BACKGROUND Psychiatric units have often been underrepresented in patient safety research, limiting evidence on adverse events in psychiatric care. PURPOSE This study aimed to describe reported serious patient safety incidents and preventive strategies in psychiatric inpatient care. METHOD A retrospective multimethod register study design was applied to analyze data from 36 units in Finland between 2021 and 2023. RESULTS One hundred fifty‑two serious incidents were recorded, involving patient aggression, self‑harm, discontinuity in care, and injuries. These events contributed to prolonged treatment, unit transfers, restrictive interventions, deterioration in patient condition, increased staff workload, and organizational costs. Incidents were more frequent in adolescent units. Preventive strategies included improving care environments, standardizing practices, enhancing medication management, and strengthening staffing and competencies. CONCLUSIONS Serious incidents compromise patient safety and the quality of nursing care, underscoring the need for adequate resources, staff training, and interprofessional collaboration.
Background: Psychiatric units have often been underrepresented in patient safety research, limiting evidence on adverse events in psychiatric care. Purpose: This study aimed to describe reported serious patient safety incidents and preventive strategies in psychiatric inpatient care. Method: A retrospective multimethod register study design was applied to analyze data from 36 units in Finland between 2021 and 2023. Results: One hundred fifty‑two serious incidents were recorded, involving patient aggression, self‑harm, discontinuity in care, and injuries. These events contributed to prolonged treatment, unit transfers, restrictive interventions, deterioration in patient condition, increased staff workload, and organizational costs. Incidents were more frequent in adolescent units. Preventive strategies included improving care environments, standardizing practices, enhancing medication management, and strengthening staffing and competencies. Conclusions: Serious incidents compromise patient safety and the quality of nursing care, underscoring the need for adequate resources, staff training, and interprofessional collaboration.
Background: Floating nurses are an essential staffing strategy to maintain coverage across hospital units, yet evidence on their impact on quality and safety outcomes remains limited.Objective: The purpose of this study was to examine the relationship between distinct types of float nurses and nurse-sensitive indicators.Methods: A cross-sectional study was conducted across 25 inpatient units in 3 hospitals between April 2023 and April 2024. Logistic regression was used to examine associations between float pool and floated unit nurses and rates of falls, pressure injuries, and health care-associated infections.Results: Float pool nurses were associated with a reduced risk of pressure injuries and central line-associated bloodstream infections. No significant associations were observed for floated unit nurses.Conclusions: Float pool nurses and floated unit nurses have distinct associations with nurse-sensitive indicators and should be evaluated separately.
Background: Hospital-acquired pressure injuries (HAPIs) are a significant and global adverse event impacting an estimated 2.5 million patients per year, costing from $20 900 to $151 700 per pressure injury.Purpose: The purpose of this pilot study was 2-fold: (1) to evaluate the effectiveness of artificial intelligence and machine learning (AI/ML) models in predicting HAPIs, and (2) to compare that effectiveness with the predictive accuracy of traditional analytic methods.Methods: Secondary data analysis was performed for this pilot study. A training dataset was created and used for our exploratory evaluation and comparisons between AI/ML models and traditional analytic methods for pressure injury predictive accuracy.Results: While logistic regression provided a reasonable fit and interpretable coefficients, tree-based AI models performed notably better in predicting HAPIs.Conclusions: The use of AI/ML models appears to offer an increase in precision for the identification of patients at risk for developing HAPIs.
Background: Catheter-associated urinary tract infections (CAUTI) lead to higher health care costs and adverse patient outcomes.Local Problem: A Midwestern acute care hospital saw an increase in CAUTI rates from July to December 2022.Methods: Plan-Do-Study-Act cycles guided this quality improvement project.Interventions: Staff education and coaching facilitated a change from daily bathing and urinary catheter care using soap and water to using chlorhexidine gluconate wipes for all adult patients with indwelling urinary catheters.Results: This change led to a substantial 81.1% reduction in the CAUTI rate, sustained over 24 months, decreasing from an average rate of 2.12 to 0.40.Conclusion: Implementing a standardized chlorhexidine gluconate bathing and catheter care protocol significantly reduced CAUTI rates, suggesting it is an effective strategy for minimizing CAUTI risk in acute care settings.
