A Comprehensive Review of Privacy and Security Techniques in Electronic Health Records (EHRs)

The incorporation of EHR into the systems of care delivery has positively impacted the quality and availability of patient services. However, with this digital transformation in the health sector comes he essential questions to do with the privacy and security of the patients' information. This paper aims at discussing late approaches to protecting EHR systems, where deep learning, distributed computing, blockchain, and artificial intelligence are discussed. The review also shows how these technologies can be applied to safely store identity, medical and other patients' information, avoiding breaches, and cyber threats while considering obstacles regarding data heterogeneity, integration, and necessities of real-time intrusion detection. The paper also points out some of the challenges bound to arise when these sophisticated methods are applied, for instance, superior scaling and costs. This paper aims to review different approaches and case studies used in the current context to define the holes in the literature and propose directions for the development of better EHR security. Thus, the discoveries substantiate the necessity of developing innovative, large-scale, and easy-to-implement protection paradigms for patient data in the modern context of health care.