Does NLP Deserve To Be The New Hotness In Healthcare?

Lately, I’ve been seeing a lot more talk about the benefits of using natural language processing technology in healthcare. In fact, when I Googled the topic, I turned up a number of articles on the subject published over the last several weeks. Clearly, something is afoot here.

What’s driving the happy talk? One case in point is a new report from health IT industry analyst firm Chilmark Research laying out 12 possible use cases for NLP in healthcare.

According to Chilmark, some of the most compelling options include speech recognition, clinical documentation improvement, data mining research, computer-assisted coding and automated registry reporting. Its researchers also seem to be fans of clinical trial matching, prior authorization, clinical decision support and risk adjustment and hierarchical condition categories, approaches it labels “emerging.”

From what I can see, the highest profile application of NLP in healthcare is using it to dig through unstructured data and text. For example, a recent article describes how Intermountain Healthcare has begun identifying heart failure patients by reading data from 25 different free text documents stored in the EHR. Clearly, exercises like these can have an immediate impact on patient health.

However, stories like the above are actually pretty unusual. Yes, healthcare organizations have been working to use NLP to mine text for some time, and it seems like a very logical way to filter out critical information. But is there a reason that NLP use even for this purpose isn’t as widespread as one might think? According to one critic, the answer is yes.

In a recent piece, Dale Sanders, president of technology at HealthCatalyst, goes after the use of comparative data, predictive analytics and NLP in healthcare, arguing that their benefits to healthcare organizations have been oversold.

Sanders, who says he came to healthcare with a deep understanding of NLP and predictive analytics, contends that NLP has had ”essentially no impact” on healthcare. ”We’ve made incremental progress, but there are fundamental gaps in our industry’s data ecosystem– missing pieces of the data puzzle– that inherently limit what we can achieve with NLP,” Sanders argues.

He doesn’t seem to see this changing in the near future either. Given how much money has already been sunk in the existing generation of EMRs, vendors have no incentive to improve their capacity for indexing information, Sanders says.

“In today’s EMRs, we have little more than expensive word processors,” he writes. “I keep hoping that the Googles, Facebooks and Amazons of the world will quietly build a new generation EMR.” He’s not the only one, though that’s a topic for another article.

I wish I could say that I side with researchers like Chilmark that see a bright near-term future for NLP in healthcare. After all, part of why I love doing what I do is exploring and getting excited about emerging technologies with high potential for improving healthcare, and I’d be happy to wave the NLP flag too.

Unfortunately, my guess is that Sanders is right about the obstacles that stand in the way of widespread NLP use in our industry. Until we have a more robust way of categorizing healthcare data and text, searching through it for value can only go so far. In other words, it may be a little too soon to pitch NLP’s benefits to providers.

About the author

Anne Zieger

Anne Zieger

Anne Zieger is a healthcare journalist who has written about the industry for 30 years. Her work has appeared in all of the leading healthcare industry publications, and she's served as editor in chief of several healthcare B2B sites.


  • Anne,
    Appreciate the written perspective on Natural Language Processing (NLP) and it’s use within healthcare. Having been in NLP for over a decade and founding a high-tech company, SyTrue which leverages our own proprietary NLP technology within our solution pipeline, I would like to share a few thoughts around the technology as our perspective is a bit different on unstructured data than most. For instance, in order for NLP to be successful, one needs to realize the realities of the US healthcare system and how the system produces healthcare documentation. Healthcare documentation is extremely “dirty” and disparate; medical records are often locked in scanned images or come from a wide variety of EMR extracts that lack an industry standard. To achieve NLP success across a healthcare enterprise the implementation of the technology should be able to deal with the varying variety of documentation formats. Unfortunately, most outside of healthcare and certainly some marketers do not have an understanding of the unstructured data environment and a true understanding of NLP and how it can scale enterprise-wide. Unfortunately, this lack of understanding around the technology does a disservice to the technology itself, especially within the healthcare environment. NLP has been implemented, successfully and at scale addressing varying formats that include Optical Character Recognition (OCR), text narratives, real-time medical record processing, etc.

    The US healthcare system waste approximately one trillion dollars in inefficiencies annually. This number can be significantly reduced through the use of NLP; impacting healthcare at the patient level by addressing the data that resides in the patients’ medical records which are the foundation to healthcare billing. NLP is being used today, across the various medical record formats that either support the medical bill or do not support the medical bill impacting fraud, waste and abuse. Leveraging NLP at the health plan level addressing fraud, waste and abuse would greatly reduce the one trillion dollar waste and potentially fund health insurance to every single American without bankrupting the US. The challenge is not the technology, but leadership at Centers for Medicare and Medicaid Services (CMS) to remove the barriers in an archaic system that relies on outdated, manual processes that equates to a massive spend at the taxpayer’s expense – to the tune of $2,739,726,027.39 billion dollars PER DAY. NLP and other technologies have a real opportunity to make a significant impact to reduce this great waste. If CMS deployed an NLP technology solution targeting fraud, waste, and abuse at the medical record level, CMS would be able to review 100% of the claims identified for fraud, versus less than the 1% reviewed in the current, manual process eliminating >$400 billion dollars in fraud, waste and abuse(1).

    Is NLP ready for primetime? The answer is unequivocally YES, and is being leveraged by forward-thinking health plans and providers today. The question to ask is can we afford not to implement an automated NLP solution that brings consistency, compliance and boost productivity to dramatically decrease the healthcare administrative spend.


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