Semantic Integration of BPMN Models and FHIR Data to Enable Personalized Decision Support for Malignant Melanoma

Author:

Beckmann Catharina Lena1ORCID,Keuchel Daniel2,Soleman Wa Ode Iin Arliani1,Nürnberg Sylvia2ORCID,Böckmann Britta12

Affiliation:

1. Department of Computer Science, University of Applied Sciences and Arts Dortmund (FH Dortmund), 44227 Dortmund, Germany

2. Institute for Artificial Intelligence in Medicine, University Hospital Essen, Girardetstraße 2, 45131 Essen, Germany

Abstract

With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and Notation) contain evidence-based treatment guidance for all phases of a certain diagnosis, while physicians need the parts relevant to a specific patient at a specific point in the clinical process. Therefore, integration of patient data from electronic health records (EHRs) providing context to clinicians is needed, which is stored and communicated in HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources). To address this issue, we propose a method combining an integration of stored data into BPMN and a loss-free transformation from BPMN into FHIR, and vice versa. Based on that method, an identification of the next necessary decision point in a specific patient context is possible. We verified the method for treatment of malignant melanoma by using an extract of a formalized SOP document with predefined decision points and validated FHIR references with real EHR data. The patient data could be stored and integrated into the BPMN element ‘DataStoreReference’. Our loss-free transformation process therefore is the foundation for combining evidence-based knowledge from formalized clinical guidelines or SOPs and patient data from EHRs stored in FHIR. Processing the SOP with the available patient data can then lead to the next upcoming decision point, which will be displayed to the physician integrated with the corresponding data.

Funder

DFG Research Training Group 2535

WisPerMed

the University of Duisburg-Essen, Germany

Publisher

MDPI AG

Subject

Information Systems

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