The order is in, and the LDT Final Rule is out.

In May 2024, the U.S. Food & Drug Administration (“FDA” or the “Agency”) published its Final Rule establishing its regulatory framework over laboratory developed tests (“LDTs”) as medical devices and, in effect, announced the end to decades of enforcement discretion by the Agency. The deadline to comply with the first phase of the Final Rule was set for May 6, 2025. On Monday, March 31, however, a federal judge in the U.S. Eastern District of Texas ordered that “FDA’s final rule exceeds its authority and is unlawful” and that “[t]herefore, consistent with controlling circuit precedent, the proper remedy is vacatur of the final rule and remand to FDA for further consideration in light of this opinion.”

Epstein Becker & Green’s Life Sciences Team is continuing to review and digest this order and its impact on the clinical lab industry, and we plan to release a more fulsome analysis in the coming days.

For decades, FDA’s Center for Devices and Radiological Health (CDRH) has been recognizing standards that can be referenced in premarket medical device submissions.  Congress broadly directed federal agencies to begin relying on standards in 1996, through the National Technology Transfer and Advancement Act, but the informal practice dates back to the 1970s.  Congress specifically directed FDA to begin using standards for medical device submissions through the Food and Drug Administration Modernization Act of 1997 (FDAMA). 

Being a curious person, I wanted to see what FDA has done with that authority by looking at the CDRH database for Recognized Consensus Standards: Medical Devices.  My main takeaway is that CDRH is not yet investing enough time and energy in recognizing standards that support digital health and AI.

Findings

I downloaded the data set on September 20, 2024, and looked when standards were recognized by FDA and to which therapeutic or functional areas they related.

Most months, I try to answer a well-focused question.  This month, however, I want to simply take a broad look at how FDA conducts its postmarket surveillance study program under Section 522 of the federal Food, Drug, and Cosmetic Act. FDA published a new final guidance on this topic on October 7, 2022, and the agency also made corresponding updates to its database. That gave me the chance to study the data, however incomplete they are.  More on that later.

Overview of the Guidance and the Program

Before I turn to the data, I thought it would be helpful to provide a high-level reminder of what FDA’s postmarket surveillance study program is all about.  As explained in the guidance, Section 522 provides FDA with the authority to require manufacturers to conduct postmarket surveillance at the time of approval or clearance or at any time thereafter of certain class II or class III devices.  I’ll talk more about the question of timing below.

This month, I explore just how old medical devices are as measured by the date they were cleared or approved by the FDA, using the Global Unique Device Identification Database.

That database is the only one that FDA maintains that gives insight into specifically what devices are currently marketed in the United States.  And of course, this being unpacking averages, I don’t just want the average, but I want to understand the range.

I may be jumping the gun here, but I’m anxious to understand how the new flurry of AI medical devices is performing in the marketplace, or more specifically, whether the devices are failing to perform in a way that jeopardizes health.

FDA keeps a list these days of medical devices that involve AI, and here’s the recent growth in clearances or other approvals. 

Click to enlarge the image. 

Note for calendar year 2024, we only have first-quarter data.

The growth is notable. As these devices enter the market, they are subject to all the typical medical device postmarket regulatory ...

When I was working on my Masters in data science, one of the projects I did was to create an algorithm that would take an intended use statement for a medical device and predict whether FDA would require a clinical trial.  It worked fairly well, with accuracy of about 95%.

Since that’s a dynamic algorithm in which the user inputs an intended use statement and gets a prediction of FDA’s decision, I wanted to go about a similar task this month: create a static word cloud to show what words are most associated with intended use statements where FDA has required a clinical trial.  At least in theory, this static representation might give you a sense of words in an intended use statement that are more likely to push your device toward a clinical trial.

Most people have seen the growth in artificial intelligence/ machine learning (AI/ML)-based medical devices being cleared by FDA.  FDA updates that data once a year at the close of its fiscal year.  Clearly the trend is up. But that's a bit backward looking, in the sense that we are only learning after the fact about FDA clearances for therapeutic applications of AI/ML.  I want to look forward.  I want a leading indicator, not a laggard.

I also want to focus on uses of AI/ML that are truly therapeutic or diagnostic, as opposed to the wide variety of lifestyle and wellness AI/ML products and the applications used on the administrative side of healthcare.  As a result, in this post I explore the information on clinicaltrials.gov because not only are those data focused on the truly health related, they are also forward-looking. The more recent clinical trials involve products still under investigation and not yet commercially available or even submitted to FDA.

Combination products present a tremendous opportunity to improve health outcomes, because they leverage multiple disciplines.  If we were, for example, to focus on drugs alone with little thought to how they might be delivered, we would be surely missing a chance to enhance safety or effectiveness.  Likewise, many devices can be made more effective or safer if paired with a drug.

