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

Abstract

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).

Temporal Detection of Guideline Interactions

“…Thus, in our opinion, a "black box" system pointing out all the possible interactions between two CIGs (considering all the possible levels of detail) would be not practically useful for physicians, since, in general, it would return too many interactions. In our previous work (Piovesan et al 2014), we have devised a system that, collaborating with the physician to focus only on relevant parts of CIGs at the desired level of detail, helps her/him in the detection of relevant interactions (see Section 2.3), but we have neglected the temporal dimension. Modelling time, and extending the detection interaction system to cope with the temporal dimension, are the goal of this paper.…”

“…For the drug taxonomy, the ATC classification (http://www.whocc.no/atc/) is used; however, our approach is independent of the classification adopted. A distinguishing feature of our approach is that it copes with interactions at three different levels of abstraction: between the intentions of actions, between drug categories and between specific drugs (see concrete examples in (Piovesan et al 2014)). …”

“…In a recent work (Piovesan et al 2014) we detailed our preliminary semantic model for the description of CIG actions and for the non-temporal interactions occurring between them. For the sake of brevity, in the left part of Figure 1 we show a fragment of such an ontology relevant to this paper.…”

“…We have also pointed out that, in turn, levels (i) and (ii) may be structured at different levels of detail. In (Piovesan et al 2014), we have also proposed an ontological representation for the interactions at the different levels, as well as support for interactive physician-driven analysis of the interactions, at the different levels.…”

“…In (Piovesan et al 2014), we identified three different knowledge levels at which interactions might occur: (i) level of the intentions of the CIG actions, (ii) level of the goals of the drug categories (recommended by the pharmaceutical actions in the CIGs), and (iii) level of drugs. We have also pointed out that, in turn, levels (i) and (ii) may be structured at different levels of detail.…”