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Harmonizing Safety and Speed: A Human-Algorithm Approach to Enhance the FDA's Medical Device Clearance Policy

The United States Food and Drug Administration's (FDA's) Premarket Notification 510(k) pathway allows manufacturers to gain approval for a medical device by demonstrating its substantial equivalence to another legally marketed device. However, the inherent ambiguity of this regulatory procedure has led to high recall rates for many devices cleared through this pathway. This trend has raised significant concerns regarding the efficacy of the FDA's current approach, prompting a reassessment of the 510(k) regulatory framework. In this paper, we develop a combined human-algorithm approach to assist the FDA in improving its 510(k) medical device clearance process by reducing the risk of recalls and the workload imposed on the FDA. We first develop machine learning methods to estimate the risk of recall of 510(k) medical devices based on the information available at submission time. We then propose a data-driven clearance policy that recommends acceptance, rejection, or deferral to FDA's committees for in-depth evaluation. We conduct an empirical study using a unique large-scale dataset of over 31,000 medical devices that we assembled based on data sources from the FDA and Centers for Medicare and Medicaid Service (CMS). A conservative evaluation of our proposed policy based on this data shows a 32.9% improvement in the recall rate and a 40.5% reduction in the FDA's workload. Our analyses also indicate that implementing our policy could result in significant annual cost savings of $1.7 billion, which highlights the value of using a holistic and data-driven approach to improve the FDA's current 510(k) medical device evaluation pathway.

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