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. |
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| Authors: | Eyles E; The National Institute for Health Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK emily.eyles@bristol.ac.uk.; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.; Redaniel MT; The National Institute for Health Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.; Jones T; The National Institute for Health Research Applied Research Collaboration West (NIHR ARC West) at University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK.; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.; Prat M; School of Economics, Faculty of Social Sciences and Law, University of Bristol, Bristol, UK.; Keen T; North Bristol NHS Trust, Westbury on Trym, Bristol, UK. |
| Source: | BMJ open [BMJ Open] 2022 Apr 20; Vol. 12 (4), pp. e056523. Date of Electronic Publication: 2022 Apr 20. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Language: | English |
| Journal Info: | Publisher: BMJ Publishing Group Ltd Country of Publication: England NLM ID: 101552874 Publication Model: Electronic Cited Medium: Internet ISSN: 2044-6055 (Electronic) Linking ISSN: 20446055 NLM ISO Abbreviation: BMJ Open Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [London] : BMJ Publishing Group Ltd, 2011- |
| MeSH Terms: | Bed Occupancy* ; State Medicine*; England ; Forecasting ; Hospitalization ; Humans |
| Abstract: | 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.; (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.) |
| Competing Interests: | Competing interests: None declared. |
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| Contributed Indexing: | Keywords: ACCIDENT & EMERGENCY MEDICINE; EPIDEMIOLOGY; HEALTH SERVICES ADMINISTRATION & MANAGEMENT; STATISTICS & RESEARCH METHODS |
| Entry Date(s): | Date Created: 20220421 Date Completed: 20220422 Latest Revision: 20220716 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC9021768 |
| DOI: | 10.1136/bmjopen-2021-056523 |
| PMID: | 35443953 |
| Database: | MEDLINE |
Journal Article; Research Support, Non-U.S. Gov't