Verification of Medical Guidelines Using Background Knowledge in Task Networks

The application of a medical guideline to the treatment of a patient's disease can be seen as the execution of tasks, sequentially or in parallel, in the face of patient data. It has been shown that many of such guidelines can be represented as a "network of tasks," that is, as a sequence of steps that have a specific function or goal. In this paper, a novel methodology for verifying the quality of such guidelines is introduced. To investigate the quality of such guidelines, we propose to include medical background knowledge to task networks and to formalize criteria for good medical practice that a guideline should comply with. This framework was successfully applied to a guideline dealing with the management of diabetes mellitus type 2 by using KIV.