Process cycle times are very important for the effectiveness and efficiency measurement of acute diseases treatment. Especially in the field of acute diseases like heart attack and stroke the treatment duration varies very often depending on the hospital in question. To identify process bottlenecks, a comprehensive knowledge about the relevant clinical processes and their duration is required. Therefore, the related process parameters are to be collected and related to the clinical context.

As treatment processes are performed in several hospital departments, different departmental information systems are involved. The departmental and IT-system borders partition thereby the workflow and induce process interfaces, which have to be bridged. Additionally, the heterogeneity of the involved systems complicates the standardized and automated retrieval of process parameters. As a result, the information about the patient-based clinical treatment workflow is generated in several hospital information systems.

Based on these facts, a new event-based clinical reference process model is developed. The objective is to provide the possibility of standardized and automated clinical process parameter retrieval involving the clinical IT-systems and standards. Therefore, the whole diagnostic and treatment workflow is analyzed starting with the patient’s admission at hospital and finishing with the patient’s discharge. Thereby, the medical relevant patient-centered activities and tasks in the field of time-critical diseases like heart attack and stroke are focused on. As modeling language, the event-based process chain (EPC) is used. Clinical relevant events are derived based on clinical guidelines and classified regarding their importance for the clinical workflow as well as the possibility of their automated retrieval. As a result, generic Key Performance Indicators (KPIs) are defined and integrated into the model with the purpose of process performance monitoring using the implemented model including process cycle time’s measurement as well as workflow bottleneck identification and variation analysis.