Unpacking 2012 picture pro#
Unpacking 2012 picture code#

On the left-hand side, we have rapid acceleration from the quickest reviews of about 50 days up to about 100. Big PictureĪt each end of the graph, we see sharp nonlinear growth, presumably for what are in a sense outliers.

Here we just want to tease apart the variance. At any rate, this data does not give us information on why certain product codes would have higher review times. But it’s also pretty obvious that a lot of administrative circumstances impact review times, such as high reviewer turnover in a branch. Novelty is also important, although novelty is less of a factor in the 510(k) process because the process is based on a substantial equivalence claim. But in the years that we’ve been doing this work, those are not by themselves reliable predictors of how long a review will take. Certainly it would seem that higher risk and complexity would be likely to lead to higher review times. We would note that we aren’t entirely sure the range of factors that drive review times. In the chart, we went with the simple average of submissions, as that is most akin to the data that FDA typically publishes.

Unpacking 2012 picture pro#

It’s apparently lower because some of the quicker pro codes apparently have more devices in them.

The average of all the 25,000 submissions was 163.5 days.

Unpacking 2012 picture code#

The average of the pro code averages was 176.5 days. But that ignores the fact that some pro codes have lots more products than others. We wanted to add in an average, and the most natural probably would’ve been the average of the pro code averages included in the graphic. The x-axis in the graph is simply all of the product codes sorted by average review time from the quickest to the longest. We calculated averages for each individual pro code. To calculate the review time, we used the difference between date received and date decided, although we realize that FDA has additional data that it uses to calculate its actual review time in a more nuanced way, differentiating between time at FDA and time where the clock is turned off because the manufacturer is supposed to be doing something. That sorting left us with just over 25,000 submissions, and 852 pro codes used. We wanted to get rid of anything that was simply too anecdotal, too noisy. We then removed any product codes that had three or fewer submissions during that time. We further filtered for traditional 510(k)s because special and abbreviated submissions have different review processes, and likewise we removed any that had received expedited review. Data older than that are probably not terribly relevant. Using data available from openFDA, we selected the data for the last almost 12 years, from Januuntil September 1, 2021, based on the date FDA receives the premarket notification. We want to understand FDA’s performance generally with regard to review times associated with 510(k)s across all medical devices. This is an interactive chart that you can explore by clicking on the colors in the legend to see how specific therapeutic areas stack up against the average. In each case, we will offer a few preliminary observations on the data, but we would encourage a conversation around what others see in the data. For many averages, there’s a high degree of variability, and it’s important for industry to have a deeper understanding. In this column, in the coming months we are going to dig into the data regarding FDA regulation of medical products, deeper than the averages that FDA publishes in connection with its user fee obligations.