Shah Kanan, Sharma Akarsh, Moulton Chris, Swift Simon, Mann Clifford, Jones Simon
NYU Grossman School of Medicine, New York, NY, United States.
Icahn School of Medicine at Mount Sinai, New York, NY, United States.
JMIR Med Inform. 2021 Sep 30;9(9):e21990. doi: 10.2196/21990.
Over the last decade, increasing numbers of emergency department attendances and an even greater increase in emergency admissions have placed severe strain on the bed capacity of the National Health Service (NHS) of the United Kingdom. The result has been overcrowded emergency departments with patients experiencing long wait times for admission to an appropriate hospital bed. Nevertheless, scheduling issues can still result in significant underutilization of bed capacity. Bed occupancy rates may not correlate well with bed availability. More accurate and reliable long-term prediction of bed requirements will help anticipate the future needs of a hospital's catchment population, thus resulting in greater efficiencies and better patient care.
This study aimed to evaluate widely used automated time-series forecasting techniques to predict short-term daily nonelective bed occupancy at all trusts in the NHS. These techniques were used to develop a simple yet accurate national health system-level forecasting framework that can be utilized at a low cost and by health care administrators who do not have statistical modeling expertise.
Bed occupancy models that accounted for patterns in occupancy were created for each trust in the NHS. Daily nonelective midnight trust occupancy data from April 2011 to March 2017 for 121 NHS trusts were utilized to generate these models. Forecasts were generated using the three most widely used automated forecasting techniques: exponential smoothing; Seasonal Autoregressive Integrated Moving Average; and Trigonometric, Box-Cox transform, autoregressive moving average errors, and Trend and Seasonal components. The NHS Modernisation Agency's recommended forecasting method prior to 2020 was also replicated.
The accuracy of the models varied on the basis of the season during which occupancy was forecasted. For the summer season, percent root-mean-square error values for each model remained relatively stable across the 6 forecasted weeks. However, only the trend and seasonal components model (median error=2.45% for 6 weeks) outperformed the NHS Modernisation Agency's recommended method (median error=2.63% for 6 weeks). In contrast, during the winter season, the percent root-mean-square error values increased as we forecasted further into the future. Exponential smoothing generated the most accurate forecasts (median error=4.91% over 4 weeks), but all models outperformed the NHS Modernisation Agency's recommended method prior to 2020 (median error=8.5% over 4 weeks).
It is possible to create automated models, similar to those recently published by the NHS, which can be used at a hospital level for a large national health care system to predict nonelective bed admissions and thus schedule elective procedures.
在过去十年中，急诊科就诊人数不断增加，急诊入院人数的增长幅度更大，这给英国国民医疗服务体系（NHS）的床位容量带来了巨大压力。结果导致急诊科人满为患，患者等待入住合适医院床位的时间很长。然而，排班问题仍可能导致床位容量的严重未充分利用。床位占用率可能与床位可用性的相关性不佳。更准确可靠的床位需求长期预测将有助于预测医院服务人群的未来需求，从而提高效率并改善患者护理。
本研究旨在评估广泛使用的自动时间序列预测技术，以预测NHS所有信托机构的短期每日非选择性床位占用情况。这些技术用于开发一个简单而准确的国家卫生系统层面的预测框架，该框架可以低成本使用，并且可供没有统计建模专业知识的医疗管理人员使用。
为NHS的每个信托机构创建了考虑占用模式的床位占用模型。利用2011年4月至2017年3月期间121个NHS信托机构的每日非选择性午夜信托床位占用数据来生成这些模型。使用三种最广泛使用的自动预测技术进行预测：指数平滑法；季节性自回归积分移动平均法；以及三角、Box-Cox变换、自回归移动平均误差和趋势与季节性成分法。还复制了NHS现代化机构在2020年之前推荐的预测方法。
模型的准确性因预测占用情况的季节而异。对于夏季，每个模型的均方根误差百分比值在预测的6周内保持相对稳定。然而，只有趋势和季节性成分模型（6周的中位数误差=2.45%）优于NHS现代化机构推荐的方法（6周的中位数误差=2.63%）。相比之下，在冬季期间，随着我们对未来的进一步预测，均方根误差百分比值增加。指数平滑法产生了最准确的预测（4周内中位数误差=4.91%），但所有模型都优于NHS现代化机构在2020年之前推荐的方法（4周内中位数误差=8.5%）。
有可能创建类似于NHS最近发布的自动模型，这些模型可在医院层面用于大型国家医疗保健系统，以预测非选择性床位入院情况，从而安排择期手术。
