Applying Data Science Principles to FDA’s PreCert Program: Some Scary Stuff

Drugs or products approval concept. Rubber stamp with the text FDA approved over white background. 3D illustration

Epstein Becker & Green’s Bradley Merrill Thompson explains why the FDA PreCert Program’s built-in subjectivity and bias toward established entities should worry the healthcare and medical technology industry.

Let’s say I could show you, through reliable data that is statistically significant, that children from wealthy families on average make much better employees in very meaningful ways. Do you think companies ought to adopt policies — perhaps embedded in algorithms — that favor hiring from wealthy families? That sounds pretty un-American.

So why is everyone so excited about creating an easier pathway through FDA for projects that come from certain incumbent companies that have a pedigree demonstrating that they have been more successful in the past? In a nutshell, that’s FDA’s PreCert Program presently under development.

Throughout FDA’s recent work in digital health and beyond, as recently explained by Dr. Amy Abernathy, the principal deputy commissioner and acting CIO of the FDA, the agency has a rather broad plan to advance and indeed lead the use of data science in healthcare through these regulatory reforms. Last summer, FDA released a midyear report describing the agency’s progress in developing the PreCert Program for regulating “software as a medical device” (SaMD). Presumably toward the end of this year, FDA will release a final assessment of its pilot work for the year, and its proposals for the future. Based on Abernathy’s comments, FDA has very ambitious plans for the PreCert Program.

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But not everyone is enamored with what FDA is doing. On October 30, 2019, three US senators including presidential candidate Elizabeth Warren wrote FDA a follow-up letter (to their 2018 letter on the same topic) expressing concern over how: effectively the PreCert Program would protect patients, whether FDA had legal authority for the program, and how exactly FDA planned to make use of real-world evidence collected through the program.

So this seems like an appropriate time to discuss the criteria that stakeholders should use to evaluate the desirability of the PreCert Program. In this article, from that same data science perspective that FDA champions, I will review some of the key criteria that stakeholders should think about when they see the next iteration of the planned program.
Summary of FDA’s PreCert Program

The agency has been working to develop and then pilot the FDA Precertification Program for software since 2017. FDA’s most recent thinking on the program is reflected in a January 2019, 58-page “working model.” But it is still far from done. FDA has been spending 2019 testing some of the elements of the program on a few volunteer companies to see if the program features are administratively feasible. Presumably after this year, when FDA is done testing it, the agency will announce its future plans for the program.

At its heart, the program is about shifting regulatory focus from the software product to the company that develops the software product. Presently, typically a software developer that wants to get marketing approval from FDA would do so through two steps:

1. Voluntarily, under the so-called Q-Submission Program, engaging with FDA in a presubmission meeting to get feedback on what kinds of evidence the agency wants to see in a premarket submission; and

2. Submitting that submission.

Instead of those two steps, FDA would move to a four step process that begins with an initial focus on the company. Those four steps include:

1. An “excellence appraisal” to determine whether the company has a strong enough culture of quality to be admitted to the program.

2. A “review pathway determination” to decide which regulatory process and what kinds of evidence might be needed for a particular software submission. FDA proposes that this meeting be legally voluntary, but is not clear how the program would work without such a meeting.

3. The actual premarket review of the submission.

4. Ongoing collection and reporting to FDA of postmarket performance.

So basically steps one and four are added to the existing process, but the expectation is that the actual review — new step three — would be faster than the old step two because of the prior excellence appraisal and review pathway determination.
The Good in the Precert Program

FDA is thinking — in very creative ways — about how the agency will handle the significant growth in the development of SaMD type products in the future. Creativity is often difficult for a government agency simply because of law and politics, so for FDA to have the courage and vision to proactively pursue novel approaches is frankly wonderful.

Further, I love the idea of using data science, including algorithms, wherever they can be useful. In fact, this fall I enrolled as a graduate student to work on a Masters of Applied Data Science at the University of Michigan (at age 58, I may well be one of the oldest graduate students there.) There is no question that using sophisticated data collection and analysis techniques on FDA compliance issues can be useful.