Comorbidities play a relevant role in healthcare, so that, in the last years, several approaches Medical Informatics and Artificial Intelligence have developed software tools to support physicians in the treatment of comorbid patients. Computer Interpretable Guidelines (CIGs) are consolidated decision support tools to help physicians, but they are devoted to provide evidence-based recommendations for one specific disease. In order to support the treatment of patient affected by multiple diseases, challenging additional problems have to be addressed, such as (i) the detection of the interactions between CIG actions, (ii) their management, and, finally, (ii) the "merge" of CIGs. Several CIG approaches have been recently extended in order to face (at least one of) such challenging problems, and one of them is GLARE (GuideLine Acquisition Representation and Execution). However, such approaches have mostly focused on the "apriori" treatment of such problems, while addressing them "run-time" (i.e., to support physicians during the execution of the CIGs on a specific patient) involves additional challenges, and requires additional methodologies. In this paper we take advantage of previous extensions of GLARE (to cope with issues (i), (ii), (iii)), and propose a new knowledge-based, "focused" and interactive management of comorbid patients.