[{"publication_identifier":{"issn":["0926-9630"],"eissn":["1879-8365"]},"volume":247,"pmid":"1","intvolume":"       247","external_id":{"pmid":["29677955"]},"title":"Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR.","status":"public","publication":"Studies in health technology and informatics","publisher":" IOS Press","author":[{"first_name":"AK","full_name":"Kock-Schoppenhauer, AK","last_name":"Kock-Schoppenhauer"},{"full_name":"Ulrich, H","first_name":"H","last_name":"Ulrich"},{"last_name":"Wagen-Zink","first_name":"S","full_name":"Wagen-Zink, S"},{"last_name":"Duhm-Harbeck","first_name":"P","full_name":"Duhm-Harbeck, P"},{"full_name":"Ingenerf, J","first_name":"J","last_name":"Ingenerf"},{"first_name":"P","full_name":"Neuhaus, P","last_name":"Neuhaus"},{"last_name":"Dugas","first_name":"M","full_name":"Dugas, M"},{"id":"75847","full_name":"Bruland, Philipp","first_name":"Philipp","last_name":"Bruland","orcid":"0000-0001-6939-7630"}],"publication_status":"published","abstract":[{"text":"The establishment of a digital healthcare system is a national and community task. The Federal Ministry of Education and Research in Germany is providing funding for consortia consisting of university hospitals among others participating in the \"Medical Informatics Initiative\". Exchange of medical data between research institutions necessitates a place where meta information for this data is made accessible. Within these consortia different metadata registry solutions were chosen. To promote interoperability between these solutions, we have examined whether the portal of Medical Data Models is eligible for managing and communicating metadata and relevant information across different data integration centres of the Medical Informatics Initiative and beyond. Apart from the MDM-portal, some ISO 11179-based systems such as Samply.MDR as well as openEHR-based solutions are going to be applyed. In this paper, we have focused on the creation of a mapping model between the CDISC ODM standard and the Samply.MDR import format. In summary, it can be stated that the mapping model is feasible and promote the exchangeability between different metadata registry approaches.","lang":"eng"}],"year":"2018","citation":{"havard":"A. Kock-Schoppenhauer, H. Ulrich, S. Wagen-Zink, P. Duhm-Harbeck, J. Ingenerf, P. Neuhaus, M. Dugas, P. Bruland, Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR., Studies in Health Technology and Informatics. 247 (2018) 221–225.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Kock-Schoppenhauer, AK</span> ; <span style=\"font-variant:small-caps;\">Ulrich, H</span> ; <span style=\"font-variant:small-caps;\">Wagen-Zink, S</span> ; <span style=\"font-variant:small-caps;\">Duhm-Harbeck, P</span> ; <span style=\"font-variant:small-caps;\">Ingenerf, J</span> ; <span style=\"font-variant:small-caps;\">Neuhaus, P</span> ; <span style=\"font-variant:small-caps;\">Dugas, M</span> ; <span style=\"font-variant:small-caps;\">Bruland, Philipp</span>: Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. In: <i>Studies in health technology and informatics</i> Bd. 247,  IOS Press (2018), S. 221–225","chicago-de":"Kock-Schoppenhauer, AK, H Ulrich, S Wagen-Zink, P Duhm-Harbeck, J Ingenerf, P Neuhaus, M Dugas und Philipp Bruland. 2018. Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. <i>Studies in health technology and informatics</i> 247: 221–225.","bjps":"<b>Kock-Schoppenhauer A <i>et al.</i></b> (2018) Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. <i>Studies in health technology and informatics</i> <b>247</b>, 221–225.","ufg":"<b>Kock-Schoppenhauer, AK u. a.</b>: Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR., in: <i>Studies in health technology and informatics</i> 247 (2018),  S. 221–225.","apa":"Kock-Schoppenhauer, A., Ulrich, H., Wagen-Zink, S., Duhm-Harbeck, P., Ingenerf, J., Neuhaus, P., Dugas, M., &#38; Bruland, P. (2018). Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. <i>Studies in Health Technology and Informatics</i>, <i>247</i>, 221–225.","mla":"Kock-Schoppenhauer, AK, et al. “Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR.” <i>Studies in Health Technology and Informatics</i>, vol. 247, 2018, pp. 221–25.","chicago":"Kock-Schoppenhauer, AK, H Ulrich, S Wagen-Zink, P Duhm-Harbeck, J Ingenerf, P Neuhaus, M Dugas, and Philipp Bruland. “Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR.” <i>Studies in Health Technology and Informatics</i> 247 (2018): 221–25.","ama":"Kock-Schoppenhauer A, Ulrich H, Wagen-Zink S, et al. Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. <i>Studies in health technology and informatics</i>. 2018;247:221-225.","van":"Kock-Schoppenhauer A, Ulrich H, Wagen-Zink S, Duhm-Harbeck P, Ingenerf J, Neuhaus P, et al. Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR. Studies in health technology and informatics. 2018;247:221–5.","ieee":"A. Kock-Schoppenhauer <i>et al.</i>, “Compatibility Between Metadata Standards: Import Pipeline of CDISC ODM to the Samply.MDR.,” <i>Studies in health technology and informatics</i>, vol. 247, pp. 221–225, 2018.","short":"A. Kock-Schoppenhauer, H. Ulrich, S. Wagen-Zink, P. Duhm-Harbeck, J. Ingenerf, P. Neuhaus, M. Dugas, P. Bruland, Studies in Health Technology and Informatics 247 (2018) 221–225."},"type":"journal_article","user_id":"83781","date_updated":"2024-07-18T13:40:32Z","department":[{"_id":"DEP5024"}],"date_created":"2024-06-24T07:36:34Z","keyword":["CDISC ODM","MDR","data elements","mapping","metadata registry"],"page":"221-225","language":[{"iso":"eng"}],"_id":"11577"},{"publication_identifier":{"eissn":["1879-8365"],"issn":["0926-9630"]},"intvolume":"       245","external_id":{"pmid":["29295106"]},"volume":245,"pmid":"1","title":"What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.","status":"public","publication":"Studies in health technology and informatics","author":[{"id":"75847","full_name":"Bruland, Philipp","first_name":"Philipp","last_name":"Bruland","orcid":"0000-0001-6939-7630"},{"last_name":"Doods","full_name":"Doods, J","first_name":"J"},{"last_name":"Storck","full_name":"Storck, M","first_name":"M"},{"last_name":"Dugas","first_name":"M","full_name":"Dugas, M"}],"publication_status":"published","abstract":[{"lang":"eng","text":"Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses."}],"year":"2017","citation":{"ama":"Bruland P, Doods J, Storck M, Dugas M. What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. <i>Studies in health technology and informatics</i>. 2017;245:313-317.","mla":"Bruland, Philipp, et al. “What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.” <i>Studies in Health Technology and Informatics</i>, vol. 245, 2017, pp. 313–17.","chicago":"Bruland, Philipp, J Doods, M Storck, and M Dugas. “What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.” <i>Studies in Health Technology and Informatics</i> 245 (2017): 313–17.","ieee":"P. Bruland, J. Doods, M. Storck, and M. Dugas, “What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.,” <i>Studies in health technology and informatics</i>, vol. 245, pp. 313–317, 2017.","van":"Bruland P, Doods J, Storck M, Dugas M. What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. Studies in health technology and informatics. 2017;245:313–7.","short":"P. Bruland, J. Doods, M. Storck, M. Dugas, Studies in Health Technology and Informatics 245 (2017) 313–317.","havard":"P. Bruland, J. Doods, M. Storck, M. Dugas, What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks., Studies in Health Technology and Informatics. 245 (2017) 313–317.","din1505-2-1":"<span style=\"font-variant:small-caps;\">Bruland, Philipp</span> ; <span style=\"font-variant:small-caps;\">Doods, J</span> ; <span style=\"font-variant:small-caps;\">Storck, M</span> ; <span style=\"font-variant:small-caps;\">Dugas, M</span>: What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. In: <i>Studies in health technology and informatics</i> Bd. 245 (2017), S. 313–317","chicago-de":"Bruland, Philipp, J Doods, M Storck und M Dugas. 2017. What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. <i>Studies in health technology and informatics</i> 245: 313–317.","ufg":"<b>Bruland, Philipp u. a.</b>: What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks., in: <i>Studies in health technology and informatics</i> 245 (2017),  S. 313–317.","bjps":"<b>Bruland P <i>et al.</i></b> (2017) What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. <i>Studies in health technology and informatics</i> <b>245</b>, 313–317.","apa":"Bruland, P., Doods, J., Storck, M., &#38; Dugas, M. (2017). What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks. <i>Studies in Health Technology and Informatics</i>, <i>245</i>, 313–317."},"type":"journal_article","user_id":"83781","date_created":"2024-06-24T07:36:24Z","department":[{"_id":"DEP5024"}],"date_updated":"2024-07-18T13:42:49Z","keyword":["Information Systems","Metadata","Semantics"],"page":"313-317","extern":"1","language":[{"iso":"eng"}],"_id":"11576"},{"date_updated":"2024-07-18T13:33:42Z","date_created":"2024-07-18T13:31:36Z","department":[{"_id":"DEP5024"}],"user_id":"83781","doi":"10.1186/s12874-016-0259-3","year":"2016","citation":{"havard":"P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin, T. Ganslandt, J. Doods, M. Dugas, Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting, BMC Medical Research Methodology. 