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门诊外科组合:跨专业预测成人和儿科同一天手术病例

Thomas Clark Howell¹, Hamed Zaribafzadeh¹, Maxwell D Sumner²

¹From the Department of Surgery, Duke University Medical Center, Durham, NC.

Annals of surgery open : perspectives of surgical history, education, and clinical approaches

|March 26, 2025

概括

一个整体模型准确地使用病例报告数据预测当天手术 (SDS) 的适用性. 这种方法可以帮助高达20%的住院病例过渡到门诊手术,优化资源使用.

科学领域:

医疗信息学医疗信息学
外科手术手术研究研究
预测建模预测建模

背景情况:

门诊外科手术 (AS) 尚未得到充分利用,尽管它有潜力降低医疗保健成本并改善资源管理.
识别合适的当天手术 (SDS) 候选人的预测模型很少.
优化AS可以提高学术卫生系统的效率.

研究的目的:

使用手术前病例数据开发一个集体模型,预测同一天手术 (SDS) 的适用性.
为了确定适合在手术当天出院的患者病例 (行医手术).

主要方法:

一项回顾性研究分析了多站点学术卫生系统 (2021年1月至2023年12月) 的选择性手术病例.
三种渐变增强决策树模型预测了病例长度 (CL),术后停留时间 (LOS) 和出院处置 (DD).
一个门诊外科手术组合 (ASE) 模型被开发用于预测同一天手术 (SDS) 病例,使用病例报告数据.

主要成果:

该ASE模型表现出高预测准确度,接收器操作特征曲线 (AUC) 下面面积为0.95,平均精度为0.96.
对139593例病例的分析显示,目前多达20%的住院病例可能会转向SDS.
确定了特定的外科专业,程序和外科医生,这些外科医生有很高的SDS过渡潜力.

结论:

一个整体模型有效地预测CL,LOS和DD在调度时的可选案例.
这种预测能力可以系统地提高临床操作,包括战略规划和外科手术安排.
虽然没有完全纳入患者特定因素,但该模型为优化门诊外科手术利用提供了有价值的工具.

关键词:

这是一种门诊外科手术.卫生系统工程卫生系统工程机器学习是机器学习.经营性决策的制定.同一天的手术.

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