Thursday, October 21, 2021

Healthcare Provider Organizations Are Gaining Great Efficiencies with NLP

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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 and can be used in healthcare, how NLP can diminish clinician burnout, and 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? Without a doubt, the industry will continue to see adoption of NLP to support point-of-care activity and 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.

In addition, 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. Used in tandem with voice solutions, NLP is powering everything from better documentation and more accurate billing to population health initiatives and personalized care. The ability to rapidly comb through data and provide better clinical decision support or identify gaps in care when a patient is in front of a provider ultimately will help improve outcomes and operational efficiency while reducing cost.

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. 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 and machine learning.

A. AI and NLP are key to solving the industry’s physician burnout epidemic. These tools provide the right combination of speed and intelligence to dramatically reduce administrative burden and improve provider satisfaction. Q. Clinicians have to do so much administrative work, especially with EHRs and 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 and the Washington Post said they feel burned out when going to work. How can AI and NLP help here?

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 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. These same techniques can be used to streamline reporting and improve collaboration between clinicians. For example, NLP can comb through free text in EHRs to extract keywords and standardize data for analytics and information sharing. 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 and natural language understanding (NLU) enables physicians to speak naturally, not memorize rote commands, which creates an intuitive user experience with minimal learning curve.

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