Challenges Associated with the Safety Signal Detection Process for Medical Devices

Abstract

Background Previous safety issues involving medical devices have stressed the need for better safety signal detection. Various European Union (EU) national competent authorities have started to focus on strengthening the analysis of vigilance data. Consequently, article 90 of the new EU regulation states that the European Commission shall put in place systems and processes to actively monitor medical device safety signals. Methods A systematic literature review was conducted to synthesize the current state of knowledge and investigate the present tools used for medical device safety signal detection. An electronic literature search was performed in Embase, Medline, Cochrane, Web of science, and Google scholar from inception until January 2017. Articles that included terms related to medical devices and terms associated with safety were selected. A further selection was based on the abstract review. A full review of the remaining articles was conducted to decide on which articles finally to consider relevant for this review. Completeness was assessed based on the content of the articles. Results Our search resulted in a total of 20,819 articles, of which 24 met the inclusion criteria and were subject to data extraction and completeness scoring. A wide range of data sources, especially spontaneous reporting systems and registries, used for the detection and assessment of product problems and patient harms associated with the use of medical devices, were studied. Coding is remarkably heterogeneous, no agreement on the preferred methods for signal detection exists, and no gold standard for signal detection has been established thus far. Conclusion Data source harmonization, the development of gold standard signal detection methodologies and the standardization of coding dictionaries are amongst the recommendations to support the implementation of a new proactive approach to signal detection. The new safety surveillance system will be able to use real-world evidence to support regulatory decision-making across all jurisdictions.

Analysis of Medical Device Reports Involving Ventilator-Related Incidents in a Clinical Setting

“…Continued education on the use of medical devices by clinical staff, training on the importance of internal incident reporting in hospitals, and a robust medical device hospital surveillance system are emphasized across the clinical community. [13][14][15][16] In addition, HTMs can help ensure patient safety throughout the lifespan of a medical device by helping to identify and report concerns to MedSun. 17 The findings and lessons learned from this retrospective analysis, as well as the impact of the MDRs presented, further highlight the importance of quality, early-stage adverse event reporting to FDA.…”

“…The medical device reports reviewed in this analysis indicate that HTM input can help reduce the likelihood of adverse events through increased awareness of device performance and limitations. Continued education on the use of medical devices by clinical staff, training on the importance of internal incident reporting in hospitals, and a robust medical device hospital surveillance system are emphasized across the clinical community 13-16 . In addition, HTMs can help ensure patient safety throughout the lifespan of a medical device by helping to identify and report concerns to MedSun 17 .…”

Applications of Blockchain-Based Technology for Healthcare Devices Post-market Surveillance

“…It is essential to establish a secure environment for the swift and accurate sharing of data with authorized parties. One of the primary challenges in the safety signal detection process for medical devices is the timely exchange of PMS data between various sources and documents [ 7 , 18 ]. Recent public health crises have underscored the consequences of delayed identification of safety issues associated with promoted medical equipment.…”

Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices’ Post-Market Surveillance

“…While the main quantitative method for MD safety signal detection is disproportionality analysis [ 5 ], also others have been proposed, such as multivariate methods (change point analysis [ 6 ] and entity matching algorithm [ 7 ]), or methods based on the Data Extraction and Longitudinal Trend Analysis (DELTA) network. As a result, no agreement on the preferred methods for signal detection exists, and no gold standard for signal detection has been established so far [ 8 ].…”

“…In principle, therefore, it will be possible to use EUDAMED to analyse safety signals, but there are delays in implementing its full operationality and it will take years to populate it with sufficient and properly collected data. Thus, a need has arisen to develop methods of aggregating data that can be applied to information that is already available, but incomplete and fragmented, not harmonized, from national sources [ 8 ].…”