The Australian e-Health Research Centre will spend the next 12 months working closely with hospital bed managers and health CIOs as it seeks to build confidence in its new forecasting tool, designed to ease backlogs in hospital emergency departments.
Dr David Hansen, research director of The Australian e-Health Research Centre, said the centre’s new software, The Patient Admission Prediction Tool (PAPT), makes it possible for hospital emergency medical staff to accurately predict demand on their services.
And he said the project demonstrates the value of analyzing historical data as an indicator of future demand for services.
“We’ve shown PAPT vastly improves successful prediction of patient presentation and admission in two hospitals with very different populations,” says Dr Hansen said.
“Emergency departments already know there’s a pattern to presentations and admissions, but existing models are very simplistic. PAPT uses historical data to provide an accurate prediction of the expected load on any day,” he added.
The prototype PAPT package has a simple interface designed in consultation with those who will ultimately use it every day.
"Over the next year we plan to assess and quantify the impact of using the forecasts”, Dr Hansen says.
The project was conducted in collaboration with clinicians from Gold Coast and Toowoomba Hospitals and Griffith University and Queensland University of Technology. It began with the analysis of data from Toowoomba hospital (which serves a population of 90,000) and Gold Coast, (which has one of the busiest EDs in the state, a large itinerant population and numerous other EDs serving the area). One major challenge of the project involved matching the ED information system with a separate hospital-wide information system.
The outcome is a software tool allowing bed managers to accurately predict how many patients will present at hospital emergency departments, their expected medical needs and the number of hospital admissions. On-the-ground staff can see what the patient load will be like in the next hour, the rest of the day, into next week, or even on holidays with varying dates, such as Easter.
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