在初级医疗保健中使用机器学习预测优化患者不显示管理
Andrés Leiva-Araos1,2, Cristián Contreras3, Hemani Kaushal4
1Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA. n01513237@unf.edu.
Journal of medical systems
|January 14, 2025
概括
通过新的预测模型,减少了医疗保健不出现的情况. 该框架通过准确预测错过的预约和优化容量来改善预约安排,资源管理和患者护理.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 运营研究 运营研究
- 在医疗保健中的数据科学.
背景情况:
- "不出现"现象,即患者在没有取消的情况下错过预约,严重扰乱了医疗保健业务和资源分配.
- 现有的管理无人出现的方法往往缺乏全面的预测策略,容量管理和资源优化.
- 预测建模和特征选择的复杂性限制了医疗保健环境中先进分析工具的常规采用.
研究的目的:
- 开发和验证一个整体的预测模型框架,用于管理医疗保健没有出现.
- 将准确的不显示预测与优化服务容量,超额预订和资源分配相结合.
- 通过减少预处理步骤并消除在变量选择中需要专家判断来简化建模过程.
主要方法:
- 在五年内使用21,969名患者的数据进行了多变量分析.
- 采用半自动特征选择技术来识别没有出现的关键预测因素.
- 开发了一个预测模型框架,包含预测和资源管理的客观功能.
主要成果:
- 确定了与现有文献相一致的不出现的关键预测因素.
- 实现了与最先进的方法相比的预测结果,在特征选择中显著降低了复杂性.
- 展示了在医疗保健运营中的预测建模的简化和可扩展的方法.
结论:
- 拟议的预测模型框架为管理医疗保健不出现提供了实用和高效的解决方案.
- 该方法提高了各种医疗保健环境的预测模型的可用性和可扩展性.
- 这种方法使医疗保健提供者能够优化资源配置,改善服务交付,并减轻错过预约的影响.
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