Reconciliation of Multiple Guidelines for Decision Support: A case study on the multidisciplinary management of breast cancer within the DESIREE project

Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations

Making a Subjective Notion Computer-Interpretable: The Case of the Tumour-Volume to Breast-Volume Ratio for the Surgical Decision of Breast Cancer.

International audienceClinical 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

Implementation of an ontological reasoning to support the guideline-based management of primary breast cancer patients in the DESIREE project

International audienceThe DESIREE project has developed a platform offering several complementary therapeutic decision support modules to improve the quality of care for breast cancer patients. All modules are operating consistently with a common breast cancer knowledge model (BCKM) following the generic entity-attribute-value model. The BCKM is formalized as an ontology including both the data model to represent clinical patient information and the termino-ontological model to represent the application domain concepts. This ontological model is used to describe data semantics and to allow for reasoning at different levels of abstraction. We present the guideline-based decision support module (GL-DSS). Three breast cancer clinical practice guidelines have been formalized as decision rules including evidence levels, conformance levels, and two types of dependency, "refinement" and "complement", used to build complete care plans from the reconciliation of atomic recommendations. The system has been assessed on 138 decisions previously made without the system and replayed with the system after a washout period on simulated tumor boards (TBs) in three pilot sites. When TB clinicians changed their decision after using the GL-DSS, it was for a better decision than the decision made without the system in 75 % of the cases

Une plateforme multimodale d’aide à la décision. Application à la prise en charge du cancer du sein dans le cadre du projet DESIREE

International audienceIn order to improve health care quality, we have developed a platform including three clinical decision support systems (CDSSs) based on complementary approaches, (i) a CDSS based on clinical practice guidelines (CPGs) that provides patient-centered state of the art recommendations, (ii) a CDSS based on the experience gained when informed clinician users decide not to follow CPGs and make non-compliant decisions that they are asked to justify, and (iii) a case-based reasoning CDSS. The three CDSSs are smoothly interacting under the control of a domain ontology. Used as a conceptual and terminological structure, the domain ontology proposes a generic data model (entity-attribute-value) and a knowledge model used for ontological reasoning (subsumption) and deductive decision support (inferences). The platform has been developed to improve the management of breast cancer patients within the European Commission funded project DESIREE