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      Data interchange using i2b2

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports data network interoperability through an ontology-driven approach. We use i2b2 as a hub, to rapidly reconfigure data to meet new analytical requirements without new ETL programming.

          Materials and Methods Our 12-site National Patient-Centered Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses i2b2 to query data. We developed a process to generate a PCORnet Common Data Model (CDM) physical database directly from existing i2b2 systems, thereby supporting PCORnet analytic queries without new ETL programming. This involved: a formalized process for representing i2b2 information models (the specification of data types and formats); an information model that represents CDM Version 1.0; and a program that generates CDM tables, driven by this information model. This approach is generalizable to any logical information model.

          Results Eight PCORnet CDRN sites have implemented this approach and generated a CDM database without a new ETL process from the EHR. This enables federated querying within the CDRN and compatibility with the national PCORnet Distributed Research Network.

          Discussion We have established a way to adapt i2b2 to new information models without requiring changes to the underlying data. Eight Scalable Collaborative Infrastructure for a Learning Health System sites vetted this methodology, resulting in a network that, at present, supports research on 10 million patients’ data.

          Conclusion New analytical requirements can be quickly and cost-effectively supported by i2b2 without creating new data extraction processes from the EHR.

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          Validation of a common data model for active safety surveillance research.

          Systematic analysis of observational medical databases for active safety surveillance is hindered by the variation in data models and coding systems. Data analysts often find robust clinical data models difficult to understand and ill suited to support their analytic approaches. Further, some models do not facilitate the computations required for systematic analysis across many interventions and outcomes for large datasets. Translating the data from these idiosyncratic data models to a common data model (CDM) could facilitate both the analysts' understanding and the suitability for large-scale systematic analysis. In addition to facilitating analysis, a suitable CDM has to faithfully represent the source observational database. Before beginning to use the Observational Medical Outcomes Partnership (OMOP) CDM and a related dictionary of standardized terminologies for a study of large-scale systematic active safety surveillance, the authors validated the model's suitability for this use by example. To validate the OMOP CDM, the model was instantiated into a relational database, data from 10 different observational healthcare databases were loaded into separate instances, a comprehensive array of analytic methods that operate on the data model was created, and these methods were executed against the databases to measure performance. There was acceptable representation of the data from 10 observational databases in the OMOP CDM using the standardized terminologies selected, and a range of analytic methods was developed and executed with sufficient performance to be useful for active safety surveillance.
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            PCORnet: turning a dream into reality