At the end of 2016, FDA finalized a rule covering Postmarket Safety Reporting for Combination Products that now can be found at 21 C.F.R. Subpart B.[1]  A few years later, in July 2019, FDA finalized a guidance ...

As of Monday March 4, 2024—just three months after the end of its comment period on December 4, 2023—FDA’s rule on regulation of laboratory developed tests (“LDTs”) as medical devices is under review by the Office of Information and Regulatory Affairs (“OIRA”) within the Office of Management and Budget (“OMB”). While review by OIRA is capped at 90 days by Executive Order 12866, there is no minimum period required, and therefore action can be taken any time between now and June.

During this election year, FDA’s efforts to push the rule forward fairly quickly is ...

FDA’s January 3, 2024, Federal Register notice soliciting comments on the agency’s plan to implement best practices for guidance development got me thinking.  What do the data show regarding FDA’s performance in moving proposed guidance to final?

If you haven’t read it, the Federal Register notice explains that the Consolidated Appropriations Act of 2023 directs FDA to issue a report identifying best practices for the efficient prioritization, development, issuance, and use of guidance documents and a plan for implementing those practices. The comment period on ...

This post was co-authored by David Schwartz, CEO and Co-Founder at Ethics Through Analytics, and Michael Shumpert, Data Science Executive at Mosaic Data Science.

As you may know, we have been submitting FOIA requests asking FDA to share data from its various programs.  In October, FDA granted[1] our April FOIA request in which we asked the agency to add back demographic data fields that it had previously removed from its public Medical Device Report (“MDRs”) databases. To find potential bias, we encourage manufacturers to use this data to look for any disproportionate impact its ...

Introduction

Frequently, I am asked by clients to predict how long it will take for FDA to review and clear a 510(k).  At a high level, I observe that on average clearance can take 160 days according to the data.  Then, beyond that, I observe that review times are highly variable among differing product codes, and the very first Unpacking Averages post I wrote in October 2021 provided a graphic to show just how much variation there was depending on the technology.  Here, though, I want to dive into yet another separate factor that should be taken into account, the seasonality of FDA ...

Those who have been reading this blog know that I like to analyze collections of documents at FDA to discern, using natural language processing, whether, for example, the agency takes more time to address certain topics than others. This month, continuing the analysis I started in my October post regarding device-related citizens petitions, I used topic modeling on the citizens petitions to see which topics are most frequent, and whether there are significant differences in the amount of time it takes for FDA to make a decision based on the topic.

Discerning the Topics

As you probably ...

Our latest focus is trying to bring data to bear on common questions we get asked by clients.  Last month the topic was: how well does my device need to perform to get premarket clearance from FDA?  This month it is: how big does my sample size need to be for any necessary clinical trial for premarket clearance? 

To date, our typical answer has been, it depends.[1]  We then explain that it’s not really a regulatory question, but a question for a statistician that is driven by the design of the clinical trial.  And the design of the clinical trial is driven by the question the clinical trial is trying ...

On October 31, 2023, FDA hosted a webinar to address some of the frequently asked questions the agency has received since the September 29, 2023 release of its proposed rule on laboratory developed tests (“LDTs”). The materials from the webinar are available on FDA’s CDRH Learn webpage. Importantly, FDA announced during the webinar that the agency does not currently plan to extend the comment period for the proposed rule beyond the standard 60-day timeframe, and therefore, comments are still due on Monday December 4, 2023. In both the preamble to the proposed rule and stated ...

This month I wanted to take a data-driven look at FDA’s treatment of citizen petitions, and specifically as a starting point how quickly the agency resolves those petitions.  Make no mistake, I have an interest in this topic.  Over the more than 35 years I have been practicing law, I have filed multiple petitions including a 1995 petition that successfully caused FDA to adopt Good Guidance Practices.  But more recently, specifically on February 6, 2023, I filed a citizen petition asking FDA to rescind its final guidance on Clinical Decision Support Software.[1]  On August 5, 2023, when we ...

In a last minute push before an anticipated government shutdown, FDA put down its marker for moving forward toward regulation of lab developed tests (“LDTs”). Unlike past proposals from FDA and Capitol Hill, FDA has taken a simple approach: laboratories that make LDTs for clinical use are manufacturing in vitro diagnostic medical devices (“IVDs”) for commercial distribution, and as such must eventually comply with FDA’s already-established IVD requirements. The FDA zeitgeist boils down to this: It doesn’t matter if the lab is large or small, for profit or ...

It’s common for a client to show up at my door and explain that they have performance data on a medical device they have been testing, and for the client to ask me if the performance they found is adequate to obtain FDA clearance through the 510(k) process. I often respond, very helpfully, “it depends.” But for some reason clients aren’t completely satisfied by that.

