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Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust

OBJECTIVES: The main objective of the study was to develop more accurate and precise short-term forecasting models for admissions and bed occupancy for an NHS Trust located in Bristol, England. Subforecasts for the medical and surgical specialties, and for different lengths of stay were realised DES...

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Autores principales: Eyles, Emily, Redaniel, Maria Theresa, Jones, Tim, Prat, Marion, Keen, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021768/
https://www.ncbi.nlm.nih.gov/pubmed/35443953
http://dx.doi.org/10.1136/bmjopen-2021-056523
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author Eyles, Emily
Redaniel, Maria Theresa
Jones, Tim
Prat, Marion
Keen, Tim
author_facet Eyles, Emily
Redaniel, Maria Theresa
Jones, Tim
Prat, Marion
Keen, Tim
author_sort Eyles, Emily
collection PubMed
description OBJECTIVES: The main objective of the study was to develop more accurate and precise short-term forecasting models for admissions and bed occupancy for an NHS Trust located in Bristol, England. Subforecasts for the medical and surgical specialties, and for different lengths of stay were realised DESIGN: Autoregressive integrated moving average models were specified on a training dataset of daily count data, then tested on a 6-week forecast horizon. Explanatory variables were included in the models: day of the week, holiday days, lagged temperature and precipitation. SETTING: A secondary care hospital in an NHS Trust in South West England. PARTICIPANTS: Hospital admissions between September 2016 and March 2020, comprising 1291 days. PRIMARY AND SECONDARY OUTCOME MEASURES: The accuracy of the forecasts was assessed through standard measures, as well as compared with the actual data using accuracy thresholds of 10% and 20% of the mean number of admissions or occupied beds. RESULTS: The overall Autoregressive Integrated Moving Average (ARIMA) admissions forecast was compared with the Trust’s forecast, and found to be more accurate, namely, being closer to the actual value 95.6% of the time. Furthermore, it was more precise than the Trust’s. The subforecasts, as well as those for bed occupancy, tended to be less accurate compared with the overall forecasts. All of the explanatory variables improved the forecasts. CONCLUSIONS: ARIMA models can forecast non-elective admissions in an NHS Trust accurately on a 6-week horizon, which is an improvement on the current predictive modelling in the Trust. These models can be readily applied to other contexts, improving patient flow.
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spelling pubmed-90217682022-05-04 Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust Eyles, Emily Redaniel, Maria Theresa Jones, Tim Prat, Marion Keen, Tim BMJ Open Epidemiology OBJECTIVES: The main objective of the study was to develop more accurate and precise short-term forecasting models for admissions and bed occupancy for an NHS Trust located in Bristol, England. Subforecasts for the medical and surgical specialties, and for different lengths of stay were realised DESIGN: Autoregressive integrated moving average models were specified on a training dataset of daily count data, then tested on a 6-week forecast horizon. Explanatory variables were included in the models: day of the week, holiday days, lagged temperature and precipitation. SETTING: A secondary care hospital in an NHS Trust in South West England. PARTICIPANTS: Hospital admissions between September 2016 and March 2020, comprising 1291 days. PRIMARY AND SECONDARY OUTCOME MEASURES: The accuracy of the forecasts was assessed through standard measures, as well as compared with the actual data using accuracy thresholds of 10% and 20% of the mean number of admissions or occupied beds. RESULTS: The overall Autoregressive Integrated Moving Average (ARIMA) admissions forecast was compared with the Trust’s forecast, and found to be more accurate, namely, being closer to the actual value 95.6% of the time. Furthermore, it was more precise than the Trust’s. The subforecasts, as well as those for bed occupancy, tended to be less accurate compared with the overall forecasts. All of the explanatory variables improved the forecasts. CONCLUSIONS: ARIMA models can forecast non-elective admissions in an NHS Trust accurately on a 6-week horizon, which is an improvement on the current predictive modelling in the Trust. These models can be readily applied to other contexts, improving patient flow. BMJ Publishing Group 2022-04-19 /pmc/articles/PMC9021768/ /pubmed/35443953 http://dx.doi.org/10.1136/bmjopen-2021-056523 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology
Eyles, Emily
Redaniel, Maria Theresa
Jones, Tim
Prat, Marion
Keen, Tim
Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title_full Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title_fullStr Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title_full_unstemmed Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title_short Can we accurately forecast non-elective bed occupancy and admissions in the NHS? A time-series MSARIMA analysis of longitudinal data from an NHS Trust
title_sort can we accurately forecast non-elective bed occupancy and admissions in the nhs? a time-series msarima analysis of longitudinal data from an nhs trust
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9021768/
https://www.ncbi.nlm.nih.gov/pubmed/35443953
http://dx.doi.org/10.1136/bmjopen-2021-056523
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