            Many modern physician specialists like to think of their work as grounded in strong science. Yet 5 years ago, a group of cardiologists published their findings on the science underlying over 2700 practice recommendations issued by their specialty societies.1 Only 314 (or 11%) were based on ‘level A’ evidence, that is, evidence based on multiple well-done randomized trials. Nearly half of the recommendations were based solely on ‘expert opinion.’ Even more disconcerting is the fact that despite the activities of many researchers, the vast majority of ongoing clinical trials are too small to provide evidence relevant to patients and clinicians.2 Robust trials that can support recommendations grounded in solid science are few and far between, in part because they have become too expensive and complicated to run. Our biomedical enterprise is conducting many clinical trials, yet we may not be getting all that much for what we spend.3 No wonder that too often, patients and caregivers seeking information on how best to improve their health or the health of their loved ones find that biomedicine does not have answers for questions they ask. Too often, clinicians cannot tell patients which therapies are likely to work best for individuals like them. Too often risks, benefits, and impact on quality of life are uncertain. Providing accurate answers based on the highest levels of scientific evidence for the majority of unresolved clinical questions is a revolutionary dream shared by patients, providers, payers, health plans, researchers, and policy makers alike. PCORnet, the National Patient-Centered Clinical Research Network, promises a transformative platform that will turn this revolutionary dream into reality.4 The Patient-Centered Outcomes Research Institute (PCORI) vision begins with patient-centeredness, which is fundamental to PCORnet's structure and aspirations. This model promises to engage patients intimately in the prioritization, design, and conduct of research, a collaboration that will help researchers develop more accurate and meaningful information for patients, their caregivers, and clinicians—information that will allow our healthcare systems to achieve the best possible clinical outcomes, and the outcomes that matter most to patients. PCORnet aims to build a national research network, linked by a common data platform and embedded in clinical care delivery systems. This network will enable studies, and in particular randomized trials, that have been impractical to conduct to date—and do so with economies of scale. For patients, participating in research will be simplified: studies will be easier to locate and privacy protections will be consistent. Participants will be true partners in the research. For researchers, a network of electronic medical records representing over 100 million covered lives will make large-scale observational and interventional trials faster to launch, more representative of diverse real world populations, and capable of providing much-needed answers to comparative effectiveness research questions with greater accuracy. Just a few examples of the kinds of questions that can be asked and answered using the PCORnet platform should excite anyone interested in improving healthcare: What are the best management strategies for localized prostate cancer? Which of the available primary care treatment strategies for children with attention deficit hyperactivity disorder are most effective? What are the best treatment strategies for low back pain? Which interventions are most effective for reducing disparities in hypertension outcomes? For years, many of us have dreamed about what would be possible if we had the research infrastructure to conduct large cohort studies to understand genetic, behavioral, social, and environmental factors that contribute to health and illness,5 and to implement large randomized trials at affordable cost, to drive continuous improvement in the standards of best clinical care. PCORnet can provide these capabilities, and do so in a real world setting. As a result, clinicians and healthcare systems will benefit from an improved evidence base for their practices and recommendations, getting the most effective therapies into the hands of patients and improving healthcare delivery. Furthermore, the timetable for translation of research results into improved care can be significantly shortened since the network carrying out the research is actually responsible for the care of almost one third of Americans. Under the leadership of PCORI's Executive Director, Joe Selby and the PCORI Board of Governors, on which one of us (FSC) serves, the PCORnet initiative has been launched. PCORI designed the initiative's Phase I details and issued PCORI funding announcements for establishing the coordinating center, clinical data research networks (CDRNs), and patient-powered research networks (PPRNs). The Coordinating Center, announced in September 2013, worked quickly to kick start planning with the PCORnet Steering Committee, on which two of us (JPB and KLH) participate, setting the stage for the arrival of the CDRNs and PPRNs. Diverse panels of reviewers carefully evaluated 28 CDRN and 61 PPRN applications, recommending 11 CDRNs and 18 PPRNs for funding. The announcement of the CDRNs and PPRNs in December 2013 launched a flurry of activity to finalize this innovative network's governance and address fundamental challenges to achieving a functioning distributed research network within the next 18 months. There are still many challenges to be met in order for PCORnet to reach its ambitious goal of launching a simple pragmatic clinical trial by September 2015. PCORnet is establishing the fundamental data architecture and data standards (eg, the adoption of a data model) in a manner that will facilitate rapid implementation while leaving room for other approaches to grow and mature. Implementation will require incorporation of patient-generated outcomes, interoperable methods to query the electronic medical record, and common standards for biospecimens. Simultaneously, PCORnet is addressing key policy questions concerning the responsible conduct of research in this new environment, including informed consent, the use of central institutional review boards, and the protection of patient privacy. By addressing these and other major challenges, PCORnet has the potential to improve the conduct of, and provide guidance for, large pragmatic trials conducted within PCORnet and beyond. Then the real work begins: conducting research. Building on the work of the PCORI Methodology Committee,6 on which one of us (MSL) sits, the research conducted through PCORnet will strengthen the research community's understanding of, and capacity for, patient-centered research, including a heavy emphasis on randomized trials that focus on patient-centered outcomes. The network will be faced with the challenging yet exciting prospect of identifying the highest priority projects for the PCORnet platform to take on. Other organizations will be invited into the tent and researchers not directly affiliated with PCORnet will be able to conduct research in the network through collaborations. Ultimately, if this platform is able to demonstrate its value and revolutionary potential, PCORnet will face the test of sustainability: it will need to establish partners that wish to use this platform for research, and develop self-sustaining sources of support in a complex and challenging budgetary environment. Speaking as one such potential partner, the National Institutes of Health is eager to support studies that will be conducted in PCORnet and to develop initiatives that we hope will be among the first to make use of this important resource. PCORnet holds the promise to transform clinical research—but many challenges lie ahead. For ultimate success, all those involved in shaping this revolutionary dream must maintain the bold and visionary attitude that enabled its creation. This is not your father's clinical trial network.
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              SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies

              Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the ‘Shared Health Research Information Network’, with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.
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                Author and article information

                Journal
                J Am Med Inform Assoc
                J Am Med Inform Assoc
                jamia
                jaminfo
                Journal of the American Medical Informatics Association : JAMIA
                Oxford University Press
                1067-5027
                1527-974X
                September 2016
                05 February 2016
                : 23
                : 5
                : 909-915
                Affiliations
                1Partners Healthcare, Boston, MA, USA
                2Harvard Medical School, Boston, MA, USA
                3Massachusetts General Hospital, Boston, MA, USA
                4The Autoimmune Registry, New York, NY, USA
                5Boston Children’s Hospital, Boston, MA, USA
                Author notes
                Correspondence to Jeffrey G. Klann, PhD, Research Computing, Partners Healthcare System, Inc., One Constitution Center, Charlestown, MA 02129, USA; jeff.klann@ 123456mgh.harvard.edu ; Tel: 617-643-5879; Fax: 617-643-5280.
                Article
                ocv188
                10.1093/jamia/ocv188
                4997035
                26911824
                4aa2e186-0c13-4249-aba1-a2c0fd8dbc82
                © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.

                History
                : 31 July 2015
                : 26 October 2015
                : 31 October 2015
                Page count
                Pages: 7
                Categories
                Research and Applications

                Bioinformatics & Computational biology
                medical informatics,data integration,data models,ontology-driven data representation,patient centered outcomes research institute,pcornet cdm,informatics for integrating biology and the bedside

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