Abstract :
[en] A medical entity (hospital, nursing home, rest home, revalidation center, etc.) usually includes a multitude of information systems that allow for quick decision-making close to the medical sensors. The Internet of Medical Things (IoMT) is an area of IoT that generates a lot of data of different natures (radio, CT scan, medical reports, medical sensor data). However, these systems need to share and exchange medical information in a seamless, timely, and efficient manner with systems that are either within the same entity or other healthcare entities. The lack of inter- and intra-entity interoperability causes major problems in the analysis of patient records and leads to additional financial costs (e.g., redone examinations). To develop a medical data interoperability architecture model that will allow providers and different actors in the medical community to exchange patient summary information with other caregivers and partners to improve the quality of care, the level of data security, and the efficiency of care should take stock of the state of knowledge. This paper discusses the challenges faced by medical entities in sharing and exchanging medical information seamlessly and efficiently. It highlights the need for inter- and intra-entity interoperability to improve the analysis of patient records, reduce financial costs, and enhance the quality of care. The paper reviews existing solutions proposed by various researchers and identifies their limitations. The analysis of the literature has shown that the HL7 FHIR standard is particularly well adapted for exchanging and storing health data, while DICOM, CDA, and JSON can be converted in HL7 FHIR or HL7 FHIR to these formats for interoperability purposes. This approach covers almost all use cases.
Funding text :
This research was partially funded by ARES, and Infortech and Numediart research institutes. The APC was funded by MDPI Information.Costa et al. [] developed methods for transforming data instances between the ISO 13606 and openEHR standards, which are important standards for electronic health record (EHR) systems. The transformation process includes both archetype transformation and data transformation. The research results indicate that the exchange and sharing of clinical information between these standards are possible. The authors believe that their approach could be applied to other dual model standards and even to other domains beyond healthcare. The use of ontologies and metamodels in their technological framework has facilitated semantic interoperability. However, the researchers acknowledge that their solution is not perfect and that further research is needed, particularly in integrating terminological knowledge to enhance the semantic aspects of the transformation process. The work has been supported by grants from the Spanish Ministry for Science and Education.
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