{"title":"紧急部门的主动服务恢复:使用预测和实时模拟的混合建模方法","authors":"A. Harper, N. Mustafee","doi":"10.1145/3316480.3322892","DOIUrl":null,"url":null,"abstract":"This work in progress is an application of a hybrid modelling (HM) approach for short-term decision support in urgent and emergency healthcare. It uses seasonal ARIMA time-series forecasting to predict emergency department (ED) overcrowding in a near-future moving window (1-4 hours) using data downloaded from a digital platform (NHSquicker). NHSquicker delivers near real-time wait times from multiple centres of urgent care in the South-West of England. Alongside historical distributions, this near real-time data is used to populate an ED discrete event simulation model. The ARIMA forecasts trigger real-time simulation experimentation of ED scenarios including proactive diversion of low-acuity patients to alternative facilities in the urgent care network.","PeriodicalId":398793,"journal":{"name":"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"514 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Proactive Service Recovery in Emergency Departments: A Hybrid Modelling Approach using Forecasting and Real-Time Simulation\",\"authors\":\"A. Harper, N. Mustafee\",\"doi\":\"10.1145/3316480.3322892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work in progress is an application of a hybrid modelling (HM) approach for short-term decision support in urgent and emergency healthcare. It uses seasonal ARIMA time-series forecasting to predict emergency department (ED) overcrowding in a near-future moving window (1-4 hours) using data downloaded from a digital platform (NHSquicker). NHSquicker delivers near real-time wait times from multiple centres of urgent care in the South-West of England. Alongside historical distributions, this near real-time data is used to populate an ED discrete event simulation model. The ARIMA forecasts trigger real-time simulation experimentation of ED scenarios including proactive diversion of low-acuity patients to alternative facilities in the urgent care network.\",\"PeriodicalId\":398793,\"journal\":{\"name\":\"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation\",\"volume\":\"514 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3316480.3322892\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316480.3322892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Proactive Service Recovery in Emergency Departments: A Hybrid Modelling Approach using Forecasting and Real-Time Simulation
This work in progress is an application of a hybrid modelling (HM) approach for short-term decision support in urgent and emergency healthcare. It uses seasonal ARIMA time-series forecasting to predict emergency department (ED) overcrowding in a near-future moving window (1-4 hours) using data downloaded from a digital platform (NHSquicker). NHSquicker delivers near real-time wait times from multiple centres of urgent care in the South-West of England. Alongside historical distributions, this near real-time data is used to populate an ED discrete event simulation model. The ARIMA forecasts trigger real-time simulation experimentation of ED scenarios including proactive diversion of low-acuity patients to alternative facilities in the urgent care network.