AI Predicts Demand for Hospital Beds for Patients Coming Through Emergency Department

An artificial intelligence (AI) tool is being used to predict how many patients coming through the emergency department will need to be admitted into the hospital, helping planners manage demand on beds.

The tool, developed by researchers at University College London (UCL, London, UK), estimates how many hospital beds will be needed in four and eight hours’ time by looking at live data of patients who have arrived at the hospital’s emergency department. In their 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, also provides much 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, emailed to hospital planners. The research team is now refining the models so that they can estimate how many beds will be needed in different areas of the hospital (e.g. beds on medical wards or surgical wards).

In order to develop the AI tool, the researchers 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, and combined these probabilities for an overall estimate of the number of beds needed. Upon comparing the models’ predictions to actual admissions between May 2019 to March 2020, the team found that the models outperformed the conventional method, with central predictions an average of four admissions off the actual figure compared to the conventional method, which was on average 6.5 admissions out. After COVID-19 hit, the researchers were able to adapt 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.

“Our AI models provide a much richer picture about the likely demand on beds throughout the course of the day,” said Dr. Zella King (UCL Clinical Operational Research Unit and the UCL Institute of Health Informatics). “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.”

“This AI tool will be hugely valuable in helping us manage admissions and patient flow at UCLH,” added Alison Clements, Head of Operations, Patient Flow & Emergency Preparedness, Resilience & Response at UCLH. “Our next step is to start using the predictions in daily flow huddles. We look forward to continuing work with UCL to refine the tool and expand its predictive power across the hospital.”

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