Internet of Medical Things (IoMT): Acquiring and Transforming Data into HL7 FHIR through 5G Network Slicing

Argyro Mavrogiorgou (Department of Digital Systems, University of Piraeus, Piraeus, 18534,)
Athanasios Kiourtis (Department of Digital Systems, University of Piraeus, Piraeus, 18534,)
Marios Touloupou (Department of Digital Systems, University of Piraeus, Piraeus, 18534,)
Evgenia Kapassa (Department of Digital Systems, University of Piraeus, Piraeus, 18534,)
Dimosthenis Kyriazis (Department of Digital Systems, University of Piraeus, Piraeus, 18534,)

Emerging Science Journal
Vol 3, No 2 (2019): April

The Healthcare 4.0 era is surrounded by challenges varying from the Internet of Medical Things (IoMT) devices’ data collection, integration and interpretation. Several techniques have been developed that however do not propose solutions that can be applied to different scenarios or domains. When dealing with healthcare data, based on the severity and the application of their results, they should be provided almost in real-time, without any errors, inconsistencies or misunderstandings. Henceforth, in this manuscript a platform is proposed for efficiently managing healthcare data, by taking advantage of the latest techniques in Data Acquisition, 5G Network Slicing and Data Interoperability. In this platform, IoMT devices’ data and network specifications can be acquired and segmented in different 5G network slices according to the severity and the computation requirements of different medical scenarios. In sequel, transformations are performed on the data of each network slice to address data heterogeneity issues, and provide the data of the same network slices into HL7 FHIR-compliant format, for further analysis.