使用机器学习方法估计私人和公共医院的患者安全文化
Soheil Abbasi1, Khalil Alijanpour1, Taha Samad-Soltani2
1Department of Health, Safety, and Environment Management, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran.
医院的患者安全文化薄弱,影响了患者的护理. 机器学习模型确定了诸如错误通信和管理支持等关键领域的改进.
科学领域:
- 医疗保健管理的管理
- 医疗信息学 医疗信息学
- 患者安全研究 患者安全研究
背景情况:
- 在医疗保健系统中,患者安全至关重要.
- 医疗错误对大量患者群体构成重大风险.
- 评估患者安全文化 (PSC) 对于系统改进至关重要.
研究的目的:
- 评估公立和私立医院不同工作组的患者安全文化 (PSC).
- 使用机器学习方法来分析PSC数据.
- 确定影响PSC的因素,并开发预测模型.
主要方法:
- 使用了医院患者安全文化调查 (HSOPSC) 调查问卷.
- 机器学习算法包括人工神经网络 (ANN),随机森林 (RF) 和线性回归 (LR) 用于数据建模.
- 数据分析使用色数据挖掘和SPSS软件进行.
主要成果:
- 公立医院的总体PSC分数为41.99,私立医院的总分为40.96,这表明安全文化较弱.
- PSC与教育,工作经验,性别,收入和组织地位有显著的相关性.
- ANN认为反/错误沟通,组织学习和管理支持是影响PSC的关键因素.
结论:
- 医疗保健利益相关者应专注于培训和资源配置,以加强医院PSC.
- 开发的模型可以预测PSC,有助于主动安全干预.
- 改善PSC对于预防医疗错误和减少患者伤害至关重要.
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