Clinical guidelines are one of the major tools that have been introduced to increase the rationalization of healthcare processes, granting both the quality and the standardization of healthcare services, and the minimization of costs. Computer interpretable clinical guidelines (CIGs) are widely adopted in order to assist practitioners in decision making, providing them evidence-based recommendations based on the best available medical knowledge. However, a main problem in CIG adoption is the fact that, in the medical context, some degree of uncertainty is often present. Thus, during guidelines executions on specific patients, unpredictable facts and conditions (henceforth called exceptions) may occur. A proper and immediate treatment of such exceptions is mandatory, but most of the current software systems coping with CIGs do not support it. In this paper, the authors describe how the GLARE system has been extended to deal with this purpose. They identify different types of exceptions, considering their “pre-locability” and “pre-plannability”. On the basis of such parameters, the authors propose different treatment modalities, consisting of both data structures to model the different types of exceptions, and the algorithms to treat them. The resulting methodology is an innovative one, integrating different Artificial Intelligence techniques (ranging from planning to ontology-based reasoning). Finally, they also discuss how they implemented their system-independent methodology on top of GLARE, and describe its application in the ROPHS project, considering the management of the severe trauma guideline.
Knowledge-Based Support to the Treatment of Exceptions in Computer Interpretable Clinical Guidelines
PIOVESAN, LUCA;TERENZIANI, Paolo
2016-01-01
Abstract
Clinical guidelines are one of the major tools that have been introduced to increase the rationalization of healthcare processes, granting both the quality and the standardization of healthcare services, and the minimization of costs. Computer interpretable clinical guidelines (CIGs) are widely adopted in order to assist practitioners in decision making, providing them evidence-based recommendations based on the best available medical knowledge. However, a main problem in CIG adoption is the fact that, in the medical context, some degree of uncertainty is often present. Thus, during guidelines executions on specific patients, unpredictable facts and conditions (henceforth called exceptions) may occur. A proper and immediate treatment of such exceptions is mandatory, but most of the current software systems coping with CIGs do not support it. In this paper, the authors describe how the GLARE system has been extended to deal with this purpose. They identify different types of exceptions, considering their “pre-locability” and “pre-plannability”. On the basis of such parameters, the authors propose different treatment modalities, consisting of both data structures to model the different types of exceptions, and the algorithms to treat them. The resulting methodology is an innovative one, integrating different Artificial Intelligence techniques (ranging from planning to ontology-based reasoning). Finally, they also discuss how they implemented their system-independent methodology on top of GLARE, and describe its application in the ROPHS project, considering the management of the severe trauma guideline.
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