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Would it surprise you if I told you that a popular and well-respected machine learning algorithm developed to predict the onset of sepsis has shown some evidence of racial bias?[1]  How can that be, you might ask, for an algorithm that is simply grounded in biology and medical data?  I’ll tell you, but I’m not going to focus on one particular algorithm.  Instead, I will use this opportunity to talk about the dozens and dozens of sepsis algorithms out there.  And frankly, because the design of these algorithms mimics many other clinical algorithms, these comments will be applicable to clinical algorithms generally.…

Continue Reading Unpacking Averages: Understanding the Potential for Bias in a Sepsis Prediction Algorithm, a Case Study

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In this episode of the Diagnosing Health Care Podcast:  A complex landscape of state laws overlays the direct access testing model, ranging from physician order requirements, such as telemedicine standards and the corporate practice of medicine doctrine, to specimen collection considerations, including how the varying options for collection could impact a model.

How do these factors combine to create a roadmap for companies navigating the direct access testing industry?…

Continue Reading Podcast: Direct Access Laboratory Testing – Physician Orders and Specimen Collection – Diagnosing Health Care

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In this episode of the Diagnosing Health Care Podcast:   The U.S. Food and Drug Administration (FDA) recently issued a final guidance document clarifying how the agency intends to regulate clinical decision support (CDS) software.

How has this document caused confusion for industry? How can companies respond?…

Continue Reading Podcast: Unpacking FDA’s Final Clinical Decision Support Guidance – Diagnosing Health Care

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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?
Continue Reading Unpacking Averages: Using NLP to Assess FDA’s Compliance with Notice and Comment in Guidance Development

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The regulatory environment at the US Food and Drug Administration (“FDA”) has a tremendous impact on how companies operate, and consequently data on that environment can be quite useful in business planning.  In keeping with the theme of these posts of unpacking averages, it’s important to drill down sufficiently to get a sense of the regulatory environment in which a particular company operates rather than rely on more global averages for the entire medical device industry.  On the other hand, getting too specific in the data and focusing on one particular product category can prevent a company from seeing the forest for the trees.

Recently, I was asked by companies interested in the field of digital medical devices used in the care of people with diabetes to help them assess trends in the regulatory environment.  To do that, I decided to create an index that would capture the regulatory environment for medium risk digital diabetes devices, trying to avoid getting too specific but also avoiding global data on all medical devices.  In this sense, the index is like any other index, such as the Standard & Poor 500, which is used to assess the economic performance of the largest companies in terms of capitalization.  My plan was to first define an index of product codes for these medium risk digital diabetes products, then use that index to assess the regulatory environment in both premarket and postmarket regulatory requirements.
Continue Reading Unpacking Averages: Creating an Industry-Specific Index to Track the FDA Regulatory Environment

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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.
Continue Reading Unpacking Averages: The Difference Between Data and the Truth: Comparing FDA’s UDI Database with FDA’s 510(k) Database

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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

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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

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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

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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