| Sumario: | Introduction: A growing body of literature suggests that medication errors (MEs) result from a complex interaction of factors related to inadequate medication management systems or from human factors and staffing shortages. Research has identified several significant associations between MEs and nurse staffing indicators, but the evidence is moderate and varies considerably across studies. Most of this research has been conducted in the United States. Few studies have been undertaken in Canada. Objective: The purpose of this study was to demonstrate the feasibility of using an innovative data warehouse to assess the association of nurse staffing indicators with MEs in a Canadian context. Methods: A correlational design was used to examine retrospective data (2019–2021) from an innovative data warehouse (LEPSI) located in a university hospital center in a metropolitan area. Specifically, 94,145 patient files, 8368 employee profiles, and 2813 MEs were analyzed. A generalized linear mixed model was used to model the relationship between nurse staffing indicators and MEs, with confounding variables controlled. Results: A significant association emerged between lower odds of ME occurrence and, respectively, higher ratios of clinical nurse (bachelor's degree) and nurse technician (professional/vocational diploma) hours to overall care team member hours (OR: 0.46, 95% CI: 0.32–0.66, p < 0.001) and higher ratios of clinical nurse hours to care team hours (OR: 0.41, 95% CI: 0.31–0.54, p < 0.001). No evidence emerged to support an association between MEs and, respectively, higher ratios of overall care team hours to overall patient hours (OR: 1.01, 95% CI: 0.83–1.24, p = 0.890) and higher ratios of care team overtime hours to care team hours (OR: 0.71, 95% CI: 0.47–1.06, p = 0.097). Conclusions: Results proved consistent with the literature, thus confirming the feasibility of using the LEPSI data warehouse for nursing performance measurement. The use of a data warehouse to assess the association between nurse staffing indicators and MEs constitutes a promising approach for generating evidence to inform decision making related to patient safety.
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