There’s no shortage of particular uses for emerging AI, but one form in particular – Natural Language Processing, or NLP – is poised to have a significant impact across the entire healthcare sector.
That’s according to Punit Soni, CEO of Suki AI, who was recently interviewed by our colleague Bill Siwicki over at HealthcareITNews.
As Soni sees it, the administrative uses for NLP, particularly when it comes to consumer interactions, have long been recognized, but he also anticipates an array of uses for physicians at the point of care.
“When it comes to extracting value from physician voice notation, for example, what was out of reach just five years ago is now creating expansive opportunities through techniques such as NLP and machine learning,” he explained. “Providers certainly have a unique opportunity to use these advanced solutions to make better sense of the huge volumes of data entering EHRs and other information systems on a regular basis. Some obvious ways that providers are innovating in this area are through the use of chatbots to handle simple, repetitive tasks or familiar patient inquiries, and digital assistants to help streamline administrative workflows such as clinical documentation.”
On the patient side, Soni points to how “healthcare organizations are using NLP to help patients better understand their conditions and care plans, whether through information provided through patient portals, pushed out to mobile devices or delivered via intelligent, web-based chatbots.”
For Soni, providers also stand to benefit from what NLP can do for them on the administrative side of their practices, thus providing significant help in the battle against provider burnout.
To wit, “AI and NLP are key to solving the industry's physician burnout epidemic,” he said. “These tools provide the right combination of speed and intelligence to dramatically reduce administrative burden and improve provider satisfaction.”
Noting that voice-to-text tools have been around for awhile, Soni says that “when NLP is combined with machine learning, solutions can become highly personalized to each user. These solutions can learn the individual user's preferred terminology, documentation formatting and much more, to create accurate clinical documentation that reflects the user's thought processes and preferences. And all of these benefits are available in real time, rather than hours later from solutions that rely on human labor on the back-end.”
If there’s a hurdle yet to be leapt for AI advocates, Soni says it’s dealing with provider mistrust.
“While the opportunities for generating significant return on investment are plentiful,” he noted, “the industry has learned the hard way that clinician buy-in is key to adoption and long-term success. It's imperative that healthcare organizations take the steps necessary to educate and bring providers into the discussion of AI implementations early.”
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