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    <IdentifierUrn>urn:nbn:de:0183-25gmds0521</IdentifierUrn>
    <ArticleType>Meeting Abstract</ArticleType>
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      <Title language="en">Enhanced FHIR Services Module: MII-CDS Conformance and Advanced Features for REDCap-FHIR Integration</Title>
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          <Lastname>Ahlendorf</Lastname>
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          <Affiliation>Universit&#228;tsklinikum Jena, Jena, Germany</Affiliation>
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          <Affiliation>Universit&#228;tsklinikum Jena, Jena, Germany</Affiliation>
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          <Affiliation>Universit&#228;tsklinikum Jena, Jena, Germany</Affiliation>
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          <Corporatename>German Medical Science GMS Publishing House</Corporatename>
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        <Address>D&#252;sseldorf</Address>
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    <SubjectGroup>
      <SubjectheadingDDB>610</SubjectheadingDDB>
      <Keyword language="en">electronic data capture</Keyword>
      <Keyword language="en">REDCap</Keyword>
      <Keyword language="en">HL7 FHIR</Keyword>
      <Keyword language="en">MII Core Data Set</Keyword>
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    <DatePublishedList>
      <DatePublished>20251103</DatePublished>
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    <Language>engl</Language>
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      <AltText language="de">Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung).</AltText>
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      <Meeting>
        <MeetingId>M0631</MeetingId>
        <MeetingSequence>052</MeetingSequence>
        <MeetingCorporation>Deutsche Gesellschaft f&#252;r Medizinische Informatik, Biometrie und Epidemiologie</MeetingCorporation>
        <MeetingName>70. Jahrestagung der Deutschen Gesellschaft f&#252;r Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)</MeetingName>
        <MeetingTitle></MeetingTitle>
        <MeetingSession>V: Interoperability and standards 1</MeetingSession>
        <MeetingCity>Jena</MeetingCity>
        <MeetingDate>
          <DateFrom>20250907</DateFrom>
          <DateTo>20250911</DateTo>
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    <ArticleNo>Abstr. 99</ArticleNo>
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      <MainHeadline>Text</MainHeadline><Pgraph><Mark1>Introduction:</Mark1> At the Data Integration Center of the Jena University Hospital, we established a hybrid data collection system <TextLink reference="1"></TextLink> using the electronic data capture (EDC) platform REDCap <TextLink reference="2"></TextLink>. To facilitate interoperability of additionally collected data, our pipeline transforms structured EDC content into HL7 Fast Healthcare Interoperability Resources (FHIR), ensuring conformance with the Medical Informatics Initiative Core Data Set (MII-CDS) <TextLink reference="3"></TextLink>. </Pgraph><Pgraph><Mark1>Methods:</Mark1> We enhanced the existing FHIR Services Module (FSM) <TextLink reference="4"></TextLink> to transform REDCap data directly into FHIR resources conformant to the MII-CDS. Our annotation framework maps REDCap field types to appropriate FHIR resources through a semantic mapping system that interprets the REDCap Data Dictionary, analysing field types, validation rules, and branching logic. Record Linkage was implemented through the use of a master patient ID. The system applies MII-CDS profiles and extensions, packaging data as FHIR Bundle resources with metadata for traceability and provenance.</Pgraph><Pgraph>For evaluation, we used an ongoing in-house study with 974 records and about 50 items comparing venous and capillary glucose values during pregnancy. We validated the generated FHIR Bundles for adherence to FHIR specification, and MII-CDS conformance. We assessed the transformation quality by verifying conformance between newly generated resources and existing FHIR server data, ensuring interoperability and standardized representation of clinical parameters. The module implements selective resource generation, omitting empty or null data elements.