I then volunteer that a general rule of thumb is 95%, but that this is just a rule of thumb. For Class II medical devices undergoing review through the 510(k) process, the legal standard is that the applicant must show that ...

Introduction

Hardly a day goes by when we don’t see some media report of health care providers experimenting with machine learning, and more recently with generative AI, in the context of patient care. The allure is obvious. But the question is, to what extent do health care providers need to worry about FDA requirements as they use AI?

As discussed in our June Insight, earlier this year FDA publicly announced its development of a proposed rule that would expressly define laboratory developed tests (“LDTs”) as medical devices and subject them to the agency’s regulatory authority. Such a rule would be FDA’s first comprehensive attempt to impose its authority over LDTs since its 2014 draft guidance, which FDA ultimately chose not to finalize, and comes after several failed congressional legislative attempts to do the same.

Recently Colleen and Brad had a debate about whether Medical Device Reports (“MDRs”) tend to trail recalls, or whether MDRs tend to lead to recalls. Both Colleen and Brad have decades of experience in FDA regulation, but we have different impressions on that topic, so we decided to inform the debate with a systematic look at the data. While we can both claim some evidence in support of our respective theses based on the analysis, Brad must admit that Colleen’s thesis that MDRs tend to lag recalls has the stronger evidence. We are no longer friends. At the same time, the actual data didn’t really fit either of our predictions well, so we decided to invite James onto the team to help us figure out what was really going on. He has the unfair advantage of not having made any prior predictions, so he doesn’t have any position he needs to defend.

Introduction

Let’s say FDA proposed a guidance document that would change the definition of “low cholesterol” for health claims.  Now let’s say that when FDA finalized the guidance, instead of addressing that topic, FDA banned Beluga caviar.  If you are interested in Beluga caviar, would you think you had adequate opportunity to comment?  Would you care if FDA argued that Beluga caviar was high in cholesterol so the two documents were related?

It is certainly easy, when writing code to accomplish some data science task, to start taking the data on face value.  In my mind, the data can simply become what they claim to be.  But it’s good to step back and remember the real world in which these data are collected, and how skeptical we need to be regarding their meaning.  I thought this month might be an opportunity to show how two different FDA databases produce quite different results when they should be the same.

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.

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?

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.

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.

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 spur innovation and efficient access to new regenerative medicine products.

FDA Commissioner Scott Gottlieb remarked that the finalization of regenerative therapy guidance ...

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 prime target for hackers.  On January 22, 2019, the U.S. Department of Homeland Security Industrial Control System Cyber Emergency Team (US-CERT) issued another advisory regarding medical device ...

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 employees considered non-essential were furloughed and, consequently, routine regulatory and compliance activities at FDA were put on hold. On his Twitter account (@SGottliebFDA), Scott Gottlieb, M.D., Commissioner of the FDA ...

On December 7, 2018, the U.S. Food and Drug Administration (“FDA”) published a proposed rule (“Proposed Rule”) that, if finalized, would clarify the de novo classification process for medical devices, including (1) the format and contents of a de novo request and (2) the criteria for accepting or denying a de novo request. FDA intends to “enhance regulatory clarity and predictability... [and] provide a regulatory framework that sets clear standards, expectations and processes for de novo classification” through this proposed rulemaking.[1]

FDA regulates medical ...

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 medical devices). Of note were successful challenges to hospital mergers in Pennsylvania (Penn State Hershey Medical Center and Pinnacle Health System), and Illinois (Advocate Health ...

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 Electronic Health Record ("EHR") data to clinical investigators, research institutions and sponsors of clinical research on drugs, biologics, medical devices and combination products conducted under an ...

The Food and Drug Administration ("FDA") recently announced that it will be hosting a public workshop on October 21 and 22, 2014, in Arlington, Virginia, entitled "Collaborative Approaches for Medical Device and Healthcare Cybersecurity."

Officials from FDA, the Department of Health and Human Services ("HHS"), and the Department of Homeland Security ("DHS") will bring together medical device manufacturers, insurers, cybersecurity researchers, trade organizations, government officials, and other stakeholders to discuss the numerous challenges faced in medical device ...

by Wendy C. Goldstein and Kathleen A. Peterson

On December 27, 2011, the U.S. Food & Drug Administration ("FDA"), Office of Prescription Drug Promotion ("OPDP") (formerly the Division of Drug Marketing, Advertising, and Communications) released a new draft guidance document titled "Guidance for Industry on Responding to Unsolicited Requests for Off-Label Information About Prescription Drugs and Medical Devices" (the "Draft Guidance"). The OPDP will accept comments on the Draft Guidance through March 29, 2011.

The FDA has a longstanding policy of permitting ...