AI Document Processing Use Cases for Healthcare and Life Sciences

Pharma is engineering drug delivery with nanoparticles. Biotech is growing mini “organoids” to test new kinds of treatments. Precision medicine is mapping multi-omics for hyper-personalized care. All of it’s new, exciting, and transformative… except for the part where it all still starts with someone filling out a paper form.

In healthcare and life sciences, paper is still the norm. But paper-based workflows introduce errors, slow everything down, and people just don’t like them. The bad data they generate can lead to everything from erroneous Medicare claims rejections to lost or delayed tests. 81% of physicians say “the time and effort required to complete documentation tasks impedes patient care.”

So what’s the solution? Healthcare and life sciences enterprises have to innovate their data collection processes as much as they have to innovate their core products and services. The International Society for Pharmaceutical Engineering (ISPE) reports that replacing analog validation with new digital solutions can lead to as much as a 50% increase in overall process efficiency. 

And Onymos DocKnow, our AI Document Processing platform, is one of those new digital solutions. Here are some of the ways you can use it to start innovating document and data processing.

Accessioning and test requisition forms (TRFs) 

Test Requisition Forms (TRFs) are a critical part of most precision medicine workflows, and the vast majority of TRFs are still on paper. There are too many legacy systems, legal requirements, and handwritten supporting documents to drive an industry-wide paper-to-digital shift — yet. That means, for the foreseeable future, paper-based TRFs will stay the norm, and precision medicine will have to deal with them. 

Sometimes, TRFs actually arrive incomplete, and accessioners have to comb through dozens of pages of supporting documents to find the data points they need. Sometimes, the data itself is incorrect, and they have to perform painstaking reconciliation.

Now, they can use DocKnow. 

DocKnow can flag inconsistent data on digital copies of TRF forms and their supporting document bundles. Human-in-the-loop reviewers don’t have to scour the clinical notes or medical records to do it themselves. Plus, DocKnow autofills required inputs with data from attached documents and connected systems. It’s not a black box either — users can visually verify every piece of data DocKnow references. 

Compliance and standard operating procedures (SOPs)

Maintaining data integrity is one of the biggest challenges that healthcare and life sciences face. Some of the citations the FDA issued the most in 2024 involved inadequate procedural controls and incomplete or missing records. 

That’s probably one of the reasons we hear about Standard Operating Procedures (SOPs) so often in our conversations with hospitals, pharma enterprises, and biotech firms. SOPs define how employees should handle critical workflows, from sample processing to data entry to qualifying and rejecting claims. 

But even with strong SOPs in place, managers have to ensure they’re actually followed. Naturally, the more manual the workflows and the more complex the processes, the more likely it is that mistakes happen. 

That’s why DocKnow can automatically extract, verify, and classify data based on SOPs and other custom business rules or guidelines. And because compliance isn’t just about having the right documents but also proving their accuracy, DocKnow can ensure every action is trackable and audit-ready.

DocKnow can validate ICD codes, flag potential violations, and give users the right info to help them make the right decisions at the right times. 

Structuring unstructured data

Over the last decade, AI has been responsible for some of medicine’s biggest breakthroughs. And the pace of innovation isn’t slowing down. 

But AI is only as good as the data it’s built on, and many HealthTech enterprises don’t have all the data they need to start building. 

Or, rather, they do have it, but it’s stuck in unstructured documents and legacy databases. And they don’t know how to get it out. 

Sometimes, getting to this data isn’t about building AI products and services at all. Sometimes, it’s just about being able to identify what clinics need coached on how to properly request tests or helping laboratory technicians connect old records to new ones. 

This is the kind of intelligence DocKnow can generate from your unstructured data sources using Cognitive Insight Models (CIMs). Unlike Large Language Models (LLMs), CIMs are purpose-built for complex reasoning tasks. 

Clinical trial eligibility

Every clinical trial has strict inclusion and exclusion criteria (e.g, age, medical history, biomarkers, comorbidities, medications) for carefully screening candidates.

Managing all of this criteria by itself can be confusing enough, but there are often… exceptions, conditions, and overrides that make things even more complicated.

But DocKnow can cut through all of that complexity by automatically screening candidates or providing reference-backed recommendations through its “Insights” panel. These insights are highly configurable and can include summaries, charts, or anything else you need to keep your team more informed.

Submitting regulatory filings

The FDA, in particular, has one of the most rigious regulatory filing processes. Pharma companies releasing new products have to submit extensive documentation with thousands of pages and years’ worth of data. The review process itself is iterative and filled with variable pathways and unpredictable timelines.

So it’s not a surprise that many of the pharma companies we talk to are trying find ways to maximize filing accuracy and efficiency. Even minor errors or misclassifications can lead to significant delays.

DocKnow can automate refining filings like these. Instead of manually sifting through thousands of pages, teams can leverage AI-powered analysis to ensure adherence to guidelines and pinpoint discrepancies before submission. This can save weeks (and, sometimes, months) of effort, ensure smoother approval processes, and simply get products to market faster.

What makes DocKnow different 

Most IDP platforms, most SaaS in general, offer the same trade-off: faster time to production for less control. That’s less control over your data and less control over the solution itself. 

But DocKnow is built using No-Data Architecture. That means Onymos sees no data and saves no data, and the entire solution (not just the service itself, but the front-end code, too, if you want to use it) is hosted in your environment. You can even license the actual functional source code (that means you are never stuck inside a template we built for you). Our model eliminates the risk of third-party data breaches and vendor lock-in. 

In other words, when you use DocKnow, you deploy it where you want it, exactly how you want it. 

If your healthcare or life sciences enterprise needs to transform data processing, we can help. Get in touch with our team for a customized demo.