16 (2016).","din1505-2-1":"<span style=\"font-variant:small-caps;\"><span style=\"font-variant:small-caps;\">Bruland, Philipp</span> ; <span style=\"font-variant:small-caps;\">McGilchrist, Mark</span> ; <span style=\"font-variant:small-caps;\">Zapletal, Eric</span> ; <span style=\"font-variant:small-caps;\">Acosta, Dionisio</span> ; <span style=\"font-variant:small-caps;\">Proeve, Johann</span> ; <span style=\"font-variant:small-caps;\">Askin, Scott</span> ; <span style=\"font-variant:small-caps;\">Ganslandt, Thomas</span> ; <span style=\"font-variant:small-caps;\">Doods, Justin</span> ; u. a.</span>: Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. In: <i>BMC Medical Research Methodology</i> Bd. 16. London, Springer Science and Business Media LLC (2016), Nr. 1","ufg":"<b>Bruland, Philipp u. a.</b>: Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting, in: <i>BMC Medical Research Methodology</i> 16 (2016), H. 1.","chicago-de":"Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta, Johann Proeve, Scott Askin, Thomas Ganslandt, Justin Doods und Martin Dugas. 2016. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i> 16, Nr. 1. doi:<a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>, .","bjps":"<b>Bruland P <i>et al.</i></b> (2016) Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting. <i>BMC Medical Research Methodology</i> <b>16</b>.","apa":"Bruland, P., McGilchrist, M., Zapletal, E., Acosta, D., Proeve, J., Askin, S., Ganslandt, T., Doods, J., &#38; Dugas, M. (2016). Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i>, <i>16</i>(1), Article 159. <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>","chicago":"Bruland, Philipp, Mark McGilchrist, Eric Zapletal, Dionisio Acosta, Johann Proeve, Scott Askin, Thomas Ganslandt, Justin Doods, and Martin Dugas. “Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting.” <i>BMC Medical Research Methodology</i> 16, no. 1 (2016). <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>.","mla":"Bruland, Philipp, et al. “Common Data Elements for Secondary Use of Electronic Health Record Data for Clinical Trial Execution and Serious Adverse Event Reporting.” <i>BMC Medical Research Methodology</i>, vol. 16, no. 1, 159, 2016, <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">https://doi.org/10.1186/s12874-016-0259-3</a>.","ama":"Bruland P, McGilchrist M, Zapletal E, et al. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. <i>BMC Medical Research Methodology</i>. 2016;16(1). doi:<a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>","van":"Bruland P, McGilchrist M, Zapletal E, Acosta D, Proeve J, Askin S, et al. Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting. BMC Medical Research Methodology. 2016;16(1).","ieee":"P. Bruland <i>et al.</i>, “Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting,” <i>BMC Medical Research Methodology</i>, vol. 16, no. 1, Art. no. 159, 2016, doi: <a href=\"https://doi.org/10.1186/s12874-016-0259-3\">10.1186/s12874-016-0259-3</a>.","short":"P. Bruland, M. McGilchrist, E. Zapletal, D. Acosta, J. Proeve, S. Askin, T. Ganslandt, J. Doods, M. Dugas, BMC Medical Research Methodology 16 (2016)."},"type":"scientific_journal_article","place":"London","_id":"11745","language":[{"iso":"eng"}],"extern":"1","article_number":"159","keyword":["Clinical trials","Common data elements","Data quality","Electronic health records","Metadata","Secondary use"],"title":"Common data elements for secondary use of electronic health record data for clinical trial execution and serious adverse event reporting","publication_identifier":{"issn":["1471-2288"]},"volume":16,"intvolume":"        16","author":[{"id":"75847","first_name":"Philipp","full_name":"Bruland, Philipp","orcid":"0000-0001-6939-7630","last_name":"Bruland"},{"full_name":"McGilchrist, Mark","first_name":"Mark","last_name":"McGilchrist"},{"last_name":"Zapletal","full_name":"Zapletal, Eric","first_name":"Eric"},{"last_name":"Acosta","first_name":"Dionisio","full_name":"Acosta, Dionisio"},{"last_name":"Proeve","full_name":"Proeve, Johann","first_name":"Johann"},{"first_name":"Scott","full_name":"Askin, Scott","last_name":"Askin"},{"full_name":"Ganslandt, Thomas","first_name":"Thomas","last_name":"Ganslandt"},{"full_name":"Doods, Justin","first_name":"Justin","last_name":"Doods"},{"first_name":"Martin","full_name":"Dugas, Martin","last_name":"Dugas"}],"publisher":"Springer Science and Business Media LLC","abstract":[{"text":"Background: Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems.\r\n\r\nMethods: Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project.\r\n\r\nResults: The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records.\r\n\r\nConclusions: Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.","lang":"eng"}],"publication_status":"published","status":"public","issue":"1","publication":"BMC Medical Research Methodology"}]