Described in Nature Digital Medicine, the tool estimates how many hospital beds will be needed by looking at live data of patients who have arrived at the hospital’s emergency department.
In the study, the research team showed that the tool was more accurate than the conventional benchmark used by planners, based on the average number of beds needed on the same day of the week for the previous six weeks.
The tool, which also accounts for patients yet to arrive at hospital, provides more detailed information than the conventional method. Instead of a single figure prediction for the day overall, the tool includes a probability distribution for how many beds will be needed in four and eight hours’ time and provides its forecasts four times a day, which is emailed to hospital planners.
Researchers are now working with UCLH on refining the models so that they can estimate how many beds will be needed in different areas of the hospital, for example beds on medical wards or surgical wards.
Lead author Dr Zella King, UCL Clinical Operational Research Unit and the UCL Institute of Health Informatics, said: “Our AI models provide a much richer picture about the likely demand on beds throughout the course of the day. They make use of patient data the instant this data is recorded.
“We hope this can help planners to manage patient flow – a complex task that involves balancing planned-for patients with emergency admissions. This is important in reducing the number of cancelled surgeries and in ensuring high-quality care.”
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The researchers said they trained 12 machine learning models using patient data recorded at UCLH between May 2019 and July 2021.
These models assessed each patient’s probability of being admitted to the hospital from the emergency department based on data ranging from age and how the patient arrived in hospital, to test results and number of consultations. It combined these probabilities for an overall estimate of the number of beds needed.
They then compared the models’ predictions to actual admissions between May 2019 to March 2020. They found that they outperformed the conventional method with central predictions an average of four admissions off the actual figure, compared to the conventional method’s average of 6.5 admissions out.
After Covid hit, researchers adapted the models to take account of significant variations both in the numbers of people arriving and the amount of time they spent in the emergency department.
Alison Clements, head of Operations, Patient Flow & Emergency Preparedness, Resilience & Response at UCLH said that the tool will be ‘hugely valuable’.
“Our next step is to start using the predictions in daily flow huddles,” Clements said. “We look forward to continuing work with UCL to refine the tool and expand its predictive power across the hospital.”
The work was funded by grants from the UCL and Partner Hospitals Wellcome Institutional Strategic Support Fund (ISSF) and the NIHR UCLH Biomedical Research Centre. Several contributing authors were funded by the National Institute for Health Research and NHSX.
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