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首页> 外文期刊>Asian Nursing Research >Risk Factors Preventing Immediate Fall Detection: A Study Using Zero-Inflated Negative Binomial Regression
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Risk Factors Preventing Immediate Fall Detection: A Study Using Zero-Inflated Negative Binomial Regression

机译：防止立即跌落检测的危险因素：使用零充气负二项式回归的研究

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PurposeFalls are the most common accidents in healthcare facilities, and timely intervention can have a positive effect on the hazards and trauma experienced by patients after a fall. This study determined the factors affecting the time taken to detect a fall.MethodsA total of 3,470 cases of falls reported through the Korea Patient Safety Reporting and Learning System were included in the analysis. A zero-inflated negative binomial regression method was used for this retrospective secondary data analysis study.ResultsThere were 537 patients whose falls were not detected immediately; the count model was used to predict risk factors that delayed fall detection. Women aged 60–69 years—compared to those below 60?years and an evening nursing shift, compared to a day shift—were identified as significant factors. The fall detection time of about 2,933 patients was zero; therefore, the logit model was applied to predict a patient's possibility of belonging to the group whose fall was detected immediately. Comparisons of tertiary hospitals with general hospitals and hospitals, of the evening shift with the day shift, and of the day shift with the night shift indicated significant influencing factors.ConclusionsThese findings can assist nurses in recognizing patient and hospital characteristics related to delayed fall detection. Strategies to improve patient safety in healthcare facilities that focus on patient characteristics such as age can be recommended. Furthermore, nurse staffing requires improvement to detect fall incidents immediately.
机译：有目的事故是医疗保健设施中最常见的事故，及时干预对跌倒后患者所经历的危害和创伤产生积极影响。这项研究确定了影响堕落所采取的时间的因素。在分析中，通过韩国患者安全报告和学习系统报告的3,470例秋季的3,470例。零充气的负二进制回归方法用于该回顾性的次要数据分析研究。方法是537名患者立即未检测到下降;计数模型用于预测延迟落后检测的危险因素。 60-69岁的女性 - 与60岁以下的人相比，岁月和晚间护理转移，与日间转移相比 - 被确定为重要因素。大约2,933名患者的秋季检测时间为零;因此，应用了Logit模型以预测患者属于立即检测到的小组的可能性。与一般医院和医院的大专院位医院比较，随着白天的转变，随着夜班的一天转变表明了显着的影响因素。结论的结果可以帮助护士认识到患者和医院特征，识别与延迟下降相关的患者和医院特征。提高专注于年龄如年龄等患者特性的医疗保健设施中提高患者安全的策略。此外，护士人员指鞋需要改善，立即检测秋季事件。

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