Bradley Merrill Thompson

The motivation for this month’s post was my frustration with the techniques for searching the FDA’s 510(k) database.  Here I’m not talking about just using the search feature that FDA provides online. Instead, I have downloaded all of the data from that database and created my own search engine, but there are still inherent limitations in what the data contain and how they are structured.  For one, if you want to submit a premarket notification for an over-the-counter product, it really isn’t easy to find predicates that are specifically cleared for over-the-counter without a lot of manual work.

To see if I could find an easier way, I decided to use the database FDA maintains for unique device identifiers, called the Global Unique Device Identification Database (GUDID).  You can search that database using the so-called AccessGUDID through an FDA link that takes you to the NIH where the database is stored. That site only allows for pretty simple search, so for what I needed to do, I downloaded the entire database so I could work directly on the data myself.

While the UDI database is enormous at this juncture (over 3 million products), what I found left me with questions about just how comprehensive and complete the data are.  At the same time, it seems like a good way to supplement the information that can be gleaned from the 510(k) database.
Continue Reading Unpacking Averages: Finding Medical Device Predicates Without Using FDA’s 510(k) Database

Over the spring and summer, I did a series of posts on extracting quality information from FDA enforcement initiatives like warning letters, recalls, and inspections.  But obviously FDA enforcement actions are not the only potential sources of quality data that FDA maintains.  FDA has what is now a massive data set on Medical Device Reports (or “MDRs”) that can be mined for quality data.  Medical device companies can, in effect, learn from the experiences of their competitors about what types of things can go wrong with medical devices.

The problem, of course, is that the interesting data in MDRs is in what a data scientist would call unstructured data, in this case English language text describing a product problem, where the information or insights cannot be easily extracted given the sheer volume of the reports.  In calendar year 2021, for example, FDA received almost 2 million MDRs.  It just isn’t feasible for a human to read all of them.

That’s where a form of machine learning, natural language processing, or more specifically topic modeling, comes in.  I used topic modeling last November for a post about major trends over the course of a decade in MDRs.  Now I want to show how the same topic modeling can be used to find more specific experiences with specific types of medical devices to inform quality improvement.
Continue Reading Unpacking Averages: Using Natural Language Processing to Extract Quality Information from MDRs

A private equity client asked us recently to assess a rumor that FDA was on the warpath in enforcing the 510(k) requirement on medical devices from a particular region.  Such a government initiative would significantly deter investments in the companies doing the importing.  Turns out, the agency was not.  The FDA’s recent activities in the region were well within their historical norms.

But the project got us thinking, what does the agency’s enormous database on import actions tell us about the agency’s enforcement priorities more generally?  There are literally thousands of ways to slice and dice the import data set for insights, but we picked just one as an example.  We wanted to assess, globally, over the last 20 years, in which therapeutic areas has FDA been enforcing the 510(k) requirement most often?
Continue Reading Unpacking Averages: Assessing FDA’s Focus on Enforcing 510(K) Requirements on Imports

You might be thinking, that’s an odd title: obviously FDA’s breakthrough device designation is helpful.  However, after looking at the data, my conclusion is that I would avoid the breakthrough device designation for any product that qualifies for the 510(k) process.  The process is likely not helpful for such devices.

[Update – August 3, 2022: See the bottom of this post.]

Continue Reading Unpacking Averages: Assessing Whether FDA’s Breakthrough Device Designation Is Helpful

Recalls have always been a bit of a double-edged sword.  Obviously, companies hate recalls because a recall means their products are defective in some manner, potentially putting users at risk and damaging the brand.  They are also expensive to execute.  But a lack of recalls can also be a problem, if the underlying quality issues still exist but the companies are simply not conducting recalls.  Recalls are necessary and appropriate in the face of quality problems.

Thus, in terms of metrics, medical device companies should not adopt as a goal reducing recalls, as that will lead to behavior that could put users at risk by leaving bad products on the market.  Instead, the goal should be to reduce the underlying quality problems that might trigger the need for recall.

What are those underlying quality problems?  To help medical device manufacturers focus on the types of quality problems that might force them to conduct a recall, we have used the FDA recall database to identify the most common root causes sorted by the clinical area for the medical device.
Continue Reading Unpacking Averages: Common Root Causes Driving Medical Device Recalls

Most companies want to avoid FDA warning letters.  To help medical device companies identify violations that might lead to a warning letter, this post will dive deeply into which specific types of violations are often found in warning letters that FDA issues.

Background

As you probably know, FDA has a formal process for evaluating inspection records and other materials to determine whether issuing a warning letter is appropriate.  Those procedures can be found in chapter 4 of FDA’s Regulatory Procedures Manual.  Section 4-1-10 of that chapter requires that warning letters include specific legal citations, in addition to plain English explanations of violations.  The citations are supposed to make reference to both the statute and any applicable regulations.

As a consequence, to understand the content of the warning letters, we need to search for both statutory references as well as references to regulations.  Because statutes are deliberately drafted to be broader in their language, references to the regulations tend to be more meaningful.
Continue Reading Unpacking Averages: Violations Found in Medical Device Warning Letters

Overview

In this month’s post, in the medical device realm I explore what kinds of inspection citations most often precede a warning letter.  In this exercise, I do not try to prove causation.  I am simply exploring correlation.  But with that caveat in mind, I think it’s still informative to see what types of inspectional citations, in a high percentage of cases, will precede a warning letter.  And, as I’ve said before, joining two different data sets – in this case inspectional data with warning letter data – might just reveal new insights.
Continue Reading Unpacking Averages: Device Inspection Citations That Frequently Precede Warning Letters

It is common for FDA and others to show a map of the United States with the states color-coded by intensity to showcase the total number of inspections done in that state.  Indeed, FDA includes such a map in its newly released dashboard for FDA inspections.  In reviewing that map with the U.S. map color-coded to reflect where medical device establishments are located, do you notice anything?  Not to destroy the suspense for you, but it turns out that FDA tends to inspect where medical device inspection facilities are located.  Really.

We wanted to get beneath those numbers in two ways.  First, it’s much more informative to look at the data at a county level because there’s actually quite a bit of variation county by county.  Second, and more importantly, we wanted to normalize the inspection data by the number of facilities.  In other words, by looking at inspections per facility, we can get a better sense of the inspection frequency in each county.

Continue Reading Unpacking Averages: Likelihood of FDA Medical Device Inspections

This month, we’re going to look at a visualization that uses network techniques. Visualizing a network is a matter of nodes and edges. If the network were Facebook, the nodes would be people, and the edges would be the relationships between those people. Instead of people, we are going to look at specific device functionalities as defined by the product codes. And instead of relationships, we are going to look at when device functionalities (i.e., product codes) are used together in a marketed device as evidenced by a 510(k) submission.

Continue Reading Unpacking Averages: Popular Ways to Combine Device Functionality