利用机器学习来确定医院运营管理中的关键措施:一项回顾性研究,探索四种常见算法的可行性和性能
Wantao Zhang1, Yan Zhu2, Liqun Tong3
1Department of Operation Management, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, #141 Tianjin Road, Huangshi Port District, Huangshi, 435000, Hubei Province, China.
BMC medical informatics and decision making
|October 4, 2024
概括
机器学习 (ML) 可以识别关键的医院财务措施,随机森林 (RF) 的表现最好. 然而,ML技术尚未有效地确定医院运营管理中的护理质量措施.
科学领域:
- 医疗保健管理的管理
- 运营研究 运营研究
- 数据科学数据科学数据科学
背景情况:
- 运营管理措施对于医院绩效评估至关重要.
- 更新这些措施对于与不断变化的趋势和目标保持一致至关重要.
- 机器学习 (ML) 提供了增强措施识别的潜力,但需要进一步调查.
研究的目的:
- 评估常用ML技术的可行性和有效性,以确定和更新关键的医院运营管理措施.
- 为了比较线性模型 (LM),随机森林 (RF),部分最小平方 (PLS) 和神经网络 (NN) 的性能.
主要方法:
- 利用了48个月的历史数据,对来自中国区域卫生系统的43项财政和护理质量措施进行了分析.
- 应用了四种ML技术 (LM,RF,PLS,NN) 来确定重要的措施.
- 验证的ML识别的措施与人类决定的战略措施对比.
主要成果:
- 随机森林 (RF) 和部分最小方形 (PLS) 在确定像住院收入这样的财务措施方面表现相对一致.
- 在确定关键措施方面,ML技术从一年到一年有所不同,特别是在护理质量方面.
- ML识别的财务措施部分与人类决策保持一致,但护理质量措施没有.
结论:
- ML是确定医院关键运营措施的可行工具,特别是在财务平衡方面.
- 随机森林 (RF) 在金融措施中表现最强.
- 目前的ML技术对于识别护理质量措施是不有效的,建议使用它们作为决策支持工具.
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