PARS, a system combining semantic technologies with multiple criteria decision aiding for supporting antibiotic prescriptions.

OBJECTIVE: Motivated by the well documented worldwide spread of adverse drug events, as well as the increased danger of antibiotic resistance (caused mainly by inappropriate prescribing and overuse), we propose a novel recommendation system for antibiotic prescription (PARS). METHOD: Our approach is based on the combination of semantic technologies with MCDA (Multiple Criteria Decision Aiding) that allowed us to build a two level decision support model. Given a specific domain, the approach assesses the adequacy of an alternative/action (prescription of antibiotic) for a specific subject (patient) with an issue (bacterial infection) in a given context (medical). The goal of the first level of the decision support model is to select the set of alternatives which have the potential to be suitable. Then the second level sorts the alternatives into categories according to their adequacy using an MCDA sorting method (MR-Sort with Veto) and a structured set of description logic queries. RESULTS: We applied this approach in the domain of antibiotic prescriptions, working closely with the EpiCura Hospital Center (BE). Its performance was compared to the EpiCura recommendation guidelines which are currently in use. The results showed that the proposed system is more consistent in its recommendations when compared with the static EpiCura guidelines. Moreover, with PARS the antibiotic prescribing workflow becomes more flexible. PARS allows the user (physician) to update incrementally and dynamically a patient's profile with more information, or to input knowledge modifications that accommodate the decision context (like the introduction of new side effects and antibiotics, the development of germs that are resistant, etc). At the end of our evaluation, we detail a number of limitations of the current version of PARS and discuss future perspectives.

Can we identify termination of resuscitation criteria in cardiac arrest due to drowning: results from the French national out-of-hospital cardiac arrest registry.

RATIONALE, AIMS AND OBJECTIVES: The aim of this study was to describe the cohort of persons having experiences fatal and non-fatal drowning events, registered in the French cardiac arrest registry and to identify termination of resuscitation criteria. METHODS: We performed a prospective multicenter study based on data from French cardiac arrest registry database. All patients with cardiac arrest after drowning (CAD) recorded between July 2011 and November 2014 were included. The population description was carried out by medians [interquartile ranges (IQR)] or frequencies. The characteristics were compared in terms of the primary endpoint (alive vs dead at hospital admission) using chi-square or Fisher's exact and the Mann-Whitney U test. The predictive model was carried out using the multivariate logistic regression. RESULTS: The analysis included 234 CAD. The majority of patients were adults (83.6%) and males (64.5%). Most of the submersions occurred out of home (75.6%). We recorded 66.7% of incidents in fresh water. About a third of CAD was witnessed of which 33.8% had an immediate basic life support. Most of CAD patients received an advanced cardiac life support (87.2%). The median Mobile Medical Team response time was 22 [15-30] minutes. At hospital, 40.6% of patients were alive. Twenty one patients (9.0%) were discharged alive. Among them, 17 had a good neurological outcome. Faster interventions generally resulted in higher survival chances (Mobile Medical Team response time OR: 0.960[0.925; 0.996]; P = 0.0.031; no flow duration OR: 0.535[0.313; 0.913]; P = 0.022) if associated with ventilation (OR: 6.742[2.043; 22.250]; P = 0.002). Age (OR: 0.971[0.955; 0.988]; P = 0.001) and location outside (OR: 0.203[0.064; 0.625]; P = 0.007) are the other criteria of our model. CONCLUSIONS: The model is helpful to highlight explanative variables concerning CAD patients' outcome. The next step is the validation of these five factors by a larger study. Prevention and public training to lifesaving behaviours must be considered as priorities in French public health programmes.

[Hospital organizational analysis based on the Mintzberg model: the case of Sheikh Zayed Hospital, Rabat]. / Analyse organisationnelle de l'hôpital à travers le modèle de Mintzberg : cas de l'Hôpital Universitaire Cheikh Zaid.

The new system of hospital governance requires health institutions to develop new managerial, financial and social skills beyond their public service duties. As part of this new approach, the organizational modernization of hospitals involves introducing good management practices. However, managing the transition requires taking into account the specificities of existing organizational systems. Organizational systems are generally difficult to model and involve diverse and sometimes competing interests, concerns, habits, languages, cultures, tools and representations. This explains the high failure rate observed in hospital development projects at an organizational level. A number of organizational theories from a range of disciplines (sociology, biology, history, etc.) have examined the question of organization in hospitals. The many theories developed in this area are not incompatible. Rather, they form a set of useful tools for the analysis of organizational management. The purpose of this study was to conduct an organizational analysis of Sheikh Zayed Hospital (Rabat) based on the Mintzberg model as a prerequisite for the development and implementation of a restructuring plan.