Australia’s largest country health service will look at using artificial intelligence and predictive analytics as a means to bridge its remote healthcare divide.
WA’s Country Health Service (WACHS) has begun exploring how remote patient monitoring could help it better provide “proactive and pre-emptive” health services across its two-and-a-half million square kilometre network.
It currently services a network of 70 public hospitals and 38 nursing posts, as well as a range of community health and mental health services – the largest geographical area of any Australian health provider.
But the size of the network makes providing high quality healthcare services difficult, with individuals in regional rural areas experiencing poorer general health than those in metropolitan areas.
“The vast spread of the population and the corresponding small population numbers mean that it is challenging to sustain integrated health services across the state,” WACHS’s latest annual report states.
The agency’s hospital network is currently supported through a Command Centre that provides 24/7 support, and it is now looking to begin offering real-time remote patient monitoring supported by AI.
“Remote patient monitoring is being explored as an opportunity to further develop this service across inpatient, emergency, high dependency and intensive care beds by promoting early recognition of patient deterioration both at the bedside and in the Command Centre,” the agency said.
“The service would be supported by predictive analytics and artificial intelligence tailored to the unique setting and requirements.”
The patient monitoring service would see “increased visibility and early warning of patient condition deterioration, both locally and remotely”, allowing patients to remain closer to home and reducing the need for transfers.
WACHS has now kicked off a request for information process to understand its options for the potential service that may include a technology platform that can integrated with both existing and future health applications, including an electronic medical record.
The platform will to show the “real-time application of artificial intelligence/machine learning to provide predictive analysis of clinical patient data”, and allow for customisation.
