Led by research by Precision Driven Health (PDH), a New Zealand partnership between Orion Health, Auckland University and Waitemata District Health Board, the New Zealand-based Orion says it is exploring meaningful ways to minimise wastage in the healthcare sector and help clinicians make more accurate decisions at the point of care.
According to Orion, more than $6.5 trillion is spent on healthcare each year globally, yet typically between 20-40% of spend is wasted on unnecessary services and excess administration.
With an ageing population and a growing number of people with chronic illnesses growing globally, Orion says the issue is set to worsen.
“We’re yet to see the true impact of machine learning on healthcare. The last decade has been focused on integrating IT systems and capturing massive amounts of information about patients and their environments. The next decade will be to connect all that data and use machine learning for daily healthcare decisions, driving improved care, operational efficiencies, and cost effectiveness,” McCrae said.
According to Mc Crae, unlike most current uses of machine learning, Amadeus Intelligence ingests and combines multiple datasets from diverse data sources to predict multiple outcomes – financial, operational and administrative.
“The key to making a meaningful impact on healthcare is in the accuracy of the prediction, and the ability to respond,” McCrae said.
“Tapping into the vast amounts of data available through many different sources - including relevant genomic, socio-economic and behavioural data, information from devices, and data based on demographics and climate - will engender better decision-making, drawing on information from entire populations to treat and manage a person’s health. Today healthcare organisations need more accuracy in their predictive analytics to reduce operational costs and improve patient care and outcomes.”
McCrae says that readmission rates remain a costly challenge for healthcare organisations, and with 17.6% of hospitalisations in the US resulting in a re-hospitalisation within 30 days, an estimated 76% of those re-hospitalisations are potentially avoidable, costing $30 billion.
PDH has completed research projects that use a breadth of data types and apply machine learning models to achieve greater predictive accuracy, calculating potential savings four times higher than current predictive models. Using machine learning techniques, healthcare can expect larger reductions in readmission rates achieved in a far more cost effective manner.
“Precision screening presents a huge opportunity for the health sector, where data science can help target health interventions to those with the greatest need,” McCrae concluded.