Adverse Drug Event Notification System: Reusing Clinical Patient Data for Semi-automatic ADE Detection

摘要

Adverse drug events (ADEs) are common, costly and a public health issue. Today, their detection relies on medical chart review and spontaneous reports, but this is known to be rather ineffective. Along with the increasing availability of clinical patient data in electronic health records (EHRs), a computer-based ADE detection has a tremendous potential to contribute to patient safety. Current ADE detection systems are very specific, usually built directly on top of clinical information systems through proprietary interfaces. Thus, it is not possible to run different ADE detection tools on top of already existing systems in an ad-hoc manner. The European project "SALUS" aims at providing the necessary infrastructure and toolset for accessing and analyzing clinical patient data of heterogeneous clinical information systems. This paper highlights the SALUS ADE notification system as the key tool to enable a semi-automatic ADE detection and notification. In contrast to previous work, the ADE notification system is not restricted to a specific clinical environment. It can be run on different clinical data models with different levels of data quality. The system is equipped with innovative features, building up an intelligent, comprehensive ADE detection and notification system that promises a profound impact in the domain of computer-based ADE detection.