Natural language processing is a subdiscipline of artificial intelligence – and one that can be of great use in healthcare – that digs out clinical nuggets from all the free text in electronic health records and data warehouses.

Marty Elisco, CEO of Augintel, a healthcare NLP company, believes that NLP will go mainstream in 2023 for three reasons: The kinks have been ironed out, the value has been proven and the timing is right.

Healthcare IT News spoke with Elisco to get him to elaborate on these reasons and help healthcare CIOs and other health IT leaders understand why 2023 might just be the year for NLP.

Q. One of the reasons you suggest more healthcare provider organizations will adopt natural language processing technology in 2023 is because the kinks have been ironed out. Please talk about the kinks you say have been taken care of and how that will encourage adoption.

A. First, let's level-set the definition of NLP. NLP refers to the branch of computer science concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.

NLP can be applied in several contexts. It can refer to voice-to-text recognition. It can also be used for handwriting recognition. But in our segment, and in the context of this discussion, we are using NLP for content intelligence – or information extraction – of the written word.

About five years ago, machine learning technology took a giant leap forward. It became possible to cost effectively train algorithms with massive amounts of data. That innovation enabled NLP for content intelligence. Machine learning was beginning to be applied to massive amounts of narrative data to build NLP models that could identify key concepts described in text.

Over the past couple of years, because the cost to develop a model has dropped, it has become economically feasible to develop industry-specific models.

For example, in the legal industry, NLP has been used for e-discovery. Lawyers use NLP to mine documentation delivered during the discovery phase to make it easier to consume relevant content. And there has been progress more recently in leveraging NLP in healthcare – behavioral health and health and human services more specifically.

Initial content intelligence efforts in health and human services were typically custom projects that were meant to analyze data at a specific point in time, rather than providing a tool that could be accessed on a daily basis. The expertise and effort necessary to "teach" deep healthcare context was too burdensome for many and resulted in project failure – or never getting started at all.

In the last year or so, industry-specific solutions have become commercially available, because the pilots to prove them out have completed. These pilots benefited from the collaboration between data scientists and customers/users who refined the language model for that industry's need.

So, the kinks have been ironed out. The technology is mature and stable, innovative tech companies have built easily obtainable mission-specific SaaS solutions with deep context, and customers are now reaping the rewards.

Q. You also say the value of NLP has been proven. Please give a couple of examples of NLP proving its worth.

A. The ROI achieved by organizations leveraging NLP has been delivered.

As one example, caseworkers at Allegheny County were continuing to find that so much rich information was buried within case notes and unstructured data. With an overload of information, it took so long for caseworkers to find relevant data.

They wanted to solve this challenge – the challenge of quickly accessing important data at the right time with the ultimate goal to help improve services for the families and children they help. They knew that the ability to quickly and easily access better insights would paint a picture of a whole case without having to spend hours of time flipping through notes.

One caseworker in particular has claimed the NLP platform alone has saved her five hours per week in administrative tasks.

An NLP platform also has helped Allegheny County have a better understanding of social determinants of health. Typically, it would take a careful review of the entire case history to understand things like history of drug usage or housing insecurity – two SDOH factors that significantly impact overall wellbeing. But with all the color, detail and deeper descriptions living within the unstructured data, an NLP tool enables caseworkers to see early warning signs in real time.

Needless to say, it's incredibly helpful for families when caseworkers can pull out information such as this from unstructured data earlier in the process.

Q. And finally, you say that with the year 2023, the timing is right for NLP in healthcare. Please elaborate.

A. It's no secret that staff shortages and burnouts have shown to be a real challenge for healthcare organizations across the board in recent years. According to a study published in Mayo Clinic Proceedings, the clinician burnout rate among U.S. physicians spiked dramatically during the first two years of the COVID-19 pandemic after six years of decline.

Furthermore, the study revealed that clinician burnout was 62.8% in 2021, compared with 38.2% in 2020. The trend is clear.

Additional research has shown that 64% of burnout is attributed to administrative burden, which is certainly contributing to caseworkers' breaking points. With caseworkers so stretched out, attrition remains high.

Some organizations report 30% attrition per quarter. There is a loss of case knowledge that occurs with attrition and that loss directly impacts outcomes. When new caregivers are assigned, they simply don't have time to read entire files, which can result in interruptions in the continuum of care, particularly in complex cases.

So, you have caseworkers and clinicians stretched thin who are spending too much time away from the people in their care, and they've had enough. Coupled with the impact on outcomes from lost case knowledge, it's clear to see that the status quo simply cannot continue if we want to maintain a reliable and functioning healthcare system.

At the same time, there are significant advances in cost-effective machine learning tools, particularly NLP, that can alleviate some of that stress. The time is right for healthcare providers to lean on available tools. Therefore I believe 2023 will be the year NLP will take off.

Follow Bill's HIT coverage on LinkedIn: Bill Siwicki
Email the writer: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.