Pampattiwar Kalyani, Chavan Pallavi
SIES Graduate School of Technology and Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, 400706, Maharashtra, India.
Department of Information Technology, Ramrao Adik Institute of Technology, D.Y. Patil Deemed to be University, Nerul, Navi Mumbai, 400706, Maharashtra, India.
Sci Rep. 2025 Apr 4;15(1):11612. doi: 10.1038/s41598-025-94339-w.
In the contemporary digital era, the storage of Electronic Health Records on open platforms presents significant security and privacy challenges. Addressing these concerns requires standardizing the clinical deployment models currently in use. This paper proposes a robust model that overcomes critical issues related to security, privacy, access control, and ownership transfer of patients' records. The model incorporates data collection to assess clinical needs, followed by deployment-level checks to mitigate network attacks and enhance Quality-of-Service according to scalability demands. A Modified Genetic Algorithm is employed to improve blockchain scalability. The model also introduces mechanisms for ensuring database integrity, mitigating external attacks, and enhancing usability. It further supports platform-level modularization, access control, department-specific configurations, and patient-level confidentiality. The proposed solution outperforms existing systems, particularly in terms of ease of use, deployment delay, deployment complexity, and module-level efficiency, making it a highly suitable option for implementing secure, customized clinic security systems based on Electronic Health Records.
在当代数字时代，在开放平台上存储电子健康记录带来了重大的安全和隐私挑战。解决这些问题需要对当前使用的临床部署模型进行标准化。本文提出了一个强大的模型，该模型克服了与患者记录的安全性、隐私性、访问控制和所有权转移相关的关键问题。该模型包括数据收集以评估临床需求，随后进行部署级检查，以减轻网络攻击并根据可扩展性需求提高服务质量。采用改进的遗传算法来提高区块链的可扩展性。该模型还引入了确保数据库完整性、减轻外部攻击和提高可用性的机制。它进一步支持平台级模块化、访问控制、部门特定配置和患者级保密性。所提出的解决方案优于现有系统，特别是在易用性、部署延迟、部署复杂性和模块级效率方面，使其成为基于电子健康记录实施安全、定制化诊所安全系统的非常合适的选择。
