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PhD Candidate, Carlson School of Management, University of Minnesota at Twin Cities
Predicting Medical Device Recalls: An Evidence-Informed Deep Learning Approach Leveraging Regulatory Submission Characteristics
Abstract: Medical devices in the US entering the market through the FDA’s predominant 510(k) clearance pathway have garnered significant safety concerns. The medical industry worries that devices approved under this pathway, primarily based on new devices’ equivalency to previously approved devices (predicate devices), may be more likely to encounter recalls. These recalls could impose substantial patient harm and financial strain on the healthcare system. In response, this work proposes a data-driven approach for predicting 510(k) device recalls, aiming to alleviate such safety concerns. Following the design science paradigm and informed by the empirical findings from prior research, our approach leverages the predictive power of the characteristics in the network formed by predicate device citing relationships (predicate network). It incorporates natural language processing and deep learning techniques to tackle three design challenges, including creating the predicate database to construct the predicate network, learning the predicate network structure, and capturing the temporal patterns of predicate network features. Rigorous tests based on 45,236 devices approved from 2003 to 2020 show that our approach significantly outperforms standard prediction models and the performance varies by device subgroups and key attributes applied. The improved medical device recall prediction performance and the analysis insights into the performance variations provide actionable implications for preemptive reactions to potential recalls and improving the current 510(k) pathway requirements to reduce device safety issues.
Bio: Yi is a fifth-year Ph.D. candidate in the Department of Information and Decision Sciences at Carlson School of Management, University of Minnesota. His research interests lie in health IT, design science, network science and economics, and innovation and entrepreneurship. His recent research centers on employing data-driven empirical methodologies to unravel new IT-induced adverse outcomes in healthcare (e.g., medical device recall, human sedentary behavior) and developing evidence-informed algorithmic approaches for these outcomes' predictions. His work has been published in leading academic journals, including Journal of the American Medical Association (JAMA), Journal of General Internal Medicine, and Medical Care Research and Review. He also has a few papers under review in Information Systems Research (ISR) and INFORMS Journal on Computing (IJOC). Yi owns one US provision patent and is the recipient of the national-level NSF Innovation Corps (I-Corps) program grant for technology commercialization and customer discovery, INFORMS ISS Design Science Award, Carlson Ph.D. Student Teaching Award, University of Minnesota Early Innovation Fund, and University of Minnesota BOLD IDEAS grant.