Summary
Background: Adverse drug events (ADE) involving or not involving medication errors (ME) are common,
but frequently remain undetected as such. Presently, the majority of available clinical
decision support systems (CDSS) relies mostly on coded medication data for the generation
of drug alerts. It was the aim of our study to identify the key types of data required
for the adequate detection and classification of adverse drug events (ADE) and medication
errors (ME) in patients presenting at an emergency department (ED).
Methods: As part of a prospective study, ADE and ME were identified in 1510 patients presenting
at the ED of an university teaching hospital by an interdisciplinary panel of specialists
in emergency medicine, clinical pharmacology and pharmacy. For each ADE and ME the
required different clinical data sources (i.e. information items such as acute clinical
symptoms, underlying diseases, laboratory values or ECG) for the detection and correct
classification were evaluated.
Results: Of all 739 ADE identified 387 (52.4%), 298 (40.3%), 54 (7.3%), respectively, required
one, two, or three, more information items to be detected and correctly classified.
Only 68 (10.2%) of the ME were simple drug-drug interactions that could be identified
based on medication data alone while 381 (57.5%), 181 (27.3%) and 33 (5.0%) of the
ME required one, two or three additional information items, respectively, for detection
and clinical classification.
Conclusions: Only 10% of all ME observed in emergency patients could be identified on the basis
of medication data alone. Focusing electronic decisions support on more easily available
drug data alone may lead to an under-detection of clinically relevant ADE and ME.
Keywords
Adverse drug event - medication error - side effect - medication data - prospective
study - clinical decision support system
