AI to help Emergency Departments improve patient waiting time information

AITHM research lead Dr Anton Pak said current systems of reporting used simple rolling average estimates that have limited accuracy, and did not account for the dynamic and complex nature of Emergency Departments.

Dr Pak put the movements of about 120,000 patients who visited a major Queensland hospital Emergency Department over two years under the microscope.

“We have used machine learning algorithms to look at a large set of real-time patient information in terms of waiting times, and this method is much more accurate at predicting wait times,” said Dr Pak.

He said researchers hope to use the research to develop a public interface where people can access ED waiting times in close to real-time.

“From a patient’s perspective, the knowledge may reduce uncertainty about waiting times and improve satisfaction. It also has the potential to assist clinicians and nurses estimate demand for care and calibrate workflow.”

He said once finalised, the system could be rolled out across Emergency Department networks in all hospitals and patients could have access to view waiting times before deciding to go to a particular hospital.

Dr Pak is a health economist and data scientist. He has worked closely with Princess Alexandra Hospital’s Deputy Director Emergency Medicine Dr Andrew Staib, gathering information including the patient journey from arrival to departure, over a two-year period from 1 January 2016, to 31 December 2017.

University of Queensland Professor of Economics Brenda Gannon, facilitated this latest research.

Read more about the research here: “Predicting waiting time to treatment for emergency department patients” in the journal Medical Informatics.