可解释的人工智能可以通过运营利指标评估COVID-19大流行期间医院的财务表现
Seifollah Gholampour1, Majid Asadi2, Arshia Dehghan1
1Department of Neurological Surgery, The University of Chicago , Chicago, Illinois, USA.
Journal of health organization and management
|October 13, 2025
概括
人工智能模型揭示了COVID-19期间医院的主要财务驱动因素. 较小的医院面临更大的脆弱性,需要有针对性的成本管理和报销结构的政策审查.
科学领域:
- 医疗保健管理的管理
- 医疗保健中的人工智能
- 金融分析 金融分析
背景情况:
- 医院的财务性至关重要,尤其是在COVID-19流行病等危机期间.
- 传统的财务分析可能无法捕捉影响医院运营利的复杂相互作用.
- 了解这些动态对于有效的医疗管理至关重要.
研究的目的:
- 开发一个人工智能框架来分析医院的财务性.
- 确定医院运营利的关键临床,财务和运营驱动因素.
- 通过结合非线性相互作用来增强传统的金融分析.
主要方法:
- 利用来自美国医院财务报告的21个参数.
- 评估了12个机器学习,整体和深度学习回归模型.
- 采用了夏普利添加式解释 (SHAP) 来进行特征重要性和互动分析.
主要成果:
- 梯度增强模型实现了高预测精度 (R2=0.79).
- 确定了供应费用,许可床位,患者日,慈善护理,医疗补助/医疗保险收入,员工福利和病例组合指数 (CMI) 作为关键决定因素.
- 揭示了医院规模依赖的财务动态和较小医院更大的脆弱性.
结论:
- 这项研究开创了在COVID-19期间将可解释AI (SHAP) 集成到医院财务分析中.
- 为人工智能驱动的,以公平为重点的医疗保健财务管理提供了一个框架.
- 强调需要平衡成本控制与护理质量,并解决补偿差异.
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