Let’s just say it as it is: Navigating literature databases can be overwhelming. The sheer volume of publications available online is staggering. Reading through all of it would take multiple lifetimes, at least. And beyond that, the complexity of search queries poses another challenge: how do you phrase the right questions to get the answers you actually need?

Before diving into this topic step by step and explain how Flinn.ai can help you streamline the process up to 50 % faster, let’s start with a fundamental question:

Why Literature Searches Matter for MedTech Compliance & Innovation

In the MedTech industry, literature searches are a key component of both regulatory compliance and continuous improvement. They help manufacturers stay informed about advancements, identify risks, and support product claims, playing a crucial role in obtaining and maintaining certifications such as the CE mark.

Beyond pre-market approval, literature searches are essential in post-market surveillance (PMS) to monitor product safety, detect emerging risks, and ensure long-term effectiveness. As a cost-effective alternative to large-scale clinical trials, systematic reviews provide a structured, data-driven foundation for decision-making. By regularly conducting literature searches, manufacturers can ensure their devices remain safe, effective, and aligned with evolving industry standards.

However, the sheer scale of available data makes literature searches incredibly complex, which leads us to …

…Top Challenges in MedTech Literature Searches and How to Fix Them

As mentioned earlier, there are several key challenges that need to be addressed:

• Overload & Human Error – Screening thousands of papers manually leads to fatigue and mistakes.

• Evolving Search Requirements – Guidelines change, and literature needs constant updates.

• Lack of Standardization – Every manufacturer does it differently, making industry-wide alignment difficult.

• Fragmented Data Sources – No single database covers everything, requiring searches across multiple platforms.

• Inconsistent Data Quality – Some studies are outdated, unreliable, or lack peer review.

• Time & Resource Constraints – Skilled professionals are spending hours on repetitive tasks instead of high-value work.

Sounds familiar?
Let’s start with the basics:

Types of Literature Searches in MedTech

To add another layer to this: Not all literature searches are the same. In MedTech, they generally fall into two main categories:

1. State-of-the-Art (SOTA) Searches for Medical Devices

SOTA searches give manufacturers a broad overview of the medical landscape, establishing the current standard of care before launching a new device.

Let’s say you’re developing a transcranial magnetic stimulation (TMS) device for depression. Your SOTA search would involve:

• Understanding depression – its prevalence, diagnosis, and treatment challenges.

• Reviewing systematic studies and medical guidelines.

• Comparing available treatment options and their effectiveness.

• Extracting relevant safety and outcome data.

The goal? To ensure your device aligns with best practices and stands up to regulatory scrutiny.

2. Device-Specific Searches for Post-Market Surveillance

Once your device is on the market, the focus shifts to performance and safety monitoring. Device-specific searches help manufacturers:

• Track clinical data related to their product.

• Collect data from competitors and alternative treatment or procedures options.

• Identify new risks, complications, or adverse events.

With regulatory bodies demanding transparency, a sloppy search process can result in incomplete safety evaluations - or worse, compliance violations.

So How Do You Prepare a Structure For a Literature Search (and Save Your Sanity)?

The magical word in this headline? Correct: Structure. A well-planned approach for a literature search protocol ensures consistency, transparency, and reliability in gathering and analyzing data. Here’s what an effective best practice for planning could look like: Start by defining a clear search plan—outlining how you will conduct your search, incorporating PICO parameters if relevant (we will discuss an example below), and structuring your search strategy, which should involve:

• A clear research question – What specific aspect of the medical device or treatment are you investigating?

• Inclusion and exclusion criteria – Define relevant and irrelevant studies to refine search results.

• Standardized evaluation templates – So you’re not reinventing the wheel every time.

Define Your Search with the PICO Framework

Before even touching a search bar, clarity is key. The PICO framework is a way to refine literature searches:

• Patient Population – Who is the device for?

• Intervention – What treatment or technology is being evaluated?

• Comparison – What alternative treatments or devices exist?

• Outcome – What performance or safety measures are relevant?

This ensures searches are targeted and not just a random mix of keywords that return thousands of irrelevant results.

Crafting the Perfect Search Query: 3 Essential Elements

Once the research focus is clear, crafting an effective search query involves:

• Keywords concerning medical conditions and treatments

• Keywords concerning comparative or similar devices

• Searching for the product under surveillance (Using descriptive terms can help, as product brand names are not always mentioned in abstracts.)

To further refine searches, filtering for specific publication types or MeSH terms can help target systematic reviews or high-quality study types.

At this stage, the process typically reaches the point where the tedious manual work begins—and this is also where we at Flinn.ai step in. Let’s explore a smarter, more efficient approach!

How Flinn.ai Makes Literature Searches 50 % Faster with AI

At Flinn.ai, we’ve seen firsthand how time-consuming and inconsistent literature searches can be. Most manufacturers struggle with manually searching multiple databases one by one, copy-pasting data into spreadsheets like it’s 1999 and/or sorting through duplicates and irrelevant studies for hours on end. Our approach still follows the general procedure of literature searches, but we have the flexibility to adapt it to the needs of our users.

What Makes Our Approach Different?

Instead of relying on slow, outdated methods, we automate the not-that-fun parts of literature searches:

• Multi-Database Search – No more jumping between PubMed, Embase, and Google Scholar—we search them all at once.

• Automation of Manual Tasks – Manual data entry, deduplication, and documentation? Handled.

• AI-Assisted Screening – The system flags relevant studies, reducing human error. We’re also developing new AI features to make query-building 5x faster, ensuring you get to the insights without the headache.

Plus, we prepared resources to help optimize your search strategies:

• Google Scholar Guide – Check out our database selection tutorial to enhance your searches.

• Step-by-Step Video Tutorials – Learn how to structure queries and extract data efficiently.

Final Thoughts: How to Make Literature Searches Work for You

A well-executed literature search can save time, ensure compliance, and provide valuable insights - if done right. Before you dive in, keep these key principles in mind:

• Stay Focused – Clearly define your research question.

• Invest in Preparation – A rushed search strategy will cost you later.

• Leverage Team Expertise – A second opinion can catch things you’ve missed.

• Use AI & Automation – Why work harder when you can work smarter? At Flinn.ai, we tailor our approach to your specific needs.

By embracing structured planning, collaboration, and the right technology, literature searches can become less of a burden and more of a strategic advantage. Still have questions about literature searches or how Flinn.ai can help? Let’s talk!