Juan C Reboredo, Jose Ramon Barba-Queiruga, Javier Ojea-Ferreiro, Francisco Reyes-Santias
{"title":"Forecasting emergency department arrivals using INGARCH models.","authors":"Juan C Reboredo, Jose Ramon Barba-Queiruga, Javier Ojea-Ferreiro, Francisco Reyes-Santias","doi":"10.1186/s13561-023-00456-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments.</p><p><strong>Objective: </strong>We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department.</p><p><strong>Material and methods: </strong>We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals.</p><p><strong>Results: </strong>We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals.</p><p><strong>Conclusion: </strong>Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.</p>","PeriodicalId":46936,"journal":{"name":"Health Economics Review","volume":"13 1","pages":"51"},"PeriodicalIF":2.7000,"publicationDate":"2023-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10612291/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health Economics Review","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1186/s13561-023-00456-5","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Forecasting patient arrivals to hospital emergency departments is critical to dealing with surges and to efficient planning, management and functioning of hospital emerency departments.
Objective: We explore whether past mean values and past observations are useful to forecast daily patient arrivals in an Emergency Department.
Material and methods: We examine whether an integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model can yield a better conditional distribution fit and forecast of patient arrivals by using past arrival information and taking into account the dynamics of the volatility of arrivals.
Results: We document that INGARCH models improve both in-sample and out-of-sample forecasts, particularly in the lower and upper quantiles of the distribution of arrivals.
Conclusion: Our results suggest that INGARCH modelling is a useful model for short-term and tactical emergency department planning, e.g., to assign rotas or locate staff for unexpected surges in patient arrivals.
期刊介绍:
Health Economics Review is an international high-quality journal covering all fields of Health Economics. A broad range of theoretical contributions, empirical studies and analyses of health policy with a health economic focus will be considered for publication. Its scope includes macro- and microeconomics of health care financing, health insurance and reimbursement as well as health economic evaluation, health services research and health policy analysis. Further research topics are the individual and institutional aspects of health care management and the growing importance of health care in developing countries.