AI Case Study
Gold Coast Health saves $3m per annum in operational costs by forecasting patient arrival rates at emergency care using machine learning
Gold Coast Health worked with Australian Federal Agency CSIRO to predict ER demand using machine learning, so that they could manage hospital resources and staffing efficiently and reduce waiting time. Encouraged by the results and an estimated savings of $3m, the project is being expanded to 27 public hospitals and to predict outbreak of influenza.
Industry
Healthcare
Healthcare Providers And Services
Project Overview
"The Patient Admission and Prediction Tool (PAPT) developed by the CSIRO is predicting patient rates at ED with up to 90% accuracy allowing hospitals to plan elective procedures, staffing more efficiently resulting in reduced wait-times and cost reduction"
Reported Results
According to CSIRO the results were:
* Expansion to all 27 major public hospitals across Queensland
* Savings of $3 Million annually
* Up to 90% accuracy in predicting patient rates
Technology
Function
Operations
General Operations
Background
"To better manage Emergency Department and resource allocation and improve patient care Gold Coast Health uses predictive tools"
Benefits
Data