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

Since the passage of the Medical Device Amendments of 1976, FDA has regulated in vitro diagnostic (IVD) tests as medical devices, subject to a full suite of FDA requirements.  During that time, FDA has also asserted that it has the authority to regulate in-house tests developed and performed by CLIA-certified, high-complexity clinical laboratories (generally referred to as laboratory-developed tests or LDTs) but chose as a matter of enforcement discretion not to regulate LDTs.  Over time, the Agency chipped away slowly at LDT enforcement discretion, carving out certain kinds of tests (e.g., direct-to-consumer LDTs) and thus making them subject to regulation, but by and large did not take broad steps to regulate LDTs.

Continue Reading The VALID Act: Senate Action Brings FDA Regulation of LDTs Closer to Fruition

Much like the ambiguous landscape involving cannabidiol (CBD) products on the consumer market, an influx of delta-8 THC containing products for consumption has highlighted a recurrent regulatory issue surrounding the legality of hemp derived products at the federal level. The Agricultural Improvement Act of 2018 (the “2018 Farm Bill”), which, among other things, offered a federal definition of hemp and removed it from the list of Schedule I controlled substances, specifically carved out hemp derived products with less than 0.3% delta-9-tetrahydocannabinol (THC) on a dry weight basis, thereby allowing products that meet this definition to flood the consumer markets.

Continue Reading Recent FDA Enforcement Action Colors Regulatory Landscape for Delta-8 THC Products

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

This month’s post focuses on how timely FDA decisions are in categorizing new diagnostics under the Clinical Laboratory Improvements Amendments of 1988 (CLIA). The answer is that, on average, the agency does okay, but they also sometimes may miss their own guideline by a wide margin.  I use the word “may” there because the FDA data set is inadequate to support a firm conclusion.  I’ll explain more about that below, but this is another case of FDA releasing incomplete data that frustrates data analytics.

Continue Reading Unpacking Averages: Assessing FDA’s Performance Categorizing New Diagnostic Tests Under CLIA

I recommend against relying on any data I provide in today’s post.  I hope the data are at least somewhat accurate.  But they are not nearly as accurate as they should be, or as they could be, if FDA just released a key bit of information they have been promising to share for years.

One of the ways data scientists can provide insights is by grafting together data from different sources that paint a picture not seen elsewhere.  What I want to do is join the clinical trial data at www.clinicaltrials.gov with the data maintained by FDA of approved drugs, called drugs@FDA.  But I can’t, at least not with much accuracy.

Continue Reading Unpacking Averages: Connecting Published Clinical Trials with FDA Drug Approvals