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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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基于人工智能的手术转换的FAST框架

Harmehr Sekhon1,2, Farid Al Zoubi3, Paul E Beaulé4

  • 1Division of Geriatric Medicine, Department of Medicine, St. Mary's Research Centre, McGill University, Montreal, QC, Canada.

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概括
此摘要是机器生成的。

基于人工智能的手术转型框架 (FAST) 使用机器学习提供实时建议,显著提高手术成功率 (SSR) 和手术室效率. 这种创新模式提高了医疗保健中的患者结果和资源利用率.

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人工智能的人工智能是人工智能.临床翻译 临床翻译操作室的操作室.规范性分析是指规范性分析.手术数据科学手术数据科学

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科学领域:

  • 人工智能在医学中的应用
  • 机器学习应用 机器学习应用
  • 优化外科手术工作流程的优化

背景情况:

  • 传统的机器学习 (ML) 在外科手术中主要侧重于预测,在手术室 (OR) 效率和手术成功率 (SSR) 中取得了有限的改善.
  • 随着手术等待时间的增加和医疗保健资源的限制,需要创新的ML解决方案来实现实时OR优化.
  • 基于人工智能的手术转换框架 (FAST) 旨在通过提供立即建议来提高OR效率来应对这些挑战.

研究的目的:

  • 开发和评估用于实时推的FAST模型,以提高OR效率.
  • 评估FAST对骨科手术成功率 (SSR) 的影响.
  • 确定FAST框架在临床环境中的可行性和可实施性.

主要方法:

  • 该FAST模型是使用6个ML算法开发的,包括决策树和神经网络.
  • 评估涉及4796例骨科病例的数据集,包括手术和团队特定变量.
  • 定期召开利益相关者研讨会,以确保遵守和采用FAST系统.

主要成果:

  • 通过强大的团队参与,FAST在骨科手术室展示了可行性和可实施性.
  • 实施导致SSR在23周内从基线时的39%大幅增加到93%.
  • 影响SSR的关键因素包括第一次手术的及时启动,高效的OR周转率和最佳的团队组成.

结论:

  • FAST是一个新的ML框架,能够提供实时反,以提高OR效率和SSR.
  • 成功整合利益相关者对于FAST系统的采用和有效性至关重要.
  • FAST框架提供了一个多功能和创新的ML应用程序,用于提高OR在各种医院环境和外科手术过程中的效率,而不需要额外的资源.