Department of Operation Management, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, #141 Tianjin Road, Huangshi Port District, Huangshi, 435000, Hubei Province, China.
Center for Health Statistics and Information, National Health Commission of People's Republic of China, #1 Xizhimen Wainan Road, Xicheng District, Beijing, 100810, China.
BMC Med Inform Decis Mak. 2024 Oct 4;24(1):286. doi: 10.1186/s12911-024-02689-8.
Measures in operations management are pivotal for monitoring and assessing various aspects of hospital performance. Existing literature highlights the importance of regularly updating key management measures to reflect changing trends and organizational goals. Advancements in machine learning (ML) have presented promising opportunities for enhancing the process of updating operations management measures. However, their specific application and performance remain relatively unexplored. We aimed to investigate the feasibility and effectiveness of using common ML techniques to identify and update key measures in hospital operations management.
Historical data on 43 measures on financial balance and quality of care under 4 categories were retrieved from the BI system of a regional health system in Central China. The dataset included 17 surgical and 15 non-surgical departments over 48 months. Four common ML techniques, linear models (LM), random forest (RF), partial least squares (PLS), and neural networks (NN), were used to identify the most important measures. Ordinary least square was employed to investigate the impact of the top 10 measures. A ground truth validation compared the ML-identified key measures against the humanly decided strategic measures from annual meeting minutes.
For financial balancing, inpatient treatment revenue was an important measure in 3/4 years, followed by equipment depreciation costs. The measures identified using the same technique differed between years, though RF and PLS yielded relatively consistent results. For quality of care, none of the ML-identified measures repeated over the years. Those consistently important over four years differed almost entirely among four techniques. On ground truth validation, the 2016-2019 ML-identified measures were among the humanly identified measures, with the exception of equipment depreciation from the 2019 dataset. All the ML-identified measures for quality of care failed to coincide with the humanly decided measures.
Using ML to identify key hospital operational measures is viable but performance of ML techniques vary considerably. RF performs best among the four techniques in identifying key measures in financial balance. None of the ML techniques seem effective for identifying quality of care measures. ML is suggested as a decision support tool to remind and inspire decision-makers in certain aspects of hospital operations management.
运营管理中的措施对于监测和评估医院绩效的各个方面至关重要。现有文献强调了定期更新关键管理措施以反映不断变化的趋势和组织目标的重要性。机器学习（ML）的进步为增强运营管理措施的更新过程提供了有前景的机会。然而，它们的具体应用和性能仍然相对未知。我们旨在研究使用常见的 ML 技术来识别和更新医院运营管理中的关键措施的可行性和有效性。
从中国中部地区一个区域卫生系统的 BI 系统中检索了 4 个类别下的 43 个财务平衡和护理质量措施的历史数据。数据集包括 17 个外科和 15 个非外科科室，共 48 个月。使用四种常见的 ML 技术，即线性模型（LM）、随机森林（RF）、偏最小二乘法（PLS）和神经网络（NN），来识别最重要的措施。普通最小二乘法用于研究前 10 个措施的影响。一个实地真相验证将 ML 识别的关键措施与年度会议记录中人为决定的战略措施进行了比较。
对于财务平衡，住院治疗收入是 3/4 年内的一个重要措施，其次是设备折旧成本。使用相同技术识别的措施在不同年份有所不同，尽管 RF 和 PLS 产生了相对一致的结果。对于护理质量，ML 识别的措施没有一个多年来重复出现。在四年中始终重要的措施在四种技术之间几乎完全不同。在实地真相验证中，2016-2019 年 ML 识别的措施与人识别的措施在 2019 年的数据集除外。所有用于护理质量的 ML 识别的措施都与人为决定的措施不一致。
使用 ML 识别关键医院运营措施是可行的，但 ML 技术的性能差异很大。在识别财务平衡的关键措施方面，RF 在四种技术中表现最好。在识别护理质量措施方面，没有一种 ML 技术似乎有效。建议将 ML 作为决策支持工具，在医院运营管理的某些方面提醒和激励决策者。
