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Covid-19 has already taught us that the greatest public health challenges of our generation will show no respect for national boundaries, will impact lives and health of people of all nations, and will affect economies and quality of life in unprecedented ways. The types of rapid learning envisioned to address Covid-19 and future public health crises require a systems approach that enables sharing of data and lessons learned at scale. Agreement on a systems approach augmented by technology and standards will be foundational to making such learning meaningful and to ensuring its scientific integrity. With this purpose in mind, a group of individuals from Spain, Italy, and the United States have formed a transatlantic collaboration, with the aim of generating a proposed comprehensive standards-based systems approach and data-driven framework for collection, management, and analysis of high-quality data. This framework will inform decisions in managing clinical responses and social measures to overcome the Covid-19 global pandemic and to prepare for future public health crises. We first argue that standardized data of the type now common in global regulated clinical research is the essential fuel that will power a global system for addressing (and preventing) current and future pandemics. We then present a blueprint for a system that will put these data to use in driving a range of key decisions. In the context of this system, we describe and categorize the specific types of data the system will require for different purposes and document the standards currently in use for each of these categories in the three nations participating in this work. In so doing, we anticipate some of the challenges to harmonizing these data but also suggest opportunities for further global standardization and harmonization. While we have scaled this transnational effort to three nations, we hope to stimulate an international dialogue with a culmination of realizing such a system.

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The benefits of reusing EHR data for clinical research studies are numerous. They portend the opportunity to bring new therapies to patients sooner, potentially at a lower cost, and to accelerate learning health cycles-through faster data acquisition in clinical research studies. Metrics have proven that time can be saved, workflow and processes streamlined, and data quality increased significantly. Pilot projects and now actual investigational trials used for regulatory submissions have shown that these benefits support the transformation of clinical research by leveraging EHRs for research. Panelists at a recent collaborative focused on bridging clinical research and clinical care offered varying perspectives on how the latest standards and technologies could be leveraged to facilitate data transfer from EHR systems into clinical research databases, as well as the associated improvements in data quality. Panelists also discussed other avenues to leverage EHR in clinical research. Improvements and exciting possibilities notwithstanding, much work remains. Data ownership and access, attention to metadata and structured data for data sharing, and broader adoption of global standards are key areas for collaboration. With the steady increase in adoption of EHRs around the world, this is an excellent time for all stakeholders to work together and create an environment such that EHRs can be used more readily for research. The capacity for research can thus be increased to provide more high-quality information that will contribute to rapid continuous learning health systems from which all patients can benefit.

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INTRODUCTION: Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. METHODS: The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. RESULTS: This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. CONCLUSIONS: The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients.

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The Learning Health Community is an emergent global multistakeholder grassroots incipient movement bonded together by a set of consensus Core Values Underlying a National-Scale Person-Centered Continuous Learning Health System developed at the 2012 Learning Health System (LHS) Summit. The Learning Health Community's Second LHS Summit was convened on December 8 to 9, 2016 building upon LHS efforts taking shape in order to achieve consensus on actions that, if taken, will advance LHSs and the LHS vision from what remain appealing concepts to a working reality for improving the health of individuals and populations globally. An iterative half-year collaborative revision process following the Second LHS Summit led to the development of the Learning Health Systems Consensus Action Plan.

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OBJECTIVES: We examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach. DESIGN AND METHODS: This was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European. OUTCOME: We developed principles and practical recommendations on how to share data from clinical trials. RESULTS: The task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata. CONCLUSIONS: The adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

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BACKGROUND: It is critical to integrate and analyze data from biological, translational, and clinical studies with data from health systems; however, electronic artifacts are stored in thousands of disparate systems that are often unable to readily exchange data. OBJECTIVE: To facilitate meaningful data exchange, a model that presents a common understanding of biomedical research concepts and their relationships with health care semantics is required. The Biomedical Research Integrated Domain Group (BRIDG) domain information model fulfills this need. Software systems created from BRIDG have shared meaning "baked in," enabling interoperability among disparate systems. For nearly 10 years, the Clinical Data Standards Interchange Consortium, the National Cancer Institute, the US Food and Drug Administration, and Health Level 7 International have been key stakeholders in developing BRIDG. METHODS: BRIDG is an open-source Unified Modeling Language-class model developed through use cases and harmonization with other models. RESULTS: With its 4+ releases, BRIDG includes clinical and now translational research concepts in its Common, Protocol Representation, Study Conduct, Adverse Events, Regulatory, Statistical Analysis, Experiment, Biospecimen, and Molecular Biology subdomains. INTERPRETATION: The model is a Clinical Data Standards Interchange Consortium, Health Level 7 International, and International Standards Organization standard that has been utilized in national and international standards-based software development projects. It will continue to mature and evolve in the areas of clinical imaging, pathology, ontology, and vocabulary support. BRIDG 4.1.1 and prior releases are freely available at https://bridgmodel.nci.nih.gov .

