A Deep Learning Approach for Predicting FDA’s 510(k) Medical Device Recalls Using Device Citation Relationships

More than 90% of medical devices in the United States enter the market through the Food and Drug Administration’s 510(k) clearance pathway, which is primarily based on demonstrating the equivalence of new devices (known as applicant devices) to previously cleared devices (known as predicate devices). However, healthcare professionals have raised concerns that applicant devices cleared this way may be more prone to recalls, which can result in substantial patient harm (if patients are exposed to such recalled devices) and financial strain on the healthcare system. In response, this work introduces a data-driven information technology artifact for predicting medical device recalls, aiming to alleviate these safety concerns by augmenting human decisions. In addition to the characteristics of applicant devices themselves, our predictive model leverages the characteristics of the network formed by predicate device citation relationships (predicate network). It incorporates various deep learning techniques to tackle three predictive model design challenges, including learning the predicate network structure, capturing the temporal patterns of predicate network characteristics, and accounting for the dependencies across the predicate citation history. We show with 45,398 medical devices cleared between 2003 and 2020 that our approach substantially improves the recall prediction accuracy and timeliness over existing state-of-the-art approaches. The improved recall prediction performance and the insights into the performance variations across device categories contribute to the literature on health information systems for societal good as they provide opportunities for preemptive actions to potential recalls and for improving the safety of devices cleared through the 510(k) pathway.

History: Ahmed Abbasi, Senior Editor; Jingjing Zhang, Associate Editor.

Funding: This work was supported by the National Science Foundation [Grant 2334058].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2024.1351.