Hospital computer systems contain live information about patients, staffing, beds and operating theatres. This data could be used to predict emergency hospital admissions and demand for hospital beds for planned admissions. Hospital managers would be able to plan how to allocate resources in the longer term, making it easier to cope with surges in demand.
The project aims, by using existing local datasets, to develop algorithms to forecast the number of emergency admissions and demand for hospital beds for planned knee and hip replacement operations.
NHS hospitals need to allocate beds, staff and funding in the best way possible, with limited resources but increasing demand. The issue has become urgent for many hospitals, especially as the number of people admitted as emergencies is increasing.
A rise in emergency hospital admissions can lead to planned procedures and operations being cancelled, causing worry and anxiety for patients. It also puts more pressure on hospitals to cope with a backlog of procedures after periods of high demand.
Regulating patient flow through hospitals improves the working environment, reduces overcrowding in A&E and enables hospitals to provide better care for patients.
Dr Theresa Redaniel, Senior Lecturer in Health Services Research and Epidemiology at NIHR ARC West, said:
“We hope our algorithms will help NHS trusts to better plan resources over a longer period. Understanding demand allows trusts to plan capacity, including beds, staffing levels and operating theatre time. This would help them to manage surges in demand and could lead to fewer cancelled operations.”
This project is funded through the Health Data Research UK (HDR UK) Better Care South West Partnership.