Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database

TL;DR

This study compares existing adverse drug event detection methods applied to spontaneous reporting and electronic health-care databases, showing that the latter can uncover previously undetected signals and provide richer information for more accurate identification.

Contribution

It demonstrates the applicability of spontaneous reporting methods to electronic health-care data and highlights the potential for improved adverse event detection using general practice databases.

Findings

Methods applied to EHR data can detect signals missed in spontaneous reports.

EHR data offers more comprehensive information for adverse event analysis.

Applying existing techniques to EHR data enhances detection of adverse drug events.

Abstract

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information, under reporting and incorrect entries. This often results in a detection lag or prevents the detection of some adverse drug events. These limitations do not occur in electronic health-care databases. In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared. The results suggests that the application of existing methods to the general practice database may help find signals that have gone undetected when using the spontaneous reporting system database. In addition the general practice database provides far more supplementary information, that if incorporated in analysis could provide a wealth of information for identifying adverse events more accurately.