Towards a prototype medical system for devices vigilance and patient safety

Abstract

For all healthcare institutions and organizations, patient safety is of the utmost importance. A factor that influences patient safety is the existence (or not) of observed adverse events associated with medical devices. Upon the detection of adverse events, all healthcare providers that own the affected medical devices should be promptly notified. In this paper we present the core of a prototype system for medical devices vigilance and patient safety. We present the architecture of this system, the way that it detects the healthcare providers that need to be notified through an entity matching algorithm, as well as briefly present its user interface.

Challenges Associated with the Safety Signal Detection Process for Medical Devices

“… Matheny, M. E. Ho, K. K. L. Yeh, R. W. Piemonte, T. C. Waldman, H. Shah, P. B. Cope, R. Normand, S. L. T. Donnelly, S. Robbins, S. Resnic, F. S. Circ Cardiovasc Qual Outcomes 2015 January 2008 through December 2012 23,333 PCI cases Yes, National Cardiovascular Data Registry (NCDR) Yes, DELTA No 2 The Kaiser Permanente implant registries: effect on patient safety, quality improvement, cost effectiveness, and research opportunities 27 Retrospective study Paxton, E. W. Inacio, M. C. Kiley, M. L. Perm J 2012 Not mentioned Not mentioned Yes, Kaiser Permanente Orthopedic implant registries No Yes, ICD-9 2 Towards a Prototype Medical System for Devices Vigilance and Patient Safety 9 Review article Deligiannakis, A. Giatrakos, N. Pallikarakis, N. 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health 2014 NA NA Yes, MEdical DEvices VIgilance and Patient Safety (MEDEVIPAS) Yes, multivariate method; entity matching algorithm No 2 …”

“…Of the 12 articles including different SRS, the following SRS were discussed: FDA MAUDE database (US), TGA DAEN database (Australia), the future European Databank on Medical devices (Eudamed) (EU), MHRA database (UK), MEDSUN database (US), Adverse Event Triggered Reporting for Devices (ASTER-D) (US), MEdical DEvices VIgilance and Patient Safety (MEDEVIPAS) (Greece), and the National Electronic Injury Surveillance System (NEISS) (US) 9–20 (see Table 2 ).…”

“…Ten articles described safety signal detection methodologies for medical devices, four articles discussed signal detection methodologies applied to SRS, 9 , 12 , 19 , 20 four articles discussed signal detection methodologies applied to registries, 22–24 , 26 and two articles discussed optimal signal detection methodologies for medical devices without applying the methodology to a specific type of PMS data source. 30 , 31 No articles associated with signal detection methodologies applied to non-standard data sources were identified.…”

“…Of the four articles using signal detection methodologies applied to SRS, two articles discussed disproportionate analysis (DPA) methodologies (Frequentist and Bayesian), 12 , 19 and two articles discussed multivariate methods (change point analysis and entity matching algorithm). 9 , 20 Of the four articles that included signal detection methodologies applied to registries, all four articles discussed methodologies associated with the Data Extraction and Longitudinal Trend Analysis (DELTA) network. 22–24 , 26 …”

“…For complex types of SRS analysis, multivariate approaches have been proposed: change point analysis 20 or entity matching algorithm. 9 These are not yet used for medical device safety signal detection. Methods applied to medical device registries can be categorized into those based on modified DPA ported from spontaneous reporting, and those based on the DELTA network methodology.…”