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.

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The task of finding the best job candidates among a set of applicants is both time and resource-consuming, especially when there are lots of applications. In this concern, the development of a decision support system represents a promising solution to support recruiters and facilitate their job. In this paper, we present an intelligent decision support system named I-Recruiter, that ranks applicants according to the semantic similarity between their resumes and job descriptions; the ranking process is based on machine learning and natural language processing techniques. I-Recruiter is composed of three sequentially connected blocks namely 1) Training block: which is responsible for training the model from a set of resumes, 2) Matching block: that is responsible for matching the resumes to the corresponding job description, and 3) Extracting block: that is responsible for extracting the top n ranked candidates. Experimental results for accuracy and performance showed that I-recruiter is capable of doing the job with high confidence and excellent performance. Povzetek: Predlagan je inteligentni sistem za podporo odločanju (IDSS) za pregledovanje in razvrščanje življenjepisov prosilcev na podlagi strojnega učenja in obdelave naravnih jezikov..