MetSim: a Hospital Simulation Support Tool Using Meteorological Information to Improve the Planning and Management of Health Services

MetSim:利用气象信息改进医疗服务规划和管理的医院模拟支持工具

基本信息

  • 批准号:
    EP/H010637/1
  • 负责人:
  • 金额:
    $ 43.5万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2010
  • 资助国家:
    英国
  • 起止时间:
    2010 至 无数据
  • 项目状态:
    已结题

项目摘要

The ability to predict weather offers the potential to provide valuable information that can be used in planning health services. For example, imagine a hospital planning system that was able to predict fluctuations in demand for different services as a consequence of predictions of meteorological events such as the early February cold spell in 2009. Such a tool would result in a substantial benefit to both the NHS and health outcomes. Specifically, appropriate use of meteorological intelligence would help to:1. Improve health by reducing morbidity and mortality rates, and generally improving health outcomes.2. Improve access to services by better predicting hospital pressures due to weather events, thus allowing for hospital mangers to anticipate and better prepare for such fluctuations. This research will explore and quantify the relationship between weather patterns and extreme weather events, and their impact on various health conditions, such a heart attacks, stroke, asthma, and fractures. For example, it is thought that a sudden surge in cold temperature can cause blood to thicken slightly and blood pressure to increase, which can trigger a heart attack or stroke in vulnerable patients. There are many other reported (observed) trends such as thunderstorm-related asthma. A thunderstorm in South East England, for example, saw 640 patients presenting with severe asthma to hospital, ten times the usual number. By linking weather and health in this way, we can help save lives or minimise the risk of morbidity by creating an early warning system that can ensure at-risk patients are well informed and have sufficient medication and advice. Furthermore, this research will utilise computer simulation techniques and statistical models and apply their use to create a novel hospital operational capacity support tool (MetSim) that will utilise meteorological forecasts alongside NHS hospital data to provide information to hospitals on expected levels of emergency admissions and to alert them of sudden surges in demand and daily fluctuations. By forecasting demand in this manner, MetSim will allow hospital managers to understand more closely resulting resource needs over the short-term planning horizon and assist in planning decisions such as cancellation of elective admissions. Given that the provision of hospital resources is a matter of considerable public and political concern and has been the subject of widespread debate, this research will help the NHS more effectively and efficiently plan and manage their health services.A further benefit of MetSim is that it can act as a public health warning system. Health-weather correlations could be used by regional Strategic Health Authorities or Primary Care Trusts to alert at-risk populations. This could have significant public health benefits by ensuring such people are better informed about the forthcoming risks and have sufficient medication and appropriate medical advice. Treating patients for the health conditions evaluated in this research (to include heart disease, stroke, acute bronchitis, fractures and pneumonia) accounts for a significant proportion of the NHS budget. For example, stroke and heart disease incidence in the UK is amongst the highest in the world and these two conditions alone cost the NHS an estimated 18.3 billion annually. Using MetSim to prevent hospital admissions or improving health outcomes for even a small percentage of these patients could result in significant costs savings to NHS Trusts.This novel and valuable research involves a collaborative team of specialists in Operational Research and Statistics, with co-operation and support of the Met Office, Southampton University Hospital NHS Trust, and the Cardiff and Vale NHS Trust. The level of support, commitment and excitement about this research from these three organisations is such that between them they have pledged 60,000 towards the costs of the overall project.
预测天气的能力提供了提供可用于规划卫生服务的有价值信息的潜力。例如,想象一个医院规划系统,它能够预测不同服务需求的波动,这是预测气象事件(如2009年2月初的寒流)的结果。这样一个工具将对国民保健制度和健康结果产生重大益处。具体而言,适当使用气象情报将有助于:1。通过降低发病率和死亡率以及普遍改善健康结果来改善健康状况。通过更好地预测天气事件对医院造成的压力,从而使医院管理人员能够预测并更好地为这种波动做好准备,从而改善服务的可及性。这项研究将探索和量化天气模式和极端天气事件之间的关系,以及它们对各种健康状况的影响,如心脏病发作,中风,哮喘和骨折。例如,人们认为寒冷温度的突然上升会导致血液轻微流动和血压升高,这可能会引发脆弱患者的心脏病发作或中风。还有许多其他报告(观察到)的趋势,如雷暴相关的哮喘。例如,在英格兰东南部的一场雷暴中,有640名严重哮喘患者被送往医院,是平时的十倍。通过以这种方式将天气与健康联系起来,我们可以帮助拯救生命或通过创建一个早期预警系统来最大限度地降低发病风险,该系统可以确保处于危险中的患者获得充分的信息,并获得足够的药物和建议。此外,这项研究将利用计算机模拟技术和统计模型,并将其用于创建一个新的医院运营能力支持工具(MetSim),该工具将利用气象预报以及NHS医院数据,为医院提供有关急诊入院预期水平的信息,并提醒他们需求和日常波动的突然激增。通过以这种方式预测需求,MetSim将使医院管理者能够更密切地了解短期规划范围内的资源需求,并协助规划决策,如取消择期入院。鉴于医院资源的提供是一个相当大的公众和政治关注的问题,并已被广泛讨论的主题,这项研究将有助于NHS更有效地规划和管理他们的健康服务。MetSim的另一个好处是,它可以作为一个公共卫生预警系统。区域战略卫生当局或初级保健信托基金可以使用健康-天气相关性来警告高危人群。这可以确保这些人更好地了解即将到来的风险,并获得足够的药物和适当的医疗建议,从而产生重大的公共卫生效益。在这项研究中,治疗患者的健康状况(包括心脏病、中风、急性支气管炎、骨折和肺炎)占国民保健服务预算的很大一部分。例如,英国的中风和心脏病发病率是世界上最高的,仅这两种疾病每年就花费NHS估计183亿英镑。使用MetSim来防止入院或改善这些患者的健康结果,即使是一小部分,也可以为NHS Trusts.This新颖而有价值的研究涉及运营研究和统计专家的协作团队,与英国气象局,南安普顿大学医院NHS信托基金,以及卡迪夫和淡水河谷NHS信托基金的合作和支持。这三个组织对这项研究的支持、承诺和兴奋程度是如此之高,以至于他们已经承诺为整个项目的费用提供6万美元。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
MetSim: A Simulation Support Tool Using Meteorological Information to Improve the Planning and Management of Hospital Services
MetSim:利用气象信息改进医院服务规划和管理的模拟支持工具
MetSim: A Simulation Tool for Short-Term Planning of Hospital Services
MetSim:医院服务短期规划的模拟工具
A hierarchical Bayesian model for improving short-term forecasting of hospital demand by including meteorological information
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Paul Harper其他文献

