Continuous Clinical Pathways Evaluation by using Automatic Learning Algorithms

The standardization of care processes in medicine, like Clinical pathways, is becoming more and more a common practice in health care organizations. Nevertheless, their design is not an easy task. Some approaches in the literature are based on using Workflow technology for defining Clinical Pathways. These approaches allow the creation of unambiguous, complete and automatically executable protocols. In addition to this, the use of Process Mining technology can help the design using information from real executions of Clinical Pathways cases. Nevertheless, to ensure a correct continuous evaluation and improvement of care processes, the creation of a tool that allows to know the current status of the Clinical Pathway execution it’s mandatory. In this paper, we present a tool able to compare the designed Clinical Pathways with the real implantation cases in order to detect their differences. This allows Clinical Pathways designers to improve the care protocols making them more adequate to real cases.