Medical device recalls are essential for patient safety, yet inefficiencies in current recall processes lead to delays and increased risks. This research focuses on enhancing recall protocols, particularly for neurosurgical devices and Software as a Medical Device (SaMD). Recent FDA recalls, such as the Medtronic StealthStation S8 and Duet External Drainage System, highlight vulnerabilities in recall communication, software safety, and regulatory oversight [1]. This study proposes a comprehensive framework to improve recall efficiency through electronic notifications, risk mitigation protocols, and legislative modifications.
A key issue is the reliance on passive surveillance systems like the MAUDE database, which is often incomplete and delayed [2]. Additionally, devices cleared via the 510(k) pathway show significantly higher recall rates than those approved through Premarket Approval (PMA) [3]. The proposed research includes analyzing historical recall data and identifying systemic gaps. A legislative revision of the Medical Device Recall Improvement Act (S. 2907) will be explored to mandate uniform recall notifications and stricter reporting guidelines [4].
Special emphasis is placed on SaMD, where software errors can introduce critical patient risks. Implementing predictive analytics and AI-driven anomaly detection can proactively identify failures before widespread clinical impact [5]. The study also recommends increased regulatory oversight for modular software updates, ensuring that patches do not introduce new vulnerabilities.
This research aims to provide a robust, legally enforceable recall framework that enhances patient safety, reduces recall resolution time, and strengthens compliance across the medical device industry. The outcomes will serve as a model for broader regulatory improvements beyond neurosurgery and SaMD.
U.S. Food and Drug Administration. Medical Device Recalls [Internet]. 2024 [cited 2024 Jan 30]. Available from: https://www.fda.gov/medical-devices/medical-device-recalls
JAMA Network. Limitations of the MAUDE Database [Internet]. 2023 [cited 2024 Jan 30]. Available from: https://jamanetwork.com/
Zuckerman D, Brown P, Nissen S. Medical device recalls and the FDA approval process. Arch Intern Med. 2011;171(11):1006-1011.
U.S. Congress. Medical Device Recall Improvement Act (S. 2907) [Internet]. 2024 [cited 2024 Jan 30]. Available from: https://www.congress.gov/
Topol E. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56.
This project aims to enhance the efficiency of FDA medical device recalls, particularly for neurosurgical devices and Software as a Medical Device (SaMD). The goal is to identify systemic gaps in current recall processes, improve regulatory oversight, and develop a more proactive, real-time reporting system to mitigate patient risk. We will achieve this by:
Analyzing historical recall data and identifying trends in delays and inefficiencies.
Proposing legislative improvements to strengthen recall protocols and enforce standardized reporting.
Collaborating with regulatory experts, clinicians, and engineers to refine our framework.
The funding will be allocated for:
Data Access & Analysis: Obtaining paywalled databases, proprietary software failure reports, and legal documentation.
Technical Development:Planning a predictive analytics system for recall efficiency.
Research & Publications: Conducting structured reviews and publishing findings in peer-reviewed journals.
Team Support: Stipends for undergraduate and MD student researchers assisting with data collection and analysis.
Conference Participation: Presenting findings at regulatory and medical device safety conferences.
The team consists of:
Principal Investigator : Chief Neurosurgeon and Associate Professor
Catherine (Research Lead) – Former Clinician, Expertise in regulatory compliance, biomedical engineering, and medical device safety.
Legal Team
MD Student Researchers – Supporting data analysis, literature review, and technical documentation.
Track record: We have experience in conducting interdisciplinary research, security for electronic health records, medical device design, and regulatory policy evaluation. Previous projects have resulted in peer-reviewed publications, and collaborations with industry leaders.
If the project fails, it will likely be due to:
Limited access to proprietary recall data, restricting the ability to perform a comprehensive analysis.
Regulatory inertia, making it difficult to implement proposed legislative changes.
Technical challenges in developing real-time predictive analytics.
Potential outcomes:
Partial findings could still be published to guide future regulatory discussions.
Key insights from the research could inform ongoing FDA policy revisions.
The project could pivot toward industry partnerships for continued development.
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