islEHR, a model for electronic health records interoperability

• Autorzy: Najjar Arwa , Amro Belal , Macedo Mário

Identyfikatory

DOI

10.1515/bams-2021-0117

• Języki publikacji: EN

Abstrakty

EN

Objectives: Due to the diversity, volume, and distribution of ingested data, the majority of current healthcare entities operate independently, increasing the problem of data processing and interchange. The goal of this research is to design, implement, and evaluate an electronic health record (EHR) interoperability solution - prototype - among healthcare organizations, whether these organizations do not have systems that are prepared for data sharing, or organizations that have such systems. Methods: We established an EHR interoperability prototype model named interoperability smart lane for electronic health record (islEHR), which comprises of three modules: 1) a data fetching APIs for external sharing of patients’ information from participant hospitals; 2) a data integration service, which is the heart of the islEHR that is responsible for extracting, standardizing, and normalizing EHRs data leveraging the fast healthcare interoperability resources (FHIR) and artificial intelligence techniques; 3) a RESTful API that represents the gateway sits between clients and the data integration services. Results: The prototype of the islEHR was evaluated on a set of unstructured discharge reports. The performance achieved a total time of execution ranging from 0.04 to 84.49 s. While the accuracy reached an F-Score ranging from 1.0 to 0.89. Conclusions: According to the results achieved, the islEHR prototype can be implemented among different heterogeneous systems regardless of their ability to share data. The prototype was built based on international standards and machine learning techniques that are adopted worldwide. Performance and correctness results showed that islEHR outperforms existing models in its diversity as well as correctness and performance.

Słowa kluczowe

EN

electronic health record; fast healthcare interoperability resources; interoperability; machine learning; natural language processing

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2022
• Tom: Vol. 18, no. 1
• Strony: 39--54
• Opis fizyczny: Bibliogr. 35 poz., rys., tab.

Twórcy

autor

Najjar Arwa

• Information Technology College, Hebron University, Hebron, Palestine

autor

Amro Belal

• Information Technology College, Hebron University, Hebron, Palestine

autor

Macedo Mário

• Sciences and Technologies of Information and Communication College, Atlântica University, Lisbon, Portugal

Bibliografia

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Uwagi

Opublikowane przez De Gruyter. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).