从新加坡的医院到家庭社区护理计划以及支持AI多重再接收预测模型的经验教训
John Abisheganaden1,2, Kheng Hock Lee3,4, Lian Leng Low3,5
1Department of Respiratory and Critical Care Medicine, Tan Tock Seng Hospital National Healthcare Group Singapore Singapore.
Health care science
|June 28, 2024
概括
新加坡的医院到家 (H2H) 计划使用人工智能 (AI) 模型来预测患者再入院,改善护理干预和节省资源. 这篇后续文章详细介绍了人工智能更新,结果和未来医疗保健工作的经验教训.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生政策 公共卫生政策
背景情况:
- 新加坡的医院到家 (H2H) 计划于2017年成立,旨在管理复杂的患者出院后.
- 此前开发了一种人工智能 (AI) 模型,用于预测住院再入院和指导社区护理干预.
研究的目的:
- 为H2H计划的AI模型和信息系统提供最新信息.
- 报告服务交付结果,包括员工节省的时间和患者的好处 (节省的床上时间).
- 分享有关数据分析,模型开发和系统集成的经验教训.
主要方法:
- 详细阐述H2H计划的护理模型及其支持AI的多重再接收预测.
- 报告客户参与和服务交付指标.
- 分析数据异质性,模型平衡和系统相互依赖方面的挑战.
主要成果:
- 关于人工智能和信息系统性能的更新.
- 通过减少入床时间,证明了员工的时间节省和患者的好处.
- 确定了在分析,模型管理和系统集成方面学到的关键经验教训.
结论:
- H2H计划的AI模型有效地支持社区护理干预和资源优化.
- 获得的经验将为未来的医疗保健计划提供信息,包括COVID-19应对措施和2023年后的公共医疗保健战略.
- 在综合临床系统中不断改进和适应人工智能模型对于持续的影响至关重要.
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