Arwa Najjar*, Belal Amro and Mário Macedo islEHR, a model for electronic health records interoperability https://doi.org/10.1515/bams-2021-0117 Received August 11, 2021; accepted February 18, 2022; published online March 17, 2022 Abstract 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 re- cord (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 proto- type model named interoperability smart lane for elec- tronic 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 cli- ents 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 heteroge- neous 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 cor- rectness and performance. Keywords: electronic health record; fast healthcare inter- operability resources; interoperability; machine learning; natural language processing. Introduction Electronic health records (EHRs) have become widely used in healthcare institutions. As a result, a vast amount of medical data was generated from several heteroge- neous healthcare providers, which might sometimes be shared in order to reap the most value. Interoperability in EHR systems is a pressing requirement with numerous advantages including patients’ capacity to access their medical history or any medical data at any clinic or hospital at any time, cost savings in healthcare services, workflow management, efficient medical decision- making, clinical risk reduction, and time savings [1]. Interoperability is “the capability of two or more func- tional units to process data cooperatively ” [2], and it was studied from many perspectives by researchers. From a data interpretation perspective, there are four stages of interoperability levels listed below [3]: – No interoperability (Level 0): medical data is incom- prehensible to people and machines alike. Also, the usage of technology in sharing, such as mail or fax, is lacking. – Syntactic interoperability (Level 1): the syntax of medical information is obvious and well-defined, however, its meaning is not. – Technical interoperability (Level 2): medical data can be transmitted between equipment. – Semantic interoperability (Level 3): medical data is clear and understandable by companies that do not use the same language. Another classification of interoperability levels is proposed by Ref. [4], where interoperability can be divided into three categories: – Foundational (Level 1): Data is exchanged across EHR systems without the ability to interpret it. – Structural (Level 2): Data is exchanged and interpreted at the data field level by EHR systems. – Semantic (Level 3): Information is exchanged and used by EHR systems. *Corresponding author: Arwa Najjar, Information Technology College, Hebron University, Hebron, Palestine, E-mail: ar1993wa@gmail.com Belal Amro, Information Technology College, Hebron University, Hebron, Palestine, E-mail: Bilala@hebron.edu Mário Macedo, Sciences and Technologies of Information and Communication College, Atlântica University, Lisbon, Portugal, E-mail: mariojcmacedo@gmail.com Bio-Algorithms and Med-Systems 2022; aop