Context-aware anomaly detection for electronic medical record systems
HealthSec'11 Proceedings of the 2nd USENIX conference on Health security and privacy
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The benefits and importance of electronic medical record (EMR) systems have been well recognized in the health-care industry. Yet, their wide adoption still face significant barriers in providing on-demand secure medical information access while preserving patients' privacy. Understanding the usage pattern of an EMR system is the first essential step towards building such environment. This paper conducts an in-depth trace analysis of a large-scale EMR system that has been in operation for more than a decade at the Vanderbilt Medical Center. Our study demonstrates several important characteris- tics of EMR system usage from the perspective of user-initiated sessions. First, the workload of the EMR system is highly stable and consistent with a weekly pattern. Second, EMR behavior varies between users, but each user's behavior tends to be consistent with a slow rate of migration across sessions. Finally, the degree of access between users and medical records is sparse, echoing the limits of patient-caregiver relationships that manifest in real healthcare operations. We believe these observations can assist in the development of system security measures, such as EMR-specific anomaly detection systems, and facilitate system performance optimization.