It’s part two of our latest Thrive in Five series about AI in the world of healthcare. If you missed the first instalment on HeartFlow technology, you can read about AI in healthcare here. This month, we’re looking at four AI tools in healthcare, and how they can be positively used to eliminate human error, reduce preventable deaths and produce earlier diagnoses. HN predict is the name of this week’s example. So, let’s dive in, shall we?
HN Predict
HN Predict is essentially a piece of AI-driven software that identifies those at risk of hospitalisation–people who might need urgent care or treatment in the near future–using machine learning algorithms.
“HN Predict is an AI tool which was recently tested on NHS Greater Glasgow & Clyde’s existing patient data and proved it can identify 83 per cent of people who would otherwise need unplanned hospital care months in advance. The tool is able to identify patients early enough to prevent many of their visits to A&E and hospitalisations.”
HN Predict
Now, I’m not going to pretend I know exactly how it works. But the benefits of this are vast, right? It eliminates the need for a hospital bed; it eliminates–or at the very least reduces–NHS expenditure. It alleviates the extreme pressure currently on the public health workforce. Most importantly, however, it keeps people at home and in the arms of their loved ones.
Mimicking Blue Zones
So, what’s the point of this series and the new content I’m developing? Those of you who are familiar with my work will know I’ve been talking about wearable technology and preventative health measures for over a decade. I’ve also talked before about Dan Beuttener and the Blue Zones Project. The Blue Zones are five areas of the world – Japan, Italy, California, Costa Rica and Greece – where people are healthiest and live the longest.
Combining things like wearable technology and preventative health measures with machine learning is how we extend our healthspan: mimicking those people in the Blue Zones to live a healthy life for as long as possible. Which is why I think we should all have an open mind when it comes to using AI tools in healthcare.
That’s it for now, join us next week for another example.
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