Forecasting emergency department arrivals using INGARCH models
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 pati...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Servizo Galego de Saúde (SERGAS) |
| Repositorio: | RUNA. Repositorio da Consellería de Sanidade e Sergas |
| OAI Identifier: | oai:runa.sergas.gal:20.500.11940/21511 |
| Acceso en línea: | https://portalcientifico.sergas.gal//documentos/6550da0392517a5a7db94df1 http://hdl.handle.net/20.500.11940/21511 |
| Access Level: | acceso abierto |
| Palabra clave: | AS Santiago CHUS |
| Sumario: | 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. |
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