Abnormal Process Instances Identification Method in Healthcare Environment

In order to gain the competitive advantage, more and more hospitals put their attention on determining and optimizing the standard clinical pathway. However, there are many abnormal instances in the event logs which disturb the effect of the process mining and the process analysis. Also, the prescription and the medical test items may have a large difference in the specific disease, where has the similar clinical pathways due to the multi-factor in the clinical pathways. In this paper, an abnormal process instances identification method (APIIM) is proposed. Given the event logs and the standard clinical pathway, the method classifies the instances based on the classification attributes and identifies the abnormal instances by the outlier detection technology. Moreover, a case study using the real data in one hospital is implemented and the result shows that the method is effective and efficient in discovering the abnormal process instances in the healthcare environment.