同时使用静态和动态信息预测医院病房和病房占用率:回顾性单中心队列研究
Hyeram Seo1, Imjin Ahn2, Hansle Gwon2
1Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center & University of Ulsan College of Medicine, Seoul, Republic of Korea.
JMIR medical informatics
|March 21, 2024
概括
这项研究开发了高性能预测模型,用于病房和房间级别的医院床位占用率 (BOR). 基于Web的仪表板可视化这些预测,帮助管理人员优化医院资源管理.
科学领域:
- 医疗保健管理的管理
- 数据科学在医学中的数据科学
- 医院运营研究 医院运营研究
背景情况:
- 准确预测医院床位占用率 (BOR) 对资源管理,预算和患者护理至关重要.
- 虽然医院范围内的BOR预测很重要,但针对特定病房和房间的细粒度预测为调度提供了更大的实际实用性.
研究的目的:
- 为医院管理人员开发一个基于网络的工具,以便在不同的时间框架内在病房和病房层面预测BOR.
- 为改善医院病床管理和资源分配提供可操作的见解.
主要方法:
- 时间序列预测使用长短期记忆 (LSTM) 网络训练在每小时的床位数据.
- 开发病房级模型,使用7天和30天的窗口,以及房间级模型,使用3天和7天的窗口.
- 整合静态 (房间特定) 和动态数据以提高预测准确性,使用LSTM和双向LSTM (Bi-LSTM).
主要成果:
- 双向LSTM (Bi-LSTM) 模型表现出高于标准LSTM的性能.
- 病房级别的模型实现了0.544的R平方 (MAE:0.067,MSE:0.009,RMSE:0.094).
- 包含静态数据的房间级模型实现了0.600的R平方 (MAE:0.129,MSE:0.050,RMSE:0.227).
结论:
- 成功开发了病房和房间级BOR的高性能预测模型.
- 基于Web的仪表板可以可视化预测,支持高效的床运营规划.
- 这些模型有助于优化医院资源,减少整体资源消耗.
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