The analysis of surgical activities became a popular field of research in recent years. Various methods had been published to detect surgical phases in various data sources in the operating room. Objective of this research is to develop a method for utilizing real-time information to extract surgical activities. In this work we use fine-grained data of surgical devices and operating room equipment which is produced permanently during surgeries. This low-level data help describing the current surgical phases and reflect real-time status of the endoscope, insufflator, electrosurgical devices and light sources. This is the basis for the development of a structured process to extract surgical phase recognition models. We show how to integrate expert knowledge and transfer this information into an automated and scalable information system for surgical phase recognition. The artifact is developed by adapting the method engineering methodology to find a best practice for utilizing fine-grained data for intrasurgical activity detection. We evaluated our approach with 15 data sets of laparoscopic surgeries and obtained an accuracy rate of about 83% with this approach.