Améliorer la sécurité des patients hospitalisés par l'usage optimal des ressources infirmières.

Chercheurs boursiers - Junior 1 | Concours 2012-2013


Christian Rochefort

Université McGill

 

Domaine : Services de santé

Adverse events (AEs) are estimated to occur in 2.9% to 16.6% of all acute care hospitalizations, and studies have suggested that 30% to 58% of all AEs are preventable. Preventable AEs are a leading cause of death, resulting in 44,000 to 100,000 deaths in acute care hospitals each year. Recent research suggests that understaffing and the use of less qualified nursing staff are important contributors to the incidence of AEs. The next step in investigation is to determine the optimal nurse staffing levels needed to minimize AEs. This is especially important because the current nursing shortage is expected to worsen in the coming years. To help decision makers establish safe staffing policies, longitudinal studies are required to determine the temporal relationships between nurse staffing levels and the incidence of AEs, the thresholds for safe staffing levels, and the potential modification of these relationships by level of patient complexity.

Until recently, detailed daily patient and staffing data were not readily available to conduct such an investigation. With the advent of electronic medical records and digital capture of payroll and staffing data, an exciting opportunity has emerged to address these important questions in the management of nursing resources in acute care hospitals. However, a prerequisite to answering these questions is to determine if electronic health data are accurate for measuring AEs. The purpose of this program of research is to determine: a) the accuracy of using electronic data for measuring AEs b) if nurse staffing levels are associated with an increased risk of AEs, c) if the risk of AEs in relationship to nursing staffing levels is modified by the complexity of patient requirements and, d) if optimal nurse staffing levels can be established by level of patient complexity.