Performing business process analysis in healthcare organizations is particularly difficult due to the highlydynamic, complex, ad-hoc, and multi-disciplinary nature of healthcare processes. Process mining is a promising approach to obtain a better understanding about those processes by analyzing event data recorded in healthcare information systems. However, not all process mining techniques perform well in capturing the complex and ad-hoc nature of clinical workflows. In this work we introduce a methodology for the application of process mining techniques that leads to the identification of regular behavior, process variants, and exceptional medical cases. The approach is demonstrated in a case study conducted at a hospital emergency service. For this purpose, we implemented the methodology in a tool that integrates the main stages of process analysis. The tool is specific to the case study, but the same methodology can be used in other healthcare environments.

Process mining can be used to extract healthcare processes related information from event logs by performing analysis exploiting the information recorded in it. We report a process mining analysis made to an event log containing traces on user activity recorded by a Virtual Electronic Patient Record (VEPR) system of a Central Hospital. A set of technical analyses were performed. Results from the discovery and characterization of global behavior and from a time series analysis on observed user tasks are reported. Process mining was applied successfully to discover, characterize and analyze user behavior recorded from VEPR. Worth noting the execution of tasks profile observed after log out, revealing significant security problems.

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