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一种使全科医生推适合人工智能部署的方法

Evelyn Lesiawan¹, Bruce Sutherland², Christoph Schumacher³

¹Advanced Physician Trainee, Health New Zealand - Te Whatu Ora, New Zealand.

The New Zealand medical journal

|December 11, 2025

概括

包括人工智能和机器学习在内的决策支持工具可以帮助管理越来越多的门诊转诊工作负载. 将简单的决策树与机器学习相结合,可确保患者安全和有效的分类.

科学领域:

医疗保健管理的管理
医疗信息学医学信息学
医学中的人工智能.

背景情况:

门诊转诊对医院专家来说是一个越来越大的工作负担.
不有效的转诊管理对患者护理构成重大风险.
目前的转介系统缺乏标准化,导致数据差距和变化.

研究的目的:

探索决策支持系统 (DSS) 的潜力,包括人工智能和机器学习,以优化门诊转诊管理.
为在临床实践中实施人工智能驱动的DSS提出结构化的方法.
为了应对数据标准化和转诊分类中的决策可变性所面临的挑战.

主要方法:

调查了全科医生对转诊流程的看法.
对拒绝的门诊转诊进行了审计.
制定了转介评估的决策树草案.
建议从自由文本过渡到结构化参考格式.
建议将决策树与机器学习相结合用于分类.

主要成果:

确定了在转介中对结构化数据的需求,以实现有效的自动化决策支持.
突出了当前分类决策中的人类变异性.
展示了人工智能和机器学习在标准化和改进推决策方面的潜力.
强调逐步实施对患者安全的重要性.

结论:

结构化推数据对于开发强大的自动化决策支持至关重要.
结合简单的决策树和先进的机器学习的混合方法可以增强推分类.
对黄金标准决策的逐步实施和培训对于安全有效的DSS采用至关重要.

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