Continuing my three-part series on FOIA requests using a database of over 120,000 requests filed with FDA over 10 years (2013-22), this month’s post focuses on sorting the requests by topic and then using those topics to dive deeper into FDA response times. In the post last month, I looked at response times in general. This post uses topic modeling, a natural language processing algorithm I’ve used in previous blog posts, including here and here, to discern the major topics of these requests.
Federal agencies in health care publish large amounts of data, and my posts typically analyze that data. To provide more value to readers, I’ve started submitting FOIA requests for unpublished data to produce additional insights into how FDA works. And what better first topic than data on FDA responses to FOIA requests.
Information is important, and thus so is access to it. Our democracy needs to know what’s going on in our government, and businesses trying to navigate the FDA regulatory process likewise need to understand the regulatory process. For both purposes, the FOIA process should be fair and efficient.
FDA has been releasing data on its FOIA process, specifically its FOIA logs, for a few years. For data analysis purposes, those data are missing some important fields such as the date of the final decision. Further, when it comes to looking at the data on the closed cases, the data only go back four years. In my experience, the pandemic years were anomalous in so many ways that we can’t treat any data from the last three years as typical. As a result, I wanted to go back 10 years.
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