Optimizing Medical Device Post-Market Surveillance for Public Value
The surveillance of medical devices is intended to provide critical information to all relevant stakeholders about device safety, long-term product performance, and effectiveness in improving patient outcomes. However, we are far from this ideal system. Instead, we rely largely upon voluntary reporting systems from providers, health care facility networks, industry, and claims-based or other health care practice databases to characterize risks and benefits of devices in the post-approval environment. There are many shortcomings of these current surveillance systems, and there are no common methods to define the safety signals or the underlying relationships between device, or machine, failure and clinical events. Current analytical approaches typically fail to leverage the complexity and high-dimensionality of health care data sets and single registry efforts are inefficient and not scalable. Electronic health record or claims-based systems currently do not contain the detail needed for longitudinal device evaluation and patient follow-up with outcome assessment.
The prospective registry format, with attention to epidemiological principles of cohort assembly, data collection, and appropriate analysis and inference, has the potential to remedy some of the problems facing medical device surveillance in the United States. A prospective registry network, that leverages data collection and analytic processes, and is capable of monitoring devices across health care facilities, may be the most effective and efficient surveillance model. For such a registry surveillance network model to achieve its promise of accurate, timely, and useful dissemination of product performance to the public, many critical issues need to be addressed.
Forging a partnership between industry, academic institutions, patient groups, government agencies, and other stakeholders to overcome surveillance system challenges, a collaborative surveillance registry network is needed to ensure:
- utilization of the best research methods,
- objective data collection,
- prioritization of which devices to survey under resource constraints,
- credibility and trustworthiness, and
- no single entity is setting standards alone
The Safety Signal Detection Project has been developed to better understand whether use of big data analytic methods, including the use of unstructured data and advanced machine learning analytic techniques, might enhance or complement current post-market surveillance initiatives for medical devices. This project, conducted in collaboration with the Yale Big Data to Knowledge team, will increase our knowledge of signal detection methodologies that are capable of utilizing large, high dimensional health care data sets and is intended to contribute to the ongoing development of the National Medical Device Surveillance System infrastructure. Preliminary results of this work were presented to the FDA's MDEpiNet community.
In addition to working closely with the U.S. Food and Drug Administration (FDA) and the MDEpiNet community, Yale has partnered with the American College of Cardiology (ACC) through use of the National Cardiovascular Data Registry (NCDR®) Implantable Cardioverter Defibrillator (ICD) Registry linked to Medicare administrative claims to support this project. Yale has also partnered with a diverse group of experts from the surveillance and methodology communities to form a Steering Committee to provide guidance and feedback throughout the project's lifecycle.
The Safety Signal Detection Project is part of a larger effort by Yale University, in partnership with Medtronic, Inc., and supported by a five-year cooperative agreement from the FDA's MDEpiNet initiative. The focus of this collaborative is to develop methods and facilitate best practices for medical device surveillance. The cooperative agreement was designed to address the following specific aims:
- Contribute to efforts to develop a generalizable surveillance framework, policies, and procedures to guide collaboration among private industry, regulators, and academic partners.
- Develop strategies to promote transparency and maintain integrity through sharing of device, clinical, and quality data among clinical investigators and other stakeholders.
- Develop novel research methodological approaches to data collection (including device follow-up and Unique Device Identification (UDI) compliance), signal detection, and data analysis that address the current deficiencies that undermine existing medical device surveillance systems.