BACKGROUND: Reporting adverse events (AE) with a bearing on patient safety is fundamentally important to the identification and mitigation of potential clinical risks. OBJECTIVE: The aim of this study was to analyze the AE reporting systems adopted at a university hospital for the purpose of enhancing the learning potential afforded by these systems. RESEARCH DESIGN: Retrospective cohort study METHODS: Data were collected from different information flows (reports of incidents and falls, patients' claims and complaints, and cases of hospital-acquired infection [HAI]) at an university hospital. A composite risk indicator was developed to combine the data from the different flows. Spearman's nonparametric test was applied to investigate the correlation between the AE rates and a Poisson regression analysis to verify the association among characteristics of the wards and AE rates. SUBJECTS: Sixty-four wards at a University Hospital. RESULTS: There was a marked variability among wards AE rates. Correlations emerged between patients' claims with complaints and the number of incidents reported. Falls were positively associated with average length of hospital stay, number of beds, patients' mean age, and type of ward, and they were negatively associated with the average Cost Weight of the Diagnosis-related group (DRG) of patients on a given ward. Claims and complaints were associated directly with the average DRG weight of a ward's patient admissions. CONCLUSIONS: This study attempted to learn something useful from an analysis of the mandatory (but often little used) data flows generated on adverse events occurring at an university hospital with a view to managing the associated clinical risk to patients.
BACKGROUND: Reporting adverse events (AE) with a bearing on patient safety is fundamentally important to the identification and mitigation of potential clinical risks. OBJECTIVE: The aim of this study was to analyze the AE reporting systems adopted at a university hospital for the purpose of enhancing the learning potential afforded by these systems. RESEARCH DESIGN: Retrospective cohort study METHODS: Data were collected from different information flows (reports of incidents and falls, patients' claims and complaints, and cases of hospital-acquired infection [HAI]) at an university hospital. A composite risk indicator was developed to combine the data from the different flows. Spearman's nonparametric test was applied to investigate the correlation between the AE rates and a Poisson regression analysis to verify the association among characteristics of the wards and AE rates. SUBJECTS: Sixty-four wards at a University Hospital. RESULTS: There was a marked variability among wards AE rates. Correlations emerged between patients' claims with complaints and the number of incidents reported. Falls were positively associated with average length of hospital stay, number of beds, patients' mean age, and type of ward, and they were negatively associated with the average Cost Weight of the Diagnosis-related group (DRG) of patients on a given ward. Claims and complaints were associated directly with the average DRG weight of a ward's patient admissions. CONCLUSIONS: This study attempted to learn something useful from an analysis of the mandatory (but often little used) data flows generated on adverse events occurring at an university hospital with a view to managing the associated clinical risk to patients.
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Keywords:
Patient safety; adverse events; epidemiology and detection; measurement/epidemiology; medical error; risk management; safety culture