Les systèmes hospitaliers sont des lieux de soins caractérisés par la variété des activités et des situations auxquelles ils sont confrontés. Ceci engendre des interactions induisant des situations imprévues liées à plusieurs risques.La gestion des risques apparaît donc comme une préoccupation importante pour les décideurs. Plus particulièrement, la gestion des risques dans les blocs opératoires est d’une grande importance étant donné que ces derniers présentent des lieux hautement stratégiques par rapport aux nombreuses activités qu’ils regroupent et des coûts qu’ils engendrent. Le risque zéro n’existe pas, il peut néanmoins être réduit.Ce travail de recherche a pour objectif de maîtriser la gestion des risques dans les blocs opératoires. Cette recherche s’intègre dans le cadre d’une collaboration entre l’Ecole Centrale de Lille et le CHU Sahloul de Sousse, terrain d’étude choisi pour mettre en œuvre l’approche proposée. Étant donné qu’aucun système de gestion des risques n’a été mis en place dans cet établissement, ce travail représenté un apport important et original pour le CHU Sahloul.Notre démarche se déroule en plusieurs étapes. Tout d’abord, suite à une comparaison entre les méthodes de gestion des risques existantes, nous avons choisi d’adapter la méthode de gestion des risques AMDEC (Analyse des Modes de Défaillances, de leurs Effets et de leurs Criticités) aux blocs opératoires du CHU Sahloul de Sousse.Nous proposons ensuite un système d’aide à la décision pour la gestion des risques GRAMA (Gestion des Risques par une Approche Multi - Agent) afin d’orienter les intervenants dans les blocs opératoires vers les meilleures décisions pour minimiser les risques pouvant survenir. Enfin, une simulation basée sur l’approche proposée est mise en œuvre au CHU Sahloul / The hospital systems are a place of health care distinguished by the variety of activities and situations with which they are confronted. This creates interactions leading into unexpected situations related to several risks.The risk management appears as an important concern for the decisions makers. More particularly, the risk management in the operating theatres has a major importance given that they are about a highly strategic in relation to the many activities they include and the costs they generate. The zero risk does not exist. Never theless, it can be reduced.This research aims to control risk management in operating rooms. This research gets in collaboration between l’Ecole Centrale de Lille and the University Hospital (UH) of Sousse Sahloul, field of study chosen to implement the proposed approach. Since non system of risk management has been implemented in this establishment, this work is significant and original for the UH Sahloul.Our approach is made up of several steps. First, following a comparison between the existing methods of risk management, we chose to adapt the method of risk management FMECA (Failure Modes, Effects and Criticality Analysis) in operating rooms of UH Sahloul, Sousse. We propose a decision support system for risk management based on multi-agent approach in order to guide contributors in the operating rooms making the best decisions to minimize risks which occur in UH Sahloul. Finally, a simulation based on the proposed approach is implemented in the UH Sahloul.

This thesis presents new models for determining piezoresistive response in long, thin polysilicon beams with either axial and bending moment inducing loads or torsional loads. Microelectromechanical (MEMS) test devices and calibration methods for finding the piezoresistive coefficients are also presented for both loading conditions. For axial and bending moment inducing loads, if the piezoresistive coefficients are known, the Improved Piezoresistive Flexure Model (IPFM) is used to find the new resistance of a beam under stress. The IPFM first discretizes the beam into small volumes represented by resistors. The stress that each of these volumes experiences is calculated, and the stress is used to change the resistance of the representative resistors according to a second-order piezoresistive equation. Once the resistance change in each resistor is calculated, they are combined in parallel and series to find the resistance change of the entire beam. If the piezoresitive coefficients are not initially known, data are first collected from a test device. Piezoresistive coefficients need to be estimated and the IPFM is run for the test device's different stress states giving resistance predictions. Optimization is done until changing the piezoresistive coefficients provides model predictions that accurately match experimental data. These piezoresistive coefficients can then be used to design and optimize other piezoresistive devices. A sensor is optimized using this method and is found to increase voltage response by an estimated 10 times. For torsional loads, the test device consists of a slider-crank connected to two torsional legs. The slider-crank creates torsional stress in the legs which causes a change in the electrical resistance through the legs. A model that predicts the effects of a scissor hinge on the slider-crank is presented. Torsional stresses in the legs are calculated delete{using the membrane analogy.} and the legs are discretized into long parallel resistors and the stresses delete{from the membrane analogy} applied to each resistor. Assuming a second-order piezoresistance, an optimization is then done to find the piezoresistive coefficients by changing them until the model prediction fits the test data. These coefficients can be used to predict angular displacement from resistance measurements in fully integrated torsional sensors. Potential applications are discussed, and a torsional accelerometer is presented.