对心胸外科手术持续时间的预测分析作为数据驱动能力管理的第一步.
Mariana Nikolova-Simons1, Rikkert Keldermann2, Yvon Peters3
1Philips Research, Eindhoven, the Netherlands. mariana.simons@philips.com.
NPJ digital medicine
|November 7, 2023
概括
使用人工智能预测模型优化手术室 (OR) 安排,大大减少了手术持续时间的差异和延误. 新的模型提高了选择性和急性心胸手术的效率,提高了患者流量和OR利用率.
科学领域:
- 医疗信息学 医疗信息学
- 运营研究 运营研究
- 手术规划 手术规划
背景情况:
- 有效的手术室 (OR) 容量管理对于尽量减少手术取消和长期等待列表至关重要.
- 目前的手术安排依赖于平均手术时间,往往导致计划和实际手术持续时间之间的差异.
- 这些差异会对临床和财务结果以及患者和工作人员的满意度产生负面影响.
研究的目的:
- 量化当前手术安排模型的差异.
- 开发和评估新的预测模型,以优化手术持续时间的估计.
- 通过改进调度来提高手术室利用率和患者流动.
主要方法:
- 利用了来自2294次心胸手术的非身份化数据.
- 使用现有的外科医生平均手术时间模型计算差异.
- 开发并比较了包括线性回归,随机森林和极端梯度增强在内的集合模型.
主要成果:
- 组合模型在选择性手术中减少了19% (0.99对0.80) 的根平均平方误差 (RMSE),在急性手术中减少了52% (1.87对0.89).
- 手术延迟的比例在选择性手术中下降了28%,在急性手术中下降了9%,在急性手术中下降了60%和32%.
- 改善归因于将患者和手术特征纳入预测模型.
结论:
- 先进的AI驱动的预测模型比传统的手术持续时间估计方法有显著的改进.
- 这些模型可以作为宝贵的患者流动人工智能决策支持工具,用于手术规划人员.
- 通过这些预测模型优化OR调度,可以提高效率和资源利用率.
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