Learning relational policies from electronic health record access logs

Modern healthcare organizations (HCOs) are composed of complex dynamic teams to ensure clinical operations are executed in a quick and competent manner. At the same time, the fluid nature of such environments hinders administrators' efforts to define access control policies that appropriately balance patient privacy and healthcare functions. Manual efforts to define these policies are labor-intensive and error-prone, often resulting in systems that endow certain care providers with overly broad access to patients' medical records while restricting other providers from legitimate and timely use. In this work, we propose an alternative method to generate these policies by automatically mining usage patterns from electronic health record (EHR) systems. EHR systems are increasingly being integrated into clinical environments and our approach is designed to be generalizable across HCOs, thus assisting in the design and evaluation of local access control policies. Our technique, which is grounded in data mining and social network analysis theory, extracts a statistical model of the organization from the access logs of its EHRs. In doing so, our approach enables the review of predefined policies, as well as the discovery of unknown behaviors. We evaluate our approach with 5 months of access logs from the Vanderbilt University Medical Center and confirm the existence of stable social structures and intuitive business operations. Additionally, we demonstrate that there is significant turnover in the interactions between users in the HCO and that policies learned at the department-level afford greater stability over time.