RT Journal Article
SR Electronic
T1 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
JF BMJ Open
JO BMJ Open
FD British Medical Journal Publishing Group
SP e056523
DO 10.1136/bmjopen-2021-056523
VO 12
IS 4
A1 Emily Eyles
A1 Maria Theresa Redaniel
A1 Tim Jones
A1 Marion Prat
A1 Tim Keen
YR 2022
UL http://bmjopen.bmj.com/content/12/4/e056523.abstract
AB 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 realisedDesign 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.Data may be obtained from a third party and are not publicly available. The data used in the study are collected by the NBT as part of their care and support. Sharing of anonymised data with the University of Bristol was underpinned by a data sharing agreement and solely covers the purposes of this study. Data requests can be made through the NBT.