Researchers from Monash University have undertaken a study using artificial intelligence (AI) to understand the reasons behind hospital readmissions. According to the university, the study involved using AI technology to examine 10 years’ worth of patient records. Specifically, that included 14,000 patient medical records and the details of over 327,000 hospital readmissions. Project lead Wray Buntine, who is also a professor of IT and data science at Monash University Faculty of IT, said the decision to carry out the research was underpinned by the need to lower hospital costs and improve the quality of care at hospitals. “This study utilised a rich source of clinical patient data to infer medical risk predictions and improve the quality of patient healthcare,” he said. “By examining these complex data sets, the machine learning algorithms we’ve developed can make predictions on medical risks, such as identifying if and when a patient will readmit and whether this can be avoided.”
From the research, the team developed an initial prediction model for two patient cohorts: Chronic liver disease and heart failure.
“The ability to accurately identify patients at risk of emergent readmission for chronic liver disease or heart failure may allow us to deliver timely interventions which prevent hospitalisation, thereby improving patient outcomes and contributing towards a sustainable healthcare system.”
While the project is still ongoing and due to be completed in 2021, the researchers believe the initial findings could potentially be used to assist with predicting patient readmissions. In a separate announcement, Monash University, together with Queensland Health, Queensland University of Technology, New South Wales Health, Digital Health Cooperative Research Centre (CRC), University of Sydney, and Commonwealth Health, have partnered to build the Clinical Data and Analytics Platform (CDAP) to provide clinicians nationwide access to real-time analytics about the progress of the latest COVID-19 treatment strategies and clinical trial outcomes.
Monash University’s Ann Nicholson, who is leading the analytical modelling for the CDAP, said using a real-time analytics model would help clinicians and scientists better understand COVID-19. “This decision support tool will be used to predict which patients will need hospital and intensive care admission, as well as the likely outcomes of interventions, as we learn more about this disease over time,” she said.
CDAP clinic lead Tom Snelling said the need for a digital solution like the CDAP can help clinicians quickly adapt to changing conditions. “We need digital solutions that improve our knowledge of how best to manage people with COVID-19 in near real-time,” he said. “The CDAP has been built to rapidly extract and organise clinical data, which will help us learn why some people have severe disease and which treatments result in the best outcomes.”