动态价格应用以防止医院的财务损失,基于机器学习算法
Abdulkadir Atalan1, Cem Çağrı Dönmez2
1Department of Industrial Engineering, Çanakkale Onsekiz Mart University, Çanakkale 17100, Turkey.
Healthcare (Basel, Switzerland)
|July 13, 2024
概括
本研究引入了一个动态定价模型,以减少医院因错过预约而造成的财务损失. 像AdaBoost,梯度提升和随机森林这样的机器学习算法预测没有出现,从而使惩罚费计算成为可能.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 应用机器学习应用机器学习
背景情况:
- 非营利性医院的目标是尽量减少损失,而不仅仅是最大限度地提高利.
- 患者不出院造成医院收入来源的重大财政赤字.
- 动态定价策略可以减轻因没有预约而造成的损失.
研究的目的:
- 开发一个动态的医院预约定价模式.
- 评估患者不出现的财务影响.
- 评估机器学习算法的有效性,预测没有出现和计算罚款费用.
主要方法:
- 使用了三个机器学习算法:随机森林 (RF),梯度提升 (GB) 和AdaBoost (AB).
- 分析了9个医院部门的1073名患者的预约数据.
- 开发了一种数学公式来计算未出现和重新任命差距的罚款费用.
主要成果:
- AdaBoost (AB) 的平均罚款成本率为14.28%,是最低的.
- 梯度提升 (GB) 导致了19.47%的罚款成本率.
- 随机森林 (RF) 显示了最高的平均罚款成本率,为22.87%.
结论:
- 机器学习模型可以有效地估计因错过预约而造成的财务损失.
- 算法选择对未出现的计算罚款成本产生影响.
- 结果为医院管理提供了标准,以了解和减轻患者不出现的财务风险.
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