Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
This work addresses the problem of detecting adverse drug events more effectively for healthcare professionals and researchers, but it is incremental as it applies existing methods to a new type of database.
The study applied existing adverse drug event detection methods, originally designed for spontaneous reporting databases, to a UK general practice electronic health-care database and found that this approach may help identify previously undetected signals, with the latter database offering more supplementary information for improved accuracy.
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.