一种混合AHP和K-means模型用于生物心理社会手术优先级:在高度复杂的ENT单元中进行验证
Fabián Silva-Aravena1, Jenny Morales1, Vivian D'Afonseca2
1Facultad de Ciencias Sociales y Económicas, Universidad Católica del Maule, Chile.
Clinics (Sao Paulo, Brazil)
|March 12, 2026
概括
本研究介绍了一种混合决策支持模型,该模型结合了分析层次过程 (AHP) 和K-Means集群,用于伦理外科手术等候名单管理. 这种方法显著降低了临床风险,并加速了高优先级患者的获取.
科学领域:
- 数字卫生系统 数字卫生系统
- 医疗保健服务研究 医疗服务研究
- 决策支持系统是什么?
背景情况:
- 手术等候名单在数字健康中存在重大管理挑战.
- 伦理和高效的优先级是公平获得医疗保健的关键.
- 目前的系统往往缺乏强大的框架来满足复杂的患者需求.
研究的目的:
- 开发和验证用于手术等候名单优先级的决策支持框架.
- 将生物心理社会因素整合到混合优先级模型中.
- 提高医疗保健资源分配的公平性和效率.
主要方法:
- 整合分析层次过程 (AHP) 与K-Means集群.
- 使用多维生物心理社会变量开发混合模型.
- 在智利一家公立医院的耳鼻喉科 (ENT) 部门申请和验证.
主要成果:
- 平均临床风险降低了27%.
- 紧急住院病例减少了41%,紧急病床日减少了32%.
- 与时间表方法相比,高优先级患者的访问加速超过12天.
结论:
- 混合AHP和K-Means模型提供了一个透明,可扩展和可解释的工具,用于手术优先级.
- 这种方法通过提高医疗保健提供效率和响应能力来支持数字健康转型.
- 该框架为复杂的医疗保健环境中的现实应用提供了可操作的伦理保障.
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