{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T14:31:47Z","timestamp":1658241107105},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,24]]},"abstract":"<jats:p>Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.<\/jats:p>","DOI":"10.3233\/shti210061","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:07:04Z","timestamp":1622117224000},"source":"Crossref","is-referenced-by-count":3,"title":["Feasibility Queries in Distributed Architectures \u2013 Concept and Implementation in HiGHmed"],"prefix":"10.3233","author":[{"given":"Reto","family":"Wettstein","sequence":"first","affiliation":[{"name":"Department Medical Information Systems, Heidelberg University Hospital, Germany"}]},{"given":"Hauke","family":"Hund","sequence":"additional","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]},{"given":"Insa","family":"Kobylinski","sequence":"additional","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]},{"given":"Christian","family":"Fegeler","sequence":"additional","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]},{"given":"Oliver","family":"Heinze","sequence":"additional","affiliation":[{"name":"Department Medical Information Systems, Heidelberg University Hospital, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences: Bringing Data to Life"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210061","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:09:46Z","timestamp":1635167386000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210061"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210061","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]}}}