Center for Medical Informatics, The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, UK.
School of Computer Science, University of St Andrews, St Andrews, UK.
BMJ Health Care Inform. 2024 Aug 19;31(1):e101096. doi: 10.1136/bmjhci-2024-101096.
This project aims to determine the feasibility of predicting future critical care bed availability using data-driven computational forecast modelling and routinely collected hospital bed management data.
In this proof-of-concept, single-centre data informatics feasibility study, regression-based and classification data science techniques were applied retrospectively to prospectively collect routine hospital-wide bed management data to forecast critical care bed capacity. The availability of at least one critical care bed was forecasted using a forecast horizon of 1, 7 and 14 days in advance.
We demonstrated for the first time the feasibility of forecasting critical care bed capacity without requiring detailed patient-level data using only routinely collected hospital bed management data and interpretable models. Predictive performance for bed availability 1 day in the future was better than 14 days (mean absolute error 1.33 vs 1.61 and area under the curve 0.78 vs 0.73, respectively). By analysing feature importance, we demonstrated that the models relied mainly on critical care and temporal data rather than data from other wards in the hospital.
Our data-driven forecasting tool only required hospital bed management data to forecast critical care bed availability. This novel approach means no patient-sensitive data are required in the modelling and warrants further work to refine this approach in future bed availability forecast in other hospital wards.
Data-driven critical care bed availability prediction was possible. Further investigations into its utility in multicentre critical care settings or in other clinical settings are warranted.
本项目旨在通过数据驱动的计算预测模型和常规收集的医院床位管理数据，确定预测未来重症监护床位可用性的可行性。
在这项概念验证、单中心数据信息学可行性研究中，应用了基于回归和分类的数据科学技术，回顾性地收集了前瞻性的全院床位管理数据，以预测重症监护床位容量。使用 1 天、7 天和 14 天的预测提前期预测至少有一张重症监护床位的可用性。
我们首次证明了仅使用常规收集的医院床位管理数据和可解释模型预测重症监护床位容量的可行性，而无需详细的患者水平数据。未来 1 天床位可用性的预测性能优于 14 天（平均绝对误差分别为 1.33 和 1.61，曲线下面积分别为 0.78 和 0.73）。通过分析特征重要性，我们证明模型主要依赖于重症监护和时间数据，而不是来自医院其他病房的数据。
我们的基于数据的预测工具仅需要医院床位管理数据来预测重症监护床位的可用性。这种新方法意味着在建模中不需要患者敏感数据，并值得进一步研究，以在未来的其他医院病房床位可用性预测中改进这种方法。
重症监护床位可用性的预测是可能的。需要进一步调查其在多中心重症监护环境或其他临床环境中的实用性。
