When it comes to technology adoption, artificial intelligence is pretty high up on the list for most industries. And there’s no surprise there – the technology has had numerous breakthroughs, and these advancements have proven to provide many benefits when it comes to reducing costs, streamlining workflows, and improving efficiency.
But there’s also another side to the story. With the exciting developments of AI also come doubts and misconceptions of the technology.
In fact, an Accenture study has shown that while 91% of decision-makers recognize the benefits of AI in healthcare, more than half have fears that it might be responsible for a fatal error.
It’s natural for businesses to be sceptical about fully incorporating this new technology, especially in an industry like healthcare where actual lives are at stake. But like most drastic changes, proper planning is key.
As such, it helps to be aware of the challenge’s businesses might face when adopting AI technology and explore possible solutions for those roadblocks.
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The State of AI in Healthcare
AI is no longer science fiction. In fact, whether you’ve noticed or not, artificial intelligence is already very much prevalent in our daily lives.
It’s the magic behind the chatbot that replies to your social media queries, the product recommendations you get on Amazon, and even the predicted ETA Google Maps provide when planning your route for a specific destination.
Many healthcare organisations are increasing adopting AI technology and the growth doesn’t seem to be dying out soon. Accenture study states that in the next 5 years alone, the health AI market is predicted to grow more than tenfold.
What’s more, 19% of healthcare professionals believe that it would take less than two years for AI to be widespread in the medical field, while 37% are already using AI, just in a limited capacity.
So how exactly is AI used in healthcare? While technology cannot truly replace a doctor’s personal touch, it has shown to be instrumental in many areas of healthcare.
Natural language processing can help medical professionals easily sift through academic journal articles and electronic health records, machine learning can make precision medicine possible, and intelligent chatbots can supplement the duties of a physician.
The future for AI in healthcare applications looks bright, with 84% of professionals saying that those who delay investing in the technology will likely fall behind. Still, the endeavour of incorporating AI in a delicate industry like healthcare can prove to be challenging, and various roadblocks can arise.
What’s Stopping Healthcare Companies from Adopting AI?
There are 3 impediments that healthcare institutions might face when incorporating AI into the business:
1. Patients do not trust AI-powered systems
Just like any new technological implementation, businesses will have to be extra careful not to disrupt existing processes, workflows, and procedures that might already be effective as it is.
Plus, there’s the problem of how patients might actually react to these new AI-powered systems. Shifting gears might affect customer trust. As patients are familiar with dealing with a human doctor, they might be sceptical of the effectiveness and accuracy of their AI alternatives.
And this applies on the other side as well—91% of healthcare decision-makers fear that AI will be responsible for a fatal error, and 53% worry that the technology will be poorly implemented or won’t work properly, according to Intel research.
2. Automation might replace healthcare workforce
This is probably what concerns people the most about the developments in AI: what will happen to healthcare jobs once organisations start heavily using AI-powered systems?
How will those who normally work on routine tasks (nurses and allied health professionals for example) be able to adapt to the growing threat of automation?
In most industries, including healthcare, the benefit of AI mostly comes from its ability to automate administrative, repetitive, and manual functions.
It turns out that healthcare jobs are relatively safe from technological obsolescence, according to a report by the Brookings Metropolitan Policy Program.
The automation potential for the workforce by 2030 is only at the medium to a low level: home health aides have only an 8% potential, while nurses and medical assistants only have a 29% and 54% potential, respectively.
3. There are doubts about data sufficiency and quality of outcomes
By now, you already understand how the road to adopting AI is not as smooth as people would like. However, there is one aspect that shouldn’t be overlooked—mostly because AI depends heavily on it—and that is data.
AI-powered systems without data and input from external sources will probably not function as expected. And even if there is data available, it might be unstructured, polluted, or not have a clear purpose.
The situation isn’t entirely hopeless though. Most healthcare organisations already collect data on a regular basis and even work with monitoring equipment that can generate 1000 readings per second.
With the right data and right data science platform, the healthcare industry can proactively intervene to provide better care plans, educate patients, and improve patient care and outcomes while decreasing healthcare costs.
Overcome Obstacles to Grow
Rather than seeing AI as a threat, the technology should be considered as a natural progression that can lead to more efficient processes and better patient care.
Roadblocks to implementing a transformative technology, like AI, will persist. But implementing AI in healthcare can produce deep and actionable insights that promise to have lasting profound effects on how healthcare is delivered and received.
