  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
potential 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 the time of submission. 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 and 12,000
national and international manufacturers from over 65 countries 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 38.9% improvement in the recall rate and a 43.0% reduction
in the FDA's workload. Our analyses also indicate that implementing our policy
could result in significant annual cost-savings ranging between \$2.4 billion
and \$2.7 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.
