{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T19:15:54Z","timestamp":1768072554930,"version":"3.49.0"},"reference-count":86,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T00:00:00Z","timestamp":1689033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients\u2019 personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.<\/jats:p>","DOI":"10.3390\/informatics10030060","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T01:05:01Z","timestamp":1689123901000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Towards a Universal Privacy Model for Electronic Health Record Systems: An Ontology and Machine Learning Approach"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9886-0978","authenticated-orcid":false,"given":"Raza","family":"Nowrozy","sequence":"first","affiliation":[{"name":"College of Engineering and Science, Victoria University, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1043-2029","authenticated-orcid":false,"given":"Khandakar","family":"Ahmed","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering and Science, Victoria University, Melbourne 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-4266","authenticated-orcid":false,"given":"Timothy","family":"Mcintosh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, La Trobe University, Bundoora 3086, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1007\/s10916-018-0994-6","article-title":"Secure cloud-based ehr system using attribute- based cryptosystem and blockchain","volume":"42","author":"Wang","year":"2018","journal-title":"J. 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