</Pgraph><Pgraph><Mark1>Results: T</Mark1>he enhanced FSM successfully converts manually captured data from REDCap into FHIR resources that are conformant to MII-CDS profiles: When processing a test case from the in-house study, the enhanced FSM automatically generates a FHIR Bundle containing about 40 resources. Included are structured patient demographics, time-stamped observations for glucose measurements, related encounter data, and provenance tracking through resource metadata. This ensures adherence to standardized terminologies and value sets. The system facilitates seamless data integration between research and clinical care systems, enables validation through a FHIR server, and integrates smoothly into clinical workflows.</Pgraph><Pgraph><Mark1>Discussion: </Mark1>Our FSM demonstrated reliable transformation of REDCap data into MII-CDS-conformant FHIR resources, achieving interoperability within our clinical and research environment. Real world application of the updated FSM in the use case confirmed its capability to convert the clinical data from REDCap into FHIR resources while maintaining MII-CDS profile conformance and provenance tracking. Future developments will expand dynamic linking between FHIR resources and clinical data, implement validation frameworks, and may integrate MIOs (e.g. Mutterpass) <TextLink reference="5"></TextLink>. By efficiently handling real world data transformations and meeting healthcare standards, the system significantly improves clinical data standardization for research and data exchange.</Pgraph><Pgraph><Mark1>Conclusion: </Mark1>In our university hospital setting, the enhanced FSM successfully connects REDCap with clinical systems while supporting hybrid data collection strategies and ensuring MII-CDS conformance. The module&#8217;s ability to transform captured clinical data from REDCap into standardized FHIR resources establishes a strong foundation for interoperable clinical data exchange. This advancement enhances the efficiency of data sharing within medical research contexts.</Pgraph><Pgraph>The authors declare that they have no competing interests.</Pgraph><Pgraph>The authors declare that an ethics committee vote is not required.</Pgraph></TextBlock>
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      <Reference refNo="1">
        <RefAuthor>Sai N</RefAuthor>
        <RefAuthor>Ahlendorf A</RefAuthor>
        <RefAuthor>Ammon D</RefAuthor>
        <RefAuthor></RefAuthor>
        <RefTitle>Hybrid data collection: Generating profile-conform FHIR from electronic data capture to improve interoperability</RefTitle>
        <RefYear>2024</RefYear>
        <RefBookTitle>Gesundheit &#8211; gemeinsam. Kooperationstagung der Deutschen Gesellschaft f&#252;r Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft f&#252;r Sozialmedizin und Pr&#228;vention (DGSMP), Deutschen Gesellschaft f&#252;r Epidemiologie (DGEpi), Deutschen Gesellschaft f&#252;r Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft f&#252;r Public Health (DGPH). Dresden, 08.-13.09.2024</RefBookTitle>
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        <RefTotal>Sai N, Ahlendorf A, Ammon D, et al. Hybrid data collection: Generating profile-conform FHIR from electronic data capture to improve interoperability. In: Gesundheit &#8211; gemeinsam. Kooperationstagung der Deutschen Gesellschaft f&#252;r Medizinische Informatik, Biometrie und Epidemiologie (GMDS), Deutschen Gesellschaft f&#252;r Sozialmedizin und Pr&#228;vention (DGSMP), Deutschen Gesellschaft f&#252;r Epidemiologie (DGEpi), Deutschen Gesellschaft f&#252;r Medizinische Soziologie (DGMS) und der Deutschen Gesellschaft f&#252;r Public Health (DGPH). Dresden, 08.-13.09.2024. D&#252;sseldorf: German Medical Science GMS Publishing House; 2024. DocAbstr. 699. DOI: 10.3205&#47;24gmds163</RefTotal>
        <RefLink>https:&#47;&#47;doi.org&#47;10.3205&#47;24gmds163</RefLink>
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      <Reference refNo="2">
        <RefAuthor>Vanderbilt University</RefAuthor>
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        <RefLink>http:&#47;&#47;dx.doi.org&#47;10.1007&#47;s00103-024-03888-4</RefLink>
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        <RefAuthor>Vanderbilt REDCap Group</RefAuthor>
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