Healthcare provider organizations are gaining big efficiencies with NLP
The use of artificial intelligence by healthcare provider organizations is on the rise. AI has truly been proving itself in recent years, in both clinical administrative settings.
One big AI technology is natural language processing, or NLP. According to one recent survey, 36% of healthcare organizations plan to implement NLP this year. Most healthcare organizations are looking at NLP for consumer interactions. But one vendor player, Suki AI, predicts that in the coming years, NLP will be used on a wider scale for physicians at the point of care.
For one thing, the vendor says, NLP can help with clinician burnout. The technology especially can assist with administrative work.
Here, Punit Soni, CEO of Suki AI, discusses with Healthcare IT News how NLP is being used can be used in healthcare, how NLP can diminish clinician burnout, challenges healthcare CIOs face when rolling out NLP.
Q. 36% of healthcare organizations plan to implement NLP this year. What kinds of consumer interactions can NLP be used for at healthcare organizations? Why does Suki AI predict in the coming years NLP will be used on a wider scale for physicians at the point of care?
A. Use cases for AI now touch nearly every aspect of healthcare. 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 machine learning.
Providers certainly have a unique opportunity to use these advanced solutions to make better sense of the huge volumes of data entering EHRs 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, digital assistants to help streamline administrative workflows such as clinical documentation.
In addition, healthcare organizations are using NLP to help patients better understtheir conditions care plans, whether through information provided through patient portals, pushed out to mobile devices or delivered via intelligent, web-based chatbots.
Without a doubt, the industry will continue to see adoption of NLP to support point-of-care activity decision-making. Similar to how consumers now regularly interact with digital assistants to complete a variety of tasks, such as playing music or ordering groceries, physicians will increasingly use voice to do what they need done.
Used in tandem with voice solutions, NLP is powering everything from better documentation more accurate billing to population health initiatives personalized care. The ability to rapidly comb through data provide better clinical decision support or identify gaps in care when a patient is in front of a provider ultimately will help improve outcomes operational efficiency while reducing cost.
Q. Clinicians have to do so much administrative work, especially with EHRs ever-changing regulations. That combined with COVID-19 has led to burnout, which can negatively affect patient care. Fifty-five percent of frontline healthcare workers surveyed by KFF the Washington Post said they feel burned out when going to work. How can AI NLP help here?
A. AI NLP are key to solving the industry’s physician burnout epidemic. These tools provide the right combination of speed intelligence to dramatically reduce administrative burden improve provider satisfaction.
Consider that voice-to-text tools have existed in the industry for some time. When powered by advanced AI techniques, these types of solutions take clinical documentation improvement to the next level. NLP natural language understanding (NLU) enables physicians to speak naturally, not memorize rote commands, which creates an intuitive user experience with minimal learning curve.
And 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 much more, to create accurate clinical documentation that reflects the user’s thought processes 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.
These same techniques can be used to streamline reporting improve collaboration between clinicians. For example, NLP can comb through free text in EHRs to extract keywords standardize data for analytics information sharing.
Looking ahead, we expect that voice will be the next revolution in user interfaces after mobile, healthcare will be the frontier in which this will play out.
Q. What are a couple of the challenges healthcare CIOs face in the years ahead when rolling out AI NLP technologies, how can they overcome them?
A. One of the greatest challenges to the introduction of new technologies in healthcare is provider mistrust. While the opportunities for generating significant return on investment are plentiful, the industry has learned the hard way that clinician buy-in is key to adoption long-term success.
It’s imperative that healthcare organizations take the steps necessary to educate bring providers into the discussion of AI implementations early. In essence, give them a seat at the table when it comes to choosing vendors identifying appropriate solutions.
Healthcare organizations also face many competing priorities when it comes to technology investments rollouts – all while operating within some of the tightest margins in recent history. While the business case for AI NLP often is an easy one to make, CIOs must still present a clear ROI to the executive suite.
For example, in rural areas, where healthcare organizations often face challenges attracting retaining physician talent, investment in AI NLP could be a valuable recruitment retention tool.