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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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    在医疗保健中,对人工智能 (AI) 的有效部署后监测至关重要. 一个新的框架通过跟踪完整性,性能和影响来确保人工智能系统保持安全,有效和有益.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 人工智能在医学中的应用
    • 临床决策支持 临床决策支持

    背景情况:

    • 医疗保健人工智能 (AI) 的部署后监测对于安全,质量和治理至关重要.
    • 确保人工智能系统继续提供好处,并按预期运行,需要强有力的监督.

    研究的目的:

    • 开发和介绍一个实用的框架,用于监控在医疗保健部署的AI系统.
    • 引导卫生系统制定可行的AI监测计划.

    主要方法:

    • 开发了一个基于三个原则的框架:系统完整性,性能和影响.
    • 以一个学术医疗中心的例子为例,用于传统和生成AI.
    • 提供有关指标,审查时间表,责任和后续行动的指导.

    主要成果:

    • 该框架在斯坦福医疗保健积极使用.
    • 它涉及系统正常运行时间,错误检测,IT生态系统变化,数据漂移以及临床/患者价值.
    • 为制定全面的监测计划提供了实际指导.

    结论:

    • 该框架为卫生系统提供了一个模板,以确保人工智能部署是安全和有效的.
    • 实施挑战包括资源限制和组织复杂性.
    • 持续监测对于在医疗保健中持续获得人工智能的好处至关重要.