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

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


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

On February 15, 2019, the U.S. Food and Drug Administration (“FDA”) finalized two guidance documents regarding regenerative medicine therapies (see FDA’s announcement here). This development comes nearly 14 months after FDA issued both guidance documents in draft form, which also coincided with FDA’s announcement of a new comprehensive regenerative medicine policy framework intended to

On October 18, 2018, the FDA published Content of Premarket Submissions for Management of Cybersecurity in Medical Devices.  This guidance outlined recommendations for cybersecurity device design and labeling as well as important documents that should be included in premarket approval submissions.  This guidance comes at a critical time as the healthcare industry is a

The federal government entered into a partial shutdown at midnight on Saturday, December 22, 2018. The implications of the ongoing shutdown are far-reaching, but its impact on the Food and Drug Administration (“FDA”) is of particular concern to members of FDA-regulated industries and those with a role in ensuring the public health. Thousands of FDA

The Federal Trade Commission (“FTC”) and the Antitrust Division of the Department of Justice (“Antitrust Division”) released their respective year-end reviews highlighted by aggressive enforcement in the health care industry. The FTC, in particular, indicated that 47% of its enforcement actions during calendar year 2016 took place in the health care industry (including pharmaceuticals and

On May 17, 2016, FDA issued Draft Guidance for Industry on Use of Electronic Health Record Data in Clinical Investigations (“Draft Guidance”).  This Draft Guidance builds on prior FDA guidance on Computerized Systems Used in Clinical Investigations and Electronic Source Data in Clinical Investigations, and provides information on FDA’s expectations for the use of