Funded by DFG
This project targets to improve the accuracy of adaptive software systems by integration of pre-obtained knowledge about clinical workflows.
Workflow description enables to define the actual environment and situation and helps to predict further development of the process without demanding information input of the surgeon. In minimally-invasive surgery, workflow analysis and adaptive prediction of the development can optimize the efficiency of the clinical work routine. Processes can be initialized automatically, the efficiency enhanced and thus the work of the operating team facilitated, which results in immense cost reductions.
The surgery chosen to monitor, analyze and model is primary the laparoscopic cholecystectomy. The standardized workflow and exceptional circumstances are defined and assigned to the adaptive system. A methodical approach enables the system to identify the current status and to predict the following process. Quality and quantity of sensor data are high in order to achieve highest accuracy. A direct assistance of the operating team by providing that context relevant and filtered information and an automatic induced reaction in case of emergency is created in cooperation with the Chair of Software & Systems Engineering of the Technical University Munich.