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Artificial intelligence and the clinical world: a view from the front line
Author(s) -
Pearce Christopher,
McLeod Adam,
Rinehart Natalie,
Whyte Robin,
Deveny Elizabeth,
Shearer Marianne
Publication year - 2019
Publication title -
medical journal of australia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.904
H-Index - 131
eISSN - 1326-5377
pISSN - 0025-729X
DOI - 10.5694/mja2.50025
Subject(s) - front line , front (military) , line (geometry) , artificial intelligence , medicine , computer science , history , engineering , mechanical engineering , mathematics , geometry , archaeology
The practice of medicine is changing rapidly, in line with society, where our lives are now driven by the digital revolution. Health has lagged behind, in part because it remains focused on human interactions. While digital interactions with banks are now the norm, most aspects of health still require human interaction. Recently The Lancet opined: “A scenario in which medical information, gathered at the point of care, is analysed using sophisticated machine algorithms to provide realtime actionable analytics, seems to be within touching distance”. This was closely followed by “Despite the excitement around these sophisticated [artificial intelligence] technologies, very few are in clinical use”.1 In this article, we describe such an algorithm that was deployed in Australia in 2018, with commentary on issues that were once theoretical but now need to be considered in detail. Building on a previous program,2 several primary health networks (PHNs) across Victoria and Sydney have made available their pooled, deidentified primary care data for collaborative research. With recent expansion, Outcome Health’s POLAR (Population Level Analysis and Reporting tool) Data Space will have information from more than 500 practices. The Victorian PHNs involved are piloting a predictive tool to assist general practitioners with determining a patient’s individual risk of attending an emergency department (ED).