Vibration-based damage detection of offshore wind turbine foundations under environmental and operational variations
基于振动的海上风力涡轮机基础在环境和运行变化下的损伤检测
  • DOI:
    10.1016/j.ymssp.2025.112868
  • 发表时间:
    2025-08-15
  • 期刊:
  • 影响因子:
    8.900
  • 作者:
    Yulong Zhang;John H.G. Macdonald;Song Liu;Paul Harper;Zhilin Xue
  • 通讯作者:
    Zhilin Xue
Capacity Planning of Virtual Wards for Frail and Elderly Patients
体弱老年患者虚拟病房容量规划
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Harriet Grange;G. Johns;Alka Ahuja;Paul Harper;Elizabeth Williams;Daniel Gartner
  • 通讯作者:
    Daniel Gartner
Idiosyncrasy to ammoniated mercury ointment: Report of two cases
  • DOI:
    10.1016/s0022-3476(34)80072-8
  • 发表时间:
    1934-12-01
  • 期刊:
  • 影响因子:
  • 作者:
    Paul Harper
  • 通讯作者:
    Paul Harper
The rapid whole blood agglutination d-dimer assay has poor sensitivity for use as an exclusion test in suspected deep vein thrombosis.
快速全血凝集 D-二聚体测定作为疑似深静脉血栓形成的排除试验灵敏度较差。
  • DOI:
  • 发表时间:
    2001
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Paul Harper;Catherine Marson;Audrey Grimmer;Karen Monahan;Gillian Humm;Bart Baker
  • 通讯作者:
    Bart Baker
Observations on insect-borne diseases in the South Pacific campaign
  • DOI:
    10.1016/s0022-3476(47)80255-0
  • 发表时间:
    1947-12-01
  • 期刊:
  • 影响因子:
  • 作者:
    Paul Harper
  • 通讯作者:
    Paul Harper

Paul Harper的其他文献

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{{ truncateString('Paul Harper', 18)}}的其他基金

Modelling Emergency Medical Services in Indonesia
印度尼西亚紧急医疗服务建模
  • 批准号:
    EP/T003197/1
  • 财政年份:
    2019
  • 资助金额:
    $ 43.5万
  • 项目类别:
    Research Grant
Applications of ultra-long Raman lasers in Photonics
超长拉曼激光器在光子学中的应用
  • 批准号:
    EP/E015646/1
  • 财政年份:
    2007
  • 资助金额:
    $ 43.5万
  • 项目类别:
    Research Grant

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重新利用氟马西尼用于院前肌肉注射治疗因娱乐性药物过量而导致的昏迷
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Transforming the future use of injectable medicines outside the hospital: Increasing capacity in the NHS
改变未来医院外注射药物的使用:提高 NHS 的能力
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    2024
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Open Access Block Award 2024 - Kings College Hospital NHS Foundation Trust
2024 年开放访问区块奖 - 国王学院医院 NHS 基金会信托
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    EP/Z532940/1
  • 财政年份:
    2024
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    $ 43.5万
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Sensing Spaces of Healthcare: Rethinking the NHS Hospital
感知医疗保健空间:重新思考 NHS 医院
  • 批准号:
    MR/X022447/1
  • 财政年份:
    2024
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    Fellowship
Causal inference of oral and general health using multiple large cohorts, NDB, and hospital data
使用多个大型队列、NDB 和医院数据对口腔和一般健康状况进行因果推断
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改善无家可归者出院情况:嵌入阶梯式楔形设计的质量改进项目
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