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OBJECTIVES: To describe the IMI EHR4CR project which is designing and developing, and aims to demonstrate, a scalable, widely acceptable and efficient approach to interoperability between EHR systems and clinical research systems. METHODS: The IMI EHR4CR project is combining and extending several previously isolated state-of-the-art technical components through a new approach to develop a platform for reusing EHR data to support medical research. This will be achieved through multiple but unified initiatives across different major disease areas (e.g. cardiovascular, cancer) and clinical research use cases (protocol feasibility, patient identification and recruitment, clinical trial execution and serious adverse event reporting), with various local and national stakeholders across several countries and therefore under various legal frameworks. RESULTS: An initial instance of the platform has been built, providing communication, security and terminology services to the eleven participating hospitals and ten pharmaceutical companies located in seven European countries. Proof-of-concept demonstrators have been built and evaluated for the protocol feasibility and patient recruitment scenarios. The specifications of the clinical trial execution and the adverse event reporting scenarios have been documented and reviewed. CONCLUSIONS: Through a combination of a consortium that brings collectively many years of experience from previous relevant EU projects and of the global conduct of clinical trials, of an approach to ethics that engages many important stakeholders across Europe to ensure acceptability, of a robust iterative design methodology for the platform services that is anchored on requirements of an underlying Service Oriented Architecture that has been designed to be scalable and adaptable, EHR4CR could be well placed to deliver a sound, useful and well accepted pan-European solution for the reuse of hospital EHR data to support clinical research studies.

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BACKGROUND: The Tufts Center for the Study of Drug Development (Tufts CSDD) collaborated with the Clinical Data Interchange Standards Consortium (CDISC) on a joint working group study with 10 participating companies including biopharmaceutical, CROs, and eClinical technology vendors. The objective of the study was to examine current and projected use of eClinical technology and standards across respondent organizations and in clinical studies and to gather perceptions and attitudes about technology and standards adoption. METHODS: The Tufts CSDD study examined the use of eClinical technology and CDISC standards through a comprehensive survey combined with analyses of clinical study data among biopharmaceutical companies and contract research organizations. RESULTS: The results suggest increasing use of specific eClinical technology solutions and standards. The barriers to adoption of eClinical trial tools are addressed as well as the benefits of standards adoption. Differences between respondent perceptions and actual study data are examined, and the survey results are compared with those from prior studies. CONCLUSIONS: The results of the study indicate that increasing use of standards could translate into improvements in time, costs, and overall approval rates. The study also observed an uptake in the use of eClinical technologies that could potentially create efficiencies and streamline operational processes.

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The majority of vaccines are administered during childhood. Vaccination records are important documents to be kept for a lifetime, but the documentation of immunization events is poorly standardized. At the point of care, paper records are often unavailable, making it impossible to obtain accurate vaccination histories. Vaccination records should include batch specifications to allow the tracking of licensed vaccines in cases of recall. The WHO have generated the International Certificate of Vaccination or Prophylaxis for the documentation of childhood and travel vaccinations as well as seasonal and booster immunizations. When moving vaccination records into the digital age, data standards and interoperability need to be considered. The ideal vaccination record should facilitate the interpretation of safety reports and promote a data continuum from pre-licensure trials to post-marketing surveillance. The current article describes which data elements are essential, and how vaccination documentation could be streamlined and simplified.

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Semantic interoperability among terminologies, data elements, and information models is fundamental and critical for sharing information from the scientific bench to the clinical bedside and back among systems. To meet this need, the vision for CDISC is to build a global, accessible electronic library, which enables precise and standardized data element definitions that can be used in applications and studies to improve biomedical research and its link with health care. As a pilot study, we propose a representation and harmonization framework for clinical study data elements and implement a prototype CDISC Shared Health and Research Electronic Library (CSHARE) using Semantic MediaWiki. We report the preliminary observations of how the components worked and the lessons learnt. In summary, the wiki provided a useful prototyping tool from a process standpoint.

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Open global standards, workflow integration profiles, and related processes have been developed through consensus-based procedures. Although these standards and profiles should be augmented with additional therapeutic area-based content standards, they are available now to enable the use of electronic health records to directly support clinical research concurrent with patient care. Improving the exchange of information and integrating workflow between research and health care are inherently necessary to accelerate the pace of informing clinical decisions based on current research findings and for an efficient, cost-effective health care system to improve the quality of patient care.

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OBJECTIVE: Inefficiencies in clinical trial data collection cause delays, increase costs, and may reduce clinician participation in medical research. In this proof-of-concept study, we examine the feasibility of using point-of-care data capture for both the medical record and clinical research in the setting of a working clinical trial. We hypothesized that by doing so, we could increase reuse of patient data, eliminate redundant data entry, and minimize disruption to clinic workflow. DESIGN: We developed and used a point-of-care electronic data capture system to record data during patient visits. The standards-based system was used for clinical research and to generate the clinic note for the medical record. The system worked in parallel with data collection procedures already in place for an ongoing multicenter clinical trial. Our system was iteratively designed after analyzing case report forms and clinic notes, and observing clinic workflow patterns and business procedures. Existing data standards from CDISC and HL7 were used for database insertion and clinical document exchange. RESULTS: Our system was successfully integrated into the clinic environment and used in two live test cases without disrupting existing workflow. Analyses performed during system design yielded detailed information on practical issues affecting implementation of systems that automatically extract, store, and reuse healthcare data. CONCLUSION: Although subject to the limitations of a small feasibility study, our study demonstrates that electronic patient data can be reused for prospective multicenter clinical research and patient care, and demonstrates a need for further development of therapeutic area standards that can facilitate researcher use of healthcare data.

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The Clinical Data Interchange Standards Consortium (CDISC) has succeeded in developing global clinical data interchange standards that are ready for implementation. The various CDISC models are identified and explained in this article as well as how these models work together. In addition to developing the CDISC standards, CDISC is involved actively in many collaborative projects with other organizations as a result of their numerous alliances and partnerships. CDISC standards are supporting not only the pharmaceutical industry but also other initiatives and services in healthcare.

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