Improved hospital safety performance and reduced medicolegal risk: an ecological study using 2 Canadian databases

提高医院安全绩效并降低医疗法律风险:一项基于两个加拿大数据库的生态学研究

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Abstract

BACKGROUND: Few empirical studies have validated the relation between medicolegal risk and hospital patient safety performance. We sought to determine whether there was a relation between in-hospital patient safety events and medicolegal cases involving Canadian physicians. METHODS: In this ecological study, we used Poisson regression to compare data from the Canadian Institute for Health Information's Discharge Abstract Database and the database of the Canadian Medical Protective Association (CMPA) of medicolegal cases over 10 years (2005/06 to 2014/15). We identified incidents and cases based on 15 Agency for Healthcare Research and Quality patient safety indicators within the Canadian Institute for Health Information and CMPA data sets. We performed subgroup analyses for obstetrical and surgical cases. RESULTS: We found a statistically significant positive association between volume changes in patient safety indicator events (n = 339 741) and medicolegal cases (n = 15 180) (parameter estimate 1.15, 95% confidence interval [CI] 0.4 to 1.9). This association suggests that, on average, a 10% decrease in events would correspond to a decrease of 11% in medicolegal cases. The degree of positive association varied by practice type, with obstetrics (97 982 patient safety indicator events, 865 cases) showing a 25% decrease in medicolegal cases for every 10% decrease in events (parameter estimate 2.9, 95% CI 0.5 to 5.3) and surgery (168 886 patient safety indicator events, 4568 cases) showing a decrease of 9% for every 10% decrease in events (parameter estimate 0.9, 95% CI 0.2 to 1.7). INTERPRETATION: The statistically significant positive association between patient safety indicator events and medicolegal cases quantifies a relation between patient safety and physician medicolegal risk in Canadian hospitals. This suggests new, practical uses for both medicolegal and patient safety indicator data in system-level quality-improvement efforts.

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