and stored in a way so that there is one truth about a patient s medical history and not multiple interpretations depending on how one reassembles the patient s clinical data on the back end. There are two hurdles preventing clinical data collection systems from capturing data relationships across data domains. As mentioned before, a lack of a single industry data standard has prevented vendors from building data collection tools optimized to use data standards. If there were a single mandated clinical data standard then vendors would be more willing to invest in building tools that stored data in that format. If there were a single mandated clinical data standard then it would be easier to reconcile that information between data systems, although data

12 quality would still be a concern in the event of redundant data collection. We also need industry agreed upon common application program interfaces (APIs) so that the various data capture tools can communicate with one another easily without one-off or custom interfaces needing to be built. Right now everyone has to craft and maintain custom interfaces between the applications in their clinical data application portfolio. A common clinical data application architecture would allow the various software packages to communicate more readily. 10. What other comments would you care to share with FDA concerning the general topic of data exchange standards? Solutions need to incorporate Clinical Professional Societies in how semantics are developed. If they are not directly included, adoption will suffer and the standards will not sufficiently meet the needs. Best practice in clinical data standards development relies on a foundational set of clinical data definitions provided by clinicians and then representing that clinical content in a technical specification. Unfortunately, the standards development process for technical specifications are currently done twice once each for use by HL7 and CDISC. A harmonized set of data standards serving both the healthcare and clinical research purposes will reduce the burden on the standards development process. During some of our recent work it has been a struggle to output results operable according to divergent standards. Competing needs of the standards is becoming cost prohibitive. FDA s own efforts are likely hampered by the lack of standardization. In the broader realm of healthcare, research is a relatively small player and ultimately involves less money. Motivation is higher on EMR side. The FDA can benefit/make progress faster by collaborating in the standards development for the EHRs. CDISC still has a valuable role to play. HL7 processes and forums are used for the artifacts, but the community of research represented by CDISC is still needed as Subject Matter Experts (SME's). CDISC could get more connected to Integrating the Healthcare Enterprise ( and use its SME's for requirements, demonstration testing, tooling, and policy development. CDISC may be considered analogous to the clinical professional societies or to AHIMA. CDISC can shift to standards development processes to impact the content of the standards and the associated business processes. Further and most important: the most significant investment in both development and implementation and tooling has been in SDTM. We see SDTM as a valuable format standard in terms of invested development, and because therapeutic data elements can be folded into the standard. We see the optimal path forward as using SDTM to format the data (as is currently done by many organizations) and using HL7 as the method of exchange.

13 References: 1. Perry MT, Barnett ME, Dent A, et al. Use and evaluation of standards for investigatorinitiated studies: preliminary results American Medical Informatics Association (AMIA) Clinical Research Informatics Summit. San Francisco: AMIA; US Food and Drug Administration. Innovation Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. In: Services HaH, ed Rockville MD Malakoff D. Clinical trials and tribulations. Spiraling costs threaten gridlock. Science. Oct ;322(5899): Kush R, Alschuler L, Ruggeri R, et al. Implementing Single Source: the STARBRITE proof-of-concept study. J Am Med Inform Assoc. Sep-Oct 2007;14(5): Sung NS, Crowley WF, Jr., Genel M, et al. Central challenges facing the national clinical research enterprise. JAMA. Mar ;289(10): Olsen L, Aisner D, McGinnis J. Roundtable on Evidence-Based Medicine. Institute of Medicine: Learning Healthcare System; Workshop Summary. Institute of Medicine of The National Academies, Washington DC Friedman CP, Wong AK, Blumenthal D. Achieving a Nationwide learning health system. Sci Transl Med 2010;2:57cm Conway PH, Clancy C. Transformation of health care at the front line. JAMA 2009;301: IHE Current Technical Framework. 31 Aug Available at Accessed August 28, Rocca, M. (2012, September). FDA Session of the Fall SCDM Conference. 11. Maintenance of the ICH Guideline on Clinical Safety Data Management: Data Elements for Transmission of Individual Case Safety Reports E2B(R2). 05Feb2001. Available at Step4/E2B_R2 Guideline.pdf, Accessed August 28, HIT Standards Committee: Recommendations to the National Coordinator for Health IT. Available at standards_recomm endations/1818, Accessed August 28, Jaffe, C. Special Announcement from the HL7 CEO. HL7 e-news. Press Release;04Sept2012

14 In conclusion, Duke supports efforts to review, clarify and harmonize on a common data exchange standard for the clinical and research enterprises. In this process it is important that the fundamental needs of the clinical research and learning health systems serve as the starting point for change. In addition we believe that the standards need to be implemented and maintained with a focus on meeting the industry need of having data exchange standards that are stable, change or are upgraded on a controlled schedule that facilitates planning and up versioning in the Industry and that a commitment be made to generating a compatible system of standards. Common data standards and guidance on the application of the standards would go a long way toward reducing the burden on the research community. We extend an offer to work directly with FDA as they move forward in the process of revising the standards and in the development of regulatory guidance. Sincerely, Robert M. Califf, MD, MACC Vice Chancellor for Clinical Research Director, Duke Translational Medicine Institute Donald F. Fortin Professor of Cardiology Duke University School of Medicine BOX DUMC 3701 TEL URL DEL 1117 Davison Building FAX Durham, NC dukemedicine.org