Comment formaliser les recommandations préventives pour faciliter leur utilisation dans des systèmes informatisés d'aide à la décision médicale ?

PARIS6-Bibl. St Antoine CHU (751122104) / SudocSudocFranceF

Modular Knowledge-Based Decision Support System Dedicated to a Cooperative Decision to Prevent Cardiovascular Diseases.

International audienceDespite the success of artificial intelligence solutions in the recent years, physicians are still reticent to use integrated functionalities to support their decision. Methods used to create these functionalities can be divided into two groups, each being associated to different questions. Data-based methods are seen as black boxes for which it is impossible to understand how the decision is taken; knowledge-based methods need to rely on formalized knowledge sources on the basis of evidence, which can be discussed and criticized by physicians for their use in real life. This paper presents a new modular decision support system for the prevention of cardiovascular diseases, based on knowledge and on cooperative decision between the patient and the physician. The decision support system is based on two layers: (i) the first layer is a knowledge-based module which generates automatically patient profile, and prevention strategies associated to the profile; (ii) the second layer is a dynamic collaborative graphic user interface which displayed information about the risks of treatment adherence failure, personalized motivation and follow-up strategies. In the future, we aim at assessing the platform in real life

PEPS, une plateforme de prévention cardiovasculaire orientée citoyen

International audienceLa plateforme PEPS a pour objectif de donner au citoyen des outils d'auto-évaluation du risque cardiovasculaire et la conception d'un plan personnalisé de prévention cardiovasculaire. Le risque global est analysé par quinze facteurs de risque, évalués par des questionnaires dédiés. Lorsque le facteur de risque a été identifié comme présent, des actions sont proposées pour réduire ce facteur de risque et le risque cardiovasculaire global. Co-construit par le patient et son médecin traitant, le plan de prévention personnalisé est un outil qui accompagne le citoyen dans des actions de prévention, en respectant ses motivations et ses préférences , respectant les principes du patient empowerment. Une première évaluation ergonomique a permis de mettre en évidence que le nombre important de question pouvait être un frein à l'approche

Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk

International audienceThe prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g. diabetes). Hence, French national clinical practice guidelines to quantify the cardiovascular risk is able to deal with more clinical situations than the SCORE. The goal of this paper is to formalize knowledge extracted from these guidelines and implement the rules so that they can be used into an auto-assessing tool of cardiovascular risk. Formalization followed five steps and was conducted under the guidance of medical experts. It resulted into a decision tree fed by eight decision variables. Evaluation of the accuracy of the decision tree showed 80% of agreement with an expert in medical informatics in predicting the cardiovascular risk level for 15 different clinical situations. Discrepancies correspond to the knowledge gaps within Clinical Practice Guidelines. We intend to extend the implementation of the decision tree to a complete tool, for allowing patient to auto-assess their cardiovascular risk. This tool will be integrated into a platform providing recommendations adapted to the calculated level of cardiovascular risk

Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus

<div><p>Background</p><p>Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization.</p><p>Objective</p><p>To obtain a consensus about the priority target conditions for algorithms monitoring children's growth.</p><p>Methods</p><p>We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating), with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists) as well as parent associations. We asked experts in the pilot (n = 11), reading (n = 8) and rating (n = 60) groups to complete the list of diagnostic classification of the <i>European Society for Paediatric Endocrinology</i> and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition.</p><p>Results</p><p>Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement).</p><p>Conclusion</p><p>This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.</p></div

Different phases of the formal consensus process (modified version of the RAND/UCLA method).

<p>Different phases of the formal consensus process (modified version of the RAND/UCLA method).</p

Results of the rating phase of the target conditions judged to be priorities for growth monitoring (n = 36 experts of 68).

<p>Results of the rating phase of the target conditions judged to be priorities for growth monitoring (n = 36 experts of 68).</p