How predictive analytics can shift us from ‘sick-care’ to true preventative healthcare
If we look at extraordinary world of the future of healthcare, one of the most important developments is predictive analytics based on the profusion of data we are now able to capture and feed into machine learning algorithms.
An article in BBC titled The invisible warning signs that predict your future health provides some great insights into this space.
Among other people involved in the space, I am quoted:
Futurist Ross Dawson, founder of the Future Exploration Network, predicts a shift from the current model of remedial “sick-care” to a new healthcare ecosystem, focused more on prevention and the tracking of potential health problems before they have a chance to develop.
“Shifting societal attitudes, with increased expectations to live full and healthy lives, are driving these changes,” he says. “This decade, the explosion of new technology and algorithms has given rise to deep learning in artificial intelligence, becoming vastly more effective at pattern recognition than humans.”
By harnessing AI to track our heart rate, breathing, movement and even chemicals in our breath, the technology has the ability to detect potential health problems at an individual level long before obvious symptoms appear. This could help doctors to intervene or allow patients to change their lifestyle to allay or prevent illness.
Perhaps most excitingly, these systems can discern patterns that are invisible to the human eye, revealing surprising aspects of how our bodies betray our future health.
Dawson highlights studies in which AI is better able to anticipate people who are likely to suffer heart attacks by constant monitoring of their pulse. One study even pulled out variables that cardiologists had not thought of as having predictive value – a home visit from the GP requested by the patient, for example.
There are many excellent examples in the article, written by doctor and author Leah Kaminsky, it is well worth a read in full.
Image: Becky Stern
