Related Experiment Video
Updated: Oct 22, 2026
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Strengthening Signal Detection by Proactive Adverse Event Case Identification in Electronic Health Records Using a
Gabriëlla von Kreijfelt1,2, Juliëtte Zwaveling3, Jette A van Lint4
1The Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, The Netherlands. g.j.von_kreijfelt@lumc.nl.
Background:
The spontaneous reporting system is the cornerstone of pharmacovigilance but is limited by substantial underreporting, which may delay safety signal detection.
Objective:
The aim of this study was to evaluate the electronic health record-adverse event search method for identifying and reporting additional potential adverse drug reactions in electronic health records to strengthen routine signal detection.
Methods:
Between August 2022 and August 2025, targeted searches for adverse drug reactions were conducted in approximately 2,750,000 electronic health records from two Dutch hospitals using a data collection text-mining tool. Search criteria were derived from information in spontaneous reports of specific drug-adverse event associations. Structured (e.g. laboratory results, medication prescriptions) and unstructured (e.g. clinical notes, hospital discharge letters) data were used to identify potential adverse drug reactions. Identified cases were manually verified and, when relevant, reported to the Netherlands Pharmacovigilance Centre Lareb and analysed alongside spontaneous reports for signal detection.
Results:
A total of 147 electronic health record-derived cases were reported for 41 drug-adverse event associations. These contributed to 34 signal detection analyses, supporting the assessment of seven safety signals. In 75% of the cases, a suspected causal relationship was documented by healthcare professionals in clinical notes. Electronic health record-derived cases accounted for up to 73% of the reports included in the seven detected signals.
Conclusions:
This study demonstrates that electronic health records can be used as an additional source of individual case safety reports in routine pharmacovigilance practice, supporting the further assessment of selected potential safety signals. Despite limitations related to efficiency, this proactive approach has potential to address underreporting and complement the spontaneous reporting system, strengthening signal detection.