An ontological knowledge and multiple abstraction level decision support system in healthcare

The rationalization of the healthcare processes and organizations is a task of fundamental importance to grant both the quality and the standardization of healthcare services, and the minimization of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are widely used to provide decision support to physicians, supplying them with evidence-based predictive and prescriptive information about patients' status and treatments, but usually on individual pathologies. This sets up the urgent need for developing decision support methodologies to assist physicians and healthcare managers in the detection of interactions between guidelines, to help them to devise appropriate patterns of treatment for comorbid patients (i.e., patients affected by multiple diseases). We identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. We then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over the multiple levels of abstraction. Finally, we discuss the impact of the adoption of computerized clinical guidelines in general, and of our methodology in particular, for patients (quality of the received healthcare services), physicians (decision support and quality of provided services), and healthcare managers and organizations (quality and optimization of provided services).