Natural Language Processing in Healthcare: Unlocking the Power of Clinical Language
Jon Jaroska, Red Sky Health CTO
Every day, healthcare organizations generate mountains of data—doctor’s notes, discharge summaries, pathology reports, and more. And the vast majority of it is unstructured, meaning it doesn’t fit neatly into rows and columns in a database. It’s just words—written by humans, for humans. It might be shorthand, messy handwriting, quick notes, but regardless – it's still data, critical patient data that has power and value.
But what if machines could understand all of that clinical language? What if they could read between the lines, pick out patterns, and actually do something useful with it? That’s where Natural Language Processing (NLP) comes in—and it’s quickly becoming one of the most powerful applications of AI in healthcare.
So, what exactly is NLP?
In plain terms, NLP is a type of AI that uses what's known as semantics to understand through the use of context. and interpret text so that essentially, computers are able to 'understand' human language. In healthcare, that means algorithms can comb through unstructured text - think physician notes or insurance denial letters, understand the intended meaning of the textand pull out relevant details like diagnoses, medications, or risk factors.
The beauty of NLP is that it doesn’t just read text—it interprets it. And that opens the door to all kinds of automation and insight that wasn’t possible before, creating more value opportunities from this unstructured data.
Real ways NLP is already helping in healthcare
Here are a few examples of how NLP is making life easier for both clinicians and operational teams:
1. Reducing documentation overload
Physicians are drowning in paperwork. Keeping up with notes on patients, procedures and recommendations takes time, but is absolutely neccessary. NLP tools can help auto-generate clinical notes or summarize long entries, cutting down on time spent typing and clicking around in the EHR. This frees up physicians and their staff to have more patient focus, and other value add efforts.
2. Smarter coding and billing
Instead of relying solely on human coders to dig through notes and match the right codes during billing and claims processing, NLP can highlight the most relevant information and suggest the appropriate ICD/CPT codes. That means fewer errors, faster reimbursements, and less back-and-forth.
3. Powering denial management
This is a big one. At Red Sky Health, we’re using NLP to read denial responses, extract key error reasons, and automatically generate customized appeals. What used to take hours now happens in real time—and we’re seeing signifcant improvements in success rates across our client base.
4. Identifying high-risk patients
NLP can also quickly (and tirelessly) scan through years of clinical notes, identify patterns and flag patients who might be at higher risk based on symptoms, lifestyle, or social factors that aren’t always obvious in structured data. Feeding this information to physicians empowers them with greater patient need insights, and creates more opportunities to recommend and/or provide proactive, necessary care.
Is NLP perfect? No—but it's improving fast.
Let’s be honest: healthcare language is complicated. Abbreviations, clinical jargon, and varying styles make it tough for machines to interpret things with 100% accuracy. But the technology is getting better every day—especially as large language models (like GPT) are fine-tuned on medical data and provide greater semantic understandability – again, helping humans talk to machines and vice versa.
There are still hurdles around data quality, sharing, privacy, and bias. But most organizations see NLP as an augmentation tool, not a replacement. It’s here to support teams, not take them out of the equation. AI should be seen as a way to empower humans, in this case, physicians and their teams, to gain better medical insights and provide better care, not as a tool that takes people completely out of the process.
What’s next for NLP in healthcare?
We’re just scratching the surface, and like all predictions, things are always fluid. But - here’s a few things we we expect to see in the 18-24 months:
Seamless integration of NLP into EHR workflows
More intelligent summarization tools (think discharge summaries that write themselves)
Conversational AI agents that actually understand and can respond to medical context
NLP working hand-in-hand with custom Large Language Models (LLMs) to power everything from appeals to clinical decision support
Final thoughts
NLP is turning language into action—and in a field like healthcare, that’s a big deal.
It’s not just about automation. It’s about understanding. About pulling insights from the notes doctors write in a hurry. About turning the messy, human parts of medicine into something machines can actually help with.
At Red Sky Health, we’re leaning hard into this space—because we believe the future of healthcare will be shaped by organizations that can make sense of both the data and the narratives.
