In recent times, the use of artificial intelligence technology in healthcare has been a very hot topic. However, while we’ve come tantalizingly close to realizing its promise, no application that I know of has come close to transforming the industry. Moreover, as John Lynn notes, healthcare organizations will not get as much out of AI use if they are not doing a good job of working with both structured and unstructured data.
That being said, new research by Accenture suggests that those of us dismissing AI tech as immature may be behind the curve. Researchers there have concluded that when combined, key clinical health AI applications could save the US healthcare economy as much $150 billion by 2026.
Before considering the stats in this report, we should bear Accenture’s definition of healthcare AI in mind:
“AI in health presents a collection of multiple technologies enabling machines to sense, comprehend, act and learn, so they can perform administrative and clinical healthcare functions…Unlike legacy technologies that are only algorithms/tools that complement a human, health AI today can truly augment human activity.”
In other words, the consulting firm sees AI as far more than a data analytics tool. Accenture analysts envision an AI ecosystem that transforms and serves as an adjunct to the many healthcare processes. That’s a pretty ambitious take, though probably not a crazy one.
In its new report, Accenture projects that the AI health market will reach $6.6 billion by 2021, up from $600 million in 2014, fueled by the growing number of health AI acquisitions taking place. The report notes that the number of such deals has leapt from less than 20 in the year 2012 to nearly 70 by mid-2016.
Researchers predict that the following applications will generate the projected $150 billion in savings/value:
- Robot-assisted surgery: $40 billion
- Virtual nursing assistants: $20 billion
- Administrative workflow assistance: $18 billion
- Fraud detection: $17 billion
- Dosage error reduction: $16 billion
- Connected machines: $14 billion
- Clinical trial participant identifier: $13 billion
- Preliminary diagnosis: $5 billion
- Automated image diagnosis: $3 billion
- Cybersecurity: $2 billion
There are a lot of interesting things about this list, which goes well beyond current hot topics like the use of AI-driven chatbots.
One that stands out to me is that two of the 10 applications address security concerns, an approach which makes sense but hadn’t turned up in my research on the topic until now.
I was also intrigued to see robot-assisted surgery topping the list of high-impact health AI options. Though I’m familiar with assistive technologies like the da Vinci robot, it hadn’t occurred to me that such tools could benefit from automation and data integration.
I love the picture Accenture paints of how this might work:
“Cognitive robotics can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician’s instrument precision…The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights.”
When implemented properly, robot-assisted surgery will generate a 21% reduction in length of hospital stays, the researchers estimate.
Of course, even the wise thinkers at Accenture aren’t always right. Nonetheless, the broad trends report identifies seem like reasonable choices. What do you think?
And by all means check out the report – it’s short, well-argued and useful.