Leveraging Synthetic Patient Data for Process Mining in Healthcare...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy
Process mining in healthcare offers the potential to optimize workflows and ensure compliance with clinical guidelines, yet its application is often hindered by data privacy issues and restricted access to real-world patient data. This study presents a methodological infrastructure to explore the us...
| Publicado en: | Studies in Health Technology & Informatics Vol. 336; pp. 1634 - 1639 |
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| Autores principales: | , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194019136&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194019136 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2026 vid: 336 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 194019136 194019136 194019136 10.3233/SHTI260502 194019136 ppf: 1634 ppct: 5 formats: tig: atl: Leveraging Synthetic Patient Data for Process Mining in Healthcare...36th Medical Informatics Europe (MIE) Conference, May 25-28, 2026, Genoa, Italy aug: au: ADLBERGER, Selina BAUERNFEIND, Sophie VAN DEN BROEK, Mitch POINTNER, Andreas ARTHOFER, Klaus KRAUSS, Oliver affil: University of Applied Sciences Upper Austria. sug: subj: Electronic Health Records Medical Records Data Mining Health Services Medical Informatics Workflow Data Quality Congresses and Conferences Italy Italy Human Audit Data Analysis, Statistical Models, Theoretical Methodological Research Artificial Intelligence Simulations Databases Privacy and Confidentiality Electronic Data Interchange Software Algorithms Funding Source ab: Process mining in healthcare offers the potential to optimize workflows and ensure compliance with clinical guidelines, yet its application is often hindered by data privacy issues and restricted access to real-world patient data. This study presents a methodological infrastructure to explore the use of synthetic patient data, generated with Synthea, to advance healthcare process mining. By integrating HL7 FHIR AuditEvent Resources, we establish standardized audit logs that capture healthcare processes while maintaining interoperability. These logs are then converted into process mining formats such as XES and OCEL, enabling analysis with established process mining techniques. The evaluation was performed using FHIR Resources generated from four Synthea modules, each representing the treatment course of a distinct disease. It demonstrates the accuracy of process reconstruction and the influence of data volume. Despite encountering challenges like missing events and the limitations of FHIR R4 AuditEvent, our approach shows the capabilities of process reconstruction using synthetic healthcare data. Future work will apply this methodology to real-world scenarios to further validate its practical effectiveness in healthcare settings. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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