This project aims to utilise live care data to forecast emergency hospital admissions, demand for beds and theatres, and to plan elective surgery accordingly. This will enable trusts to better plan their resource allocations and reduce A&E crowding.
NHS acute hospital trusts face challenges in planning hospital bed capacity, especially in winter months when patient demand for hospital care is high. Hospital service managers need to be able to plan ahead, and proactively monitor demand for hospital beds and operating theatres.
Efficient allocation of beds, staff and funding has become an urgent issue for many hospital administrations, particularly due to the rising number of people admitted as emergency cases. The increase in the number of unpredicted hospital admissions has led to the cancellation of planned procedures and operations, causing worry and anxiety to patients. It also causes increased pressure on hospitals as they cope with rescheduled procedures after periods of high demand.
Care computer systems containing live information relating to patients, staffing, theatres and beds could be used to manage patient demand for hospital beds. These data can be used to forecast emergency hospital admissions, demand for beds and theatres, and to plan elective surgery accordingly. The team will use existing local datasets and methodologies from economics and statistics, to develop algorithms that will predict patient demand for hospital beds.
The Impact and Outcomes
Improved modelling of demand will have a direct impact on the business planning of acute NHS Trusts. By understanding patient volume, trusts can plan what the optimal resource allocation to meet that demand, including the number of beds required, the workforce and the theatre time on any given day. This in turn will provide insight into the remaining capacity available for elective work.
Improving patient flow through hospitals greatly improves the working and care environment, reduces A&E crowding, avoids last-minute cancellations of elective procedures and enables hospitals to provide the most appropriate care for patients.
Better Care Loop
Prof Ashley Blom, Professor of Orthopaedic Surgery and Head of Bristol Medical School
Prof Andrew Judge, Professor of Translational Statistics, Department of Translational Health Sciences, Bristol Medical School, University of Bristol
COVID-19 mortality risk for inflammatory arthritis patients: a cohort study using SAIL Databank
24 November 2022
A study of inflammatory arthritis (IA) patients found that shielding reduced the incidence of COVID-19. IA was not associated with an increased risk of dying within 28 days, but being vulnerable...
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
1 July 2022
Hospitals need to be able to predict their capacity for admitting patients when planning elective surgeries. Researchers funded by HDR UK developed a new model for making forecasts that were more...
A population-based cohort study of obesity, ethnicity and COVID-19 mortality in 12.6 million adults in England
21 June 2022
Obesity dramatically increases the risk of death from COVID-19 but, the extent of this risk across different body weights and ethnic groups was not clear. Researchers using health and Census...