Machine learning technology is advancing at a rapid pace, and there is a definite buzz around how this expertise can be used in the healthcare setting. Specifically, artificial intelligence may be able to predict which patients are at the highest risk for clinical events that require early invention.
A study presented the American Thoracic Society International Conference in Washington showed that an algorithm has the capability to identify hospitalized patients at risk for severe sepsis and septic shock using data from electronic health records (EHRs).
When will we see machine learning in routine healthcare?
Despite this positive study demonstrating that patients could ultimately benefit from machine learning and EHRs, it doesn’t look like we’re going to see the NHS jumping on the AI development anytime soon.
These medical breakthroughs are being stunted and slowed by the lack of access to health data necessary to learn the complex patterns required for the process. That’s the consensus of various healthcare stakeholders that were at the Machine Learning in Healthcare: Industry Applications Conference in Boston earlier this year.
What has caused this push for machine learning in healthcare?
According to Russ Wilcox, partner of venture capital firm Pillar, machine learning is benefiting from a trifecta of technology trends:
- Big data
- Better hardware
- Smarter algorithms
“Ninety percent of the world’s digital information is less than two years old, and (that trend) is accelerating even faster,” Wilcox told the conference.
If we have so much data, and proof that machine learning can benefit patients, why is there an issue with accessing it – what’s the problem?
In healthcare, the majority of the data is trapped in silos; stifling machine learning’s potential early on in its promise. Industries outside of healthcare are ahead of us here. Automation and digital tools are being used in transport, safety and retail industries, but that hasn’t moved on to healthcare for patients yet.
That said, it isn’t all doom and gloom. The rise in wearable health technologies is growing, meaning that many companies now have access to good quality data that enables automation and machine learning, and is in a usable format. This will be key in the drive to push machine learning to the top of the healthcare agenda.
