Implementation of guidelines on delirium in a General Hospital: a before-after study of their impact on caregivers' knowledge and clinical skills

INTRODUCTION: Delirium is a highly prevalent disorder, with serious consequences for the hospitalised patient. Nevertheless, it remains under-diagnosed and under-treated. We developed evidence-based clinical practice guidelines (CPGs) focusing on prevention, screening, diagnosis, and treatment of delirium in a general hospital. This article presents the implementation process of these CPGs and a before-after study assessing their impact on healthcare professionals' knowledge and on clinical practice. METHODS: CPGs on delirium were first implemented in two wards (Neurology and Neurosurgery) of the Lausanne university hospital. Interactive one-hour educational sessions for small groups of nurses and physicians were organised. Participants received a summary of the guidelines and completed a multiple choice questionnaire, assessing putative changes in knowledge, before and three months after the educational session. Other indicators such as "diagnosis of delirium" reported in the discharge letters, and mean duration of patients' hospital stay before and after implementation were compared. RESULTS: Eighty percent of the nurses and physicians from the Neurology and Neurosurgery wards attended the educational sessions. Both nurses and physicians significantly improved their knowledge after the implementation (+9 percentage-points). Other indicators were not modified by the intervention. CONCLUSION: A single interactive intervention improved both nurses' and physicians' knowledge on delirium. Sustained and repeated interventions are probably needed to demonstrate changes in clinical practice.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.