Machine learning and artificial intelligence have taken the healthcare industry by storm as innovators offer tempting visions of sophisticated clinical decision support, smoother EHR workflows, and more intuitive consumer-provider relationships to beleaguered clinical staff.
The development of AI, or at least its beginnings, has been a constant theme among health IT vendors and healthcare provider organizations who believe they are on the leading edge of a revolution.
They might well be correct: early research into imaging analytics, natural language processing, and decision support tools has been largely promising, with some algorithms even claiming to outperform trained human professionals in certain tasks.
Imaging analytics, natural language processing, and the analysis of huge datasets involved in the Internet of Things (IoT) are prime use cases for machine learning, but the methodology is shaping up to be more than just the engine that drives decision-making processes under the hood.
Machine-to-human interactions through ambient computing, virtual personal assistants, and augmented reality are on the agenda for many forward-thinking health IT vendors, who see direct machine-to-human interactions as a way to revolutionize workflows and customer service.
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Healthcare consumers are different than retail, financial, or manufacturing customers. Their concerns are more complicated, their privacy is protected by stringent regulations, and their data could be scattered across a dozen disconnected providers in both analog and digital forms.
Automation is indeed coming to healthcare, as it is to every industry, and machines will no doubt soon take over some of the simpler, time-consuming tasks that humans hate to handle.
But the incredible complexity of healthcare and its big data may mean that healthcare will have to wait until other industries work out all the kinks and create an AI-driven ecosystem that can be smoothly implemented without using patients in life-or-death situations as guinea pigs.
While healthcare consumers may not always have the same experiences in hospitals and clinics as they do at their banks, in their shops, and on their phones, taking the time to get the basics of big data right might be the best move that healthcare providers can make.
Many providers may still feel that the risks of racing to implement machine-driven automation in healthcare may not yet be worth the rewards, but organizations should be prepared to start accelerating their investments if they wish to eventually see success in the inevitably data-driven environment of the near future.
Source: Health IT Analytics (View full article)
Posted by Dan Corcoran on December 11, 2017 06:12 AM
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