一个适应性决策支持系统,用于安排异质服务时间的门诊预约
Haolin Feng1, Yiwu Jia2,3, Teng Huang4
1School of Business, Sun Yat-sen University, Guangzhou, 510275, China.
Scientific reports
|November 12, 2024
概括
这项研究引入了一个数据驱动的系统,集群预测时间表 (CPS),用于门诊预约安排. CPS优化了患者分组和安排,降低了成本,提高了效率,但不牺牲公平性.
科学领域:
- 运营研究 运营研究
- 医疗保健管理的管理
- 机器学习 机器学习
背景情况:
- 有效的预约安排 (AS) 对门诊效率至关重要.
- 传统的AS方法与患者服务时间异质性作斗争.
- 现有的机器学习 (ML) 方法对AS提出实际实施挑战.
研究的目的:
- 开发一个数据驱动的决策支持系统,用于预约安排.
- 整合监督和无监督的ML,以改善患者分组和安排.
- 通过一种新的,可适应的方法来解决AS中的服务时间异质性.
主要方法:
- 拟议的集群预测计划 (CPS),一个结合监督和无监督ML的系统.
- 利用数据驱动的方法对患者进行分组,超越预先定义的规则.
- 开发了一种通用算法,用于为各种患者组生成可适应的预约模板.
主要成果:
- 与传统方法相比,CPS显著降低了成本.
- 与第一电话,第一约会 (FCFA) 计划相比,实现了高达15.0%的成本降低.
- 与使用传统测序候选者 (TSC) 规则进行新/返回分类相比,保证了超过4.7%的节省.
结论:
- 拟议的CPS系统有效地平衡了个人层面的异质性与实际的调度需求.
- CPS提高了门诊患者的运营效率,并降低了成本.
- 该系统可以改善预约安排,而不会影响公平性.
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