Temporal Detection of Guideline Interactions

Abstract

Abstract:Clinical practice guidelines are widely used to support physicians, but only on individual pathologies. On the other hand, the treatment of patients affected by multiple diseases is one of the main challenges for the modern healthcare. This requires the development of new methodologies, supporting physicians in the detection of interactions between guidelines. In a previous work, we proposed a flexible and user-driven approach, helping physicians in the detection of possible interactions between guidelines, supporting focusing and analysis at multiple levels of abstractions. However, it did not cope with the fact that interactions occur in time. For instance, the effects of two actions may potentially conflict, but practical conflicts happen only if such effects overlap in time. In this paper, we extend the ontological model to deal with the temporal aspects, and the detection algorithms to cope with them. Different types of facilities are provided to physicians, supporting the analysis of interactions between both guidelines "per se", and the concrete application of guidelines to specific patients. In both cases, different temporal facilities are provided to user physicians, based on Artificial Intelligence temporal reasoning techniques.

GLARE-SSCPM: an Intelligent Systemto Support the Treatment of Comorbid Patients

“…The first step in the extension of GLARE to cope with comorbidities was the identification of the different tasks to be solved. In strict cooperation with the physicians in our team, we identified the following tasks: (1) The detection of interactions occurring between CIGs 2The management of the interactions 3The final merging of the CIGs We have separately discussed in technical detail our techniques to cope with each task independently of the others in previous publications, mostly in conference papers (interaction detection [9], interaction management [10], conciliation [11] and temporal reasoning for comorbidities [12], [13]). Notably, to cope with the medical problem, several advances with respect to the AI state of the art were achieved, such as the treatment of complexand not explored yettemporal issues [13], and an innovative approach to conditional plan merging [11].…”

Temporal detection and analysis of guideline interactions

“…However, such a disease-specific knowledge, which is mostly procedural knowledge, is complemented by what we call "general medical knowledge", which is the basic medical knowledge, mostly declarative, which is not related to a specific disease (so that it would be not "economical" to repeat it in many different guidelines). It is worth noticing that the distinction between CIGs and general medical knowledge, already adopted in [10], is not strict. Indeed, other approaches such as ASBRU [14] represent actions effects within CIGs.…”

“…The process of non-temporal detection of this interaction may proceed as in Example 1. Further details can be found in [10]. In this situation, however, we suppose that the question asked by the physician is different.…”

“…A preliminary and short version (11 pages) of this paper has been published in the proceedings of a recent HEALTHINF conference [10]. The general ideas underlying our temporal approach, the basic temporal ontology and the identification of the different scenarios and facilities have been briefly discussed in such a paper.…”

Querying Probabilistic Temporal Constraints for Guideline Interaction Analysis: GLARE’s Approach

“…In this paper we focus on interaction detection. In [5] we developed an ontology for interactions, and complemented it with detection algorithms. Interactions between CIGs occurs over time.…”