BACKGROUND: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used.
OBJECTIVE: An evaluation of UMLS concept sufficiency for CIG definition.
METHODS: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness.
RESULTS: A significant number of the guideline text mapped to UMLS concepts.
CONCLUSIONS: The approach has shown promise and highlighted further challenges.

The number of people in the UK with two or more conditions continues to grow and their clinical management is complicated by the reliance on guidance focused on a single condition. This leaves individual clinicians responsible for collating disparate information from patient management systems and care recommendations to manually manage the contradictions that exist in the simultaneous treatment of various conditions.
We have devised a modelling language based on BPMN that allows us to create computer interpretable representations of single condition guidance and incorporate patient data to detect the points of conflict between multiple conditions based on their transformation to logical constraints. This has been used to develop a prototype clinical decision support tool that we can use to highlight the causes of conflict between them in three main areas: medication, lifestyle and well-being, and appointment bookings.
The prototype tool was used to discern contradictions in the care recommendations of chronic obstructive pulmonary disease and osteoarthritis. These were presented to a panel of clinicians who confirmed that the tool produced clinically relevant alerts that can advise clinicians of the presence of conflicts between guidelines relating to both clashes in medication or lifestyle advice.
The need for supporting general practitioners in their treatment of patients remains and this proof of concept has demonstrated that by converting this guidance into computer-interpretable pathways we can use constraint solvers to readily identify clinically relevant points of conflict between critical elements of the pathway.

Clinical practice guidelines (CPGs) often include ambiguous criteria making their translation as computer-interpretable guidelines a difficult task. In breast cancer management, whether to perform a breast conservative surgery (BCS) or not is one example. Most international CPGs recommend to perform a BCS when the tumour volume / breast volume ratio allows for good cosmetic results, which cannot be directly translated into a computable format. We propose to compute an estimate of the ratio using the maximum size of the tumour to compute the tumour volume and the bra size to compute the breast volume. In addition, we take into account the location of the tumour according to quadrants and unions of quadrants. The model has been tested on a retrospective sample of 34 clinical decisions of a breast cancer unit in a Parisian university hospital (France). Concordance was found in 91.2% of the cases, with good sensibility and specificity. This finding could set a new pathway to advance on the development of actionable decision criteria to be used in a future clinical decision support system for breast cancer management.

Clinical Practice Guidelines (CPGs) are promoted as a powerful tool for standardization of the medical care quality and improvement of patients\' outcomes. However, CPGs need to be formalized in a computer interpretable format (i.e. as Computer Interpretable Guidelines or CIGs) for their implementation within Clinical Decision Support Systems (CDSS). But, maintaining the reliability of these guidelines when deploying them in different clinical settings is still a challenge. On the one hand, the complexity of the medical language complicates the adoption of the guidelines in different clinical institutions. On the other hand, the continuous discovery of new evidence needs to be included within CPGs, updating their contents and providing tools for evidence assessment. Furthermore, although nowadays\' clinical decision-making tends towards a personalized process, guidelines are designed for a general population. In this paper, we present an Authoring Tool (AT) that allows clinicians to take an active role in the process of CPG formalization. This AT enables them to introduce new clinical knowledge and create personalized CIGs for their local application, which best fits their clinical needs. The proposed system also allows the use of ontologies to facilitate the standardization and interoperability of the created guidelines. Finally, the content included in the CIGs can be evaluated using standard systems for grading clinical evidence.

Patients with multiple medical conditions (comorbidity) pose major challenges to clinical decision support systems, since the different Clinical Practice Guidelines (CPG) often involve adverse interactions, such as drug-drug or drug-disease interactions. Moreover, opportunities often exist for optimizing care and resources across multiple CPG. These challenges have been taken up in the state of the art, with many approaches focusing on the static integration of comorbid CIG. Nevertheless, we observe that many aspects often change dynamically over time, in ways that cannot be foreseen - such as delays in care tasks, resource availability, test outcomes, and acute comorbid conditions. To ensure the clinical safety and effectiveness of integrating multiple comorbid CIG, these execution-time difficulties must be considered. Further, when dealing with comorbid conditions, we remark that clinical practitioners typically consider multiple complex solutions, depending on the patient\'s health profile. Hence, execution-time flexibility, based on dynamic health parameters, is needed to effectively and safely cope with comorbid conditions. In this work, we introduce a flexible, knowledge-driven and execution-time approach to comorbid CIG integration, based on an OWL ontology with clearly defined integration semantics.

Several different computer-assisted management systems of computer interpretable guidelines (CIGs) have been developed by the Artificial Intelligence in Medicine community. Each CIG system is characterized by a specific formalism to represent CIGs, and usually provides a manager to acquire, consult and execute them. Though there are several commonalities between most formalisms in the literature, each formalism has its own peculiarities.
The goal of our work is to provide a flexible support to the extension or definition of CIGs formalisms, and of their acquisition and execution engines. Instead of defining \"yet another CIG formalism and its manager\", we propose META-GLARE (META Guideline Acquisition, Representation, and Execution), a \"meta\"-system to define new CIG systems.
In this paper, META-GLARE, a meta-system to define new CIG systems, is presented. We try to capture the commonalities among current CIG approaches, by providing (i) a general manager for the acquisition, consultation and execution of hierarchical graphs (representing the control flow of actions in CIGs), parameterized over the types of nodes and of arcs constituting it, and (ii) a library of different elementary components of guidelines nodes (actions) and arcs, in which each type definition involves the specification of how objects of this type can be acquired, consulted and executed. We provide generality and flexibility, by allowing free aggregations of such elementary components to define new primitive node and arc types.
We have drawn several experiments, in which we have used META-GLARE to build a CIG system (Experiment 1 in Section 8), or to extend it (Experiments 2 and 3). Such experiments show that META-GLARE provides a useful and easy-to-use support to such tasks. For instance, re-building the Guideline Acquisition, Representation, and Execution (GLARE) system using META-GLARE required less than one day (Experiment 1).
META-GLARE is a meta-system for CIGs supporting fast prototyping. Since META-GLARE provides acquisition and execution engines that are parametric over the specific CIG formalism, it supports easy update and construction of CIG systems.