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机器学习评估与护士敏感指标安全事件相关的不平等和差异
Erika R Georgantes1, Fatma Gunturkun2, T J McGreevy3
1Nursing Quality Management Coordinator, Nursing Quality, Stanford Health Care, Stanford, California, USA.
概括
机器学习发现,种族/种族没有影响护士敏感指标 (NSI) 事件. 然而,紧急住院和特定诊断增加了NSI风险,为有针对性的患者护理改进提供了信息.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 临床护理 临床护理
- 医疗保健服务研究 医疗服务研究
背景情况:
- 护士敏感的指标 (NSI),如跌倒,压力损伤 (HAPI) 和感染 (HAI) 显著影响患者的结果.
- 检查与NSI相关的健康公平性和临床结果对于改善患者护理至关重要.
- 机器学习为分析复杂的医疗数据和识别风险因素提供了新的方法.
研究的目的:
- 利用机器学习来研究NSI患者的健康公平性和临床结果.
- 确定患者群体和与NSI风险增加相关的临床因素.
- 为预防NSI和减少医疗保健差异制定战略提供信息.
主要方法:
- 一个单一的学术医院的回顾性观察研究 (2016-2021年).
- 机器学习,包括LightGBM模型,用于分析NSI事件与非NSI事件.
- 单变量和多变量分析,包括模拟研究,比较了患者的人口统计和临床变量.
主要成果:
- 在163,507个招生中,有2.8%至少有一个NSI.
- NSI事件与种族/种族不相关.
- 患有NSI的患者逗留时间更长 (LOS),ICU LOS更长,急诊入院的可能性更高.
结论:
- 种族/种族不是NSI事件的风险因素.
- 紧急入院,较长的LOS,较长的ICU LOS和特定的主要诊断类别 (MDC) 与增加NSI风险有关.
- 机器学习增强了NSI的调查,有助于识别有风险的患者,并改善护理,以防止伤害.
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