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在COVID-19大流行之前和期间预测制药供应链中断
Andrea C Hupman1, Juan Zhang2, Haitao Li1
1Supply Chain & Analytics Department, University of Missouri-St. Louis, St. Louis, Missouri, USA.
概括
预测制药供应链中断对于患者安全至关重要. 在COVID-19大流行期间适应的机器学习模型显示了预测准确度的变化,突出显示了需要定期更新以管理新出现的风险.
科学领域:
- 药品供应链管理的管理
- 在医疗保健中的数据分析.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 制药供应链 (PSC) 的中断会对患者护理产生负面影响.
- 对于PSC中断的预测建模尚未得到充分探索,尤其是在COVID-19流行病等突发事件期间.
- 现有的风险减缓策略缺乏对深度不确定性的强有力的预测能力.
研究的目的:
- 检查数据驱动模型在预测COVID-19大流行之前和期间PSC中断方面的有效性.
- 在深度不确定性条件下评估预测模型的性能.
- 了解突发事件对PSC中断预测准确性和可靠性的影响.
主要方法:
- 利用来自财富500强药房福利管理公司的仿制药数据.
- 开发了使用 naive Bayes 算法预测供应商或产品中断的预测模型.
- 在COVID-19大流行之前和期间分析模型性能指标 (灵敏度,特异性,错误阳性率).
主要成果:
- 确定了影响大流行期间产品供应中断的变量关系的统计学上显著的转变,与大流行前的稳定性形成鲜明对比.
- 随着COVID-19的出现,观察到模型灵敏度,特异性和假阳性率的变化.
- 证明了持续更新模型的好处,以保持预测性能,并指出在保护灵敏度方面存在挑战.
结论:
- 随着COVID-19的流行,制药供应链中的预测关系发生了重大变化.
- 定期更新预测模型对于面对突发事件时有效降低风险至关重要.
- 调查结果为风险分析师提供了关键的见解,在PSC风险预测中应对深度不确定性和意外事件.
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