Electronic Health Record System, EHRS, has recently become a common healthcare information technology that has been widely adopted by many physicians. Due to compromised internal structures and systems, safeguarding the privacy and security of EHRS becomes a very challenging issue based on the most recent studies on trends and characteristics of protected health information breaches in the United States. Traditionally intrusion detection systems were proposed to address security of EHRS infrastructure by detecting unauthorized accesses; they, however, tend to generate a large number of false alerts mainly due to lack of proper features to model normal behaviors and overfitting when using signature-based detection algorithms. In this paper we address this limitation and propose a mixture model combining both misuse detection and anomaly detection approaches to minimize the number of false alerts over a specific time period in real time through its self-learning-fixing-and-improving capability built upon the perceptron algorithm.