An Analytical Review of Methods and Models for Health Data Control
DOI:
https://doi.org/10.22034/TJT.3.1.79Keywords:
Electronic Health Records, Privacy, Computer Security, Blockchain, Machine Learning, Medical Informatics, Online SystemsAbstract
The rapid digital transformation of healthcare has increased the generation, storage, and exchange of electronic health data, creating challenges related to security, privacy, interoperability, and access control. This structured narrative review examines contemporary approaches to healthcare data control based on peer-reviewed studies published between 2018 and 2026. The literature was synthesized into six thematic categories: access-control models, cryptographic techniques, blockchain-based frameworks, anonymization methods, artificial intelligence and machine learning approaches, and hybrid security architectures. The findings show that traditional access-control mechanisms alone are insufficient for modern healthcare environments. Emerging technologies such as homomorphic encryption, blockchain, federated learning, and artificial intelligence improve confidentiality, privacy protection, and threat detection. However, their adoption remains constrained by challenges including computational complexity, scalability, interoperability, and implementation barriers. The review also highlights the importance of interoperability standards, particularly HL7 FHIR, and patient-centered data governance for secure information exchange. Overall, the evidence suggests that hybrid and multilayered architectures that combine complementary security technologies provide the most effective approach to controlling healthcare data. Future research should focus on enhancing interoperability, developing scalable privacy-preserving solutions, strengthening governance frameworks, and facilitating real-world implementation.
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