通过机器学习模型或临床风险评分的风险分层来实现急性上部胃肠道出血的价值:成本最小化分析
Dennis L Shung1, John K Lin2, Loren Laine1,3
1Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
The American journal of gastroenterology
|September 27, 2023
概括
对上部胃肠道出血的风险评估工具可以显著降低医疗保健成本. 应用这些工具,如格拉斯哥-布拉奇福德得分或机器学习模型,为被认为低风险的患者提供了巨大的经济效益.
科学领域:
- 卫生经济学 卫生经济学
- 医疗信息学 医疗信息学
- 胃肠病学 胃肠病学
背景情况:
- 上部胃肠道出血 (UGIB) 是一个严重的医疗负担.
- 准确的风险分层对于紧急部门的安全和有效的患者管理至关重要.
研究的目的:
- 估计利用风险评估工具来识别低风险的UGIB患者适合急诊室出院的经济影响.
- 为了比较不同选策略的成本效益.
主要方法:
- 进行了成本最小化分析.
- 从美国医疗保健支付人的角度来看,采用了马尔科夫链模型.
- 选策略包括格拉斯哥-布拉奇福德得分和经过验证的机器学习模型,与常规护理相比较.
主要成果:
- 格拉斯哥 - 布拉奇福德得分策略预计,与常规护理相比,在5年内,国家储蓄超过27亿美元.
- 与通常护理相比,机器学习战略预计将在5年内节省超过34亿美元的国家储蓄.
结论:
- 实施UGIB管理的风险评估模型可以大大降低成本.
- 这些工具通过优化资源配置和提高患者吞吐量来提高医疗保健价值.
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