Implementation of a Role-Based Behavioral Access Log Framework for Secure Healthcare Electronic Health Record Systems

Abstract

Healthcare Electronic Health Record (EHR) systems store sensitive data and are used by people with varying roles and responsibilities. While authentication and role-based access control can help prevent unauthorized access, they can only detect the oddness of authorized users. This study introduces a role-aware behavioral access-log framework for healthcare EHR systems to continuously monitor user activities produce structured behavioral data for future work in data preprocessing, anomaly detection techniques, role-aware analysis and risk assessment. This proposed system will combine role-based authentication, healthcare user management, EHR activity monitoring, automatic behavioral monitoring and dashboard-based monitoring in a controlled healthcare environment. Structured behavioral access logs of user interactions are created during login and logout, viewing or updating of patient records, searching for patient records, laboratory and medication-related activities. The created logs include significant contextual details such as user identification, role assigned to the user, department, date and time of access, patient identifier, user action, session details, user login and accessed healthcare resources. The data set created is not clinical patient data, but rather data on how users are interacting with the EHR system, is appropriate for security analysis. The study shows that controlled environment of EHR can continuously supply structured behavioral access data to develop AI-based security mechanisms to detect abnormal access behavior of authorized users. The proposed framework thus connects the EHR activity monitoring and future intelligent insider-threat detection research. It helps in reproducible behavioral analysis by ensuring consistency in activity records across all roles, departments, sessions and system modules in healthcare.