Manufacturer and Device Name Extraction in Scientific Literature

Scientific publications can feel endless: often 15 to 20 pages or more, 8,000+ words, and packed with dense technical language. And yet, for anyone working in Regulatory Affairs, Post-Market Surveillance, Clinical Affairs, or Competitive Intelligence, they often hold exactly the kind of evidence you can’t afford to miss: a mention of your product, a competitor’s device, or an emerging safety concern.

Spotting those signals quickly can make all the difference. This is exactly where manufacturer and device name extraction comes in.

Most teams only need two answers to triage a paper efficiently:

• Who conducted the study?

• Which medical devices were used or tested?

Once you have that, you can make the only decision that matters in the moment:

Is this publication relevant or can I move on to the next?

Why Manufacturer and Device Name Extraction Matters

For MedTech teams, tracking what’s being published — about your own devices and your competitors’ — is a regulatory must and a strategic advantage.

Here’s why extracting manufacturer and device names matters for MedTech teams:

1. Post-Market Surveillance (PMS) Signals from the field, for instance performance issues or adverse events, often first surface in publications. Spotting them early can feed directly into corrective action or customer communication.

2. Quality and Patient Safety Literature provides real-world evidence that strengthens internal quality systems and risk assessments.

3. Competitive Intelligence Understanding how competing devices are being used and evaluated helps identify gaps or opportunities in your own portfolio.

The Challenge (of all Challenges): Manual Search and Extraction

Let’s be honest: in most teams, literature review is still a manual, frustrating process.

• You search PubMed, Cochrane, or Google Scholar.

• You try combinations of keywords like “pacemaker” or “defibrillator”.

• You skim through dozens of abstracts.

• You pay for full-text articles that might contain relevant details.

• You open a 30-page PDF and search manually using Ctrl+F.

• You copy device and manufacturer names into Excel or Word.

Sounds familiar? You’re not alone! Most people follow a similar pattern and hit the same blockers again and again:

• No full-text access: Most searches only cover titles, abstracts, and keywords. But key mentions often live deeper in the paper and behind a paywall.

• Vague search terms: Broad keywords like “implant” or “defibrillator” miss specificity. And if you don’t get the logic right, you risk excluding valuable results.

• Wasted money: You pay for access hoping to find something and 9 times out of 10, the info you needed isn’t there.

• Manual effort: Even when the paper is useful, extracting the data is slow and error-prone. Some papers mention half a dozen manufacturers and devices.

In the end, a massive amount of time and budget goes into pulling just two data points: manufacturer and device name.

Importantly, only ~2% of relevant mentions appear in abstracts, most signals are buried in the body text. That’s why abstract-only search misses critical evidence.

From Manual Screening to Automated Full-Text Extraction

Over the past years, several tools have emerged to automate the most time-consuming step of literature work: finding manufacturer and device mentions in publications.  Flinn is one of these tools. It applies automated text analysis across each publication to identify relevant details quickly and accurately.

In practice, this means you can:

• Handle literature sets at any scale (from 50 to 500+ publications).

• Automatically extract all mentions of manufacturers and medical devices.

• Receive a concise, structured list, enabling you to decide in seconds whether to read it in full or move on.

So instead of skimming PDFs for hours (or even days!), you’re free to focus on what matters:
Relevant? → Read the full paper.
Not relevant? → Move on.

Evaluation: Real Data, Real Results

But how reliable is our approach? That’s a valid question, especially in regulated environments, where missing a critical mention can have real consequences.

We didn’t rely on assumptions. We ran a benchmark on real-world publications to compare Flinn’s extraction performance against a widely used, established extraction tool in the MedTech community. Both tools worked on the same dataset to identify manufacturer and device names, and the results speak for themselves.

Benchmark Setup

We focused our test on two manufacturers and two devices to create a defined and measurable benchmark environment.

• Manufacturers: Philips, Abbott — Devices: Trilogy 100, HeartMate II

• Access: 93% Open Access, 7% behind paywall

• Volume: 868 unique manufacturer mentions and 1,193 device names extracted

Accuracy: Flinn vs. Existing Tools

Benchmark on the same dataset; Flinn vs. a widely used competing tool:

The result? Flinn identified over 60% more relevant mentions, with zero false positives.

Summary: Speed, Accuracy, and Peace of Mind

Automated name extraction offers more than just time savings. It enables:

• Higher precision, fewer false positives

• Faster triage and decision-making

• Stronger compliance in PMS workflows

• Smarter competitive insights

The takeaway:

Full-text analysis + automation = a major leap in efficiency and quality.

Want to See Flinn in Action?

If you're curious to:

• Review detailed benchmark results

• Understand the methodology

• Run a side-by-side test on your own documents

• Or even find out which competing tool we compared against

…then this is a great opportunity to get in touch with us and explore how the solution could work in your own use case.

We’d love to show you what’s possible! Reach out to us.