Analysis of overridden alerts in a drug-drug interaction detection system.
OBJECTIVE
The aim of this study was to evaluate the relevance of the signals generated by a computerized drug-drug interaction detection system and to design a classification of overridden drug-drug interaction alerts.


METHODS
Prospective study over two months.


METHODS
Five hundred and ten-bed university paediatric hospital.


METHODS
In Robert Debré Hospital physicians generate drug orders online using a computerized physician order entry system that also detects drug-drug interactions in real time. We analysed the relevance of a sample of alerts overridden by physicians.


RESULTS
We analysed a sample of 613 overridden alerts. We defined three categories of overridden alerts: informational errors (35); system errors (244) and accurate alerts (334). Two reasons account for 40% of false-positive alerts: an inability of the system to recognize real conflicts between drug treatments and guidelines stating that the two drugs can be used together, because the benefit outweighs the risk of side effects due to the drug-drug interaction.


CONCLUSIONS
We created a classification of overridden alerts, in the context of computerized physician order entry system coupled with a drug-drug interaction detection system. There is clearly room for improvement in the development of drug-drug interaction software. This classification should make it possible to break this work down into smaller tasks, making it possible to decrease the sensitivity to background noise of drug-drug interaction detection systems.
Mille
Frédéric
F
AP-HP Hôpital Robert Debré, service pharmacie, 48 boulevard serrurier 75018 Paris. fredericmille@rdb.aphp.fr
Schwartz
Céline
C
Brion
Francoise
F
Fontan
Jean-Eudes
JE
Bourdon
Olivier
O
Degoulet
Patrice
P
Jaulent
Marie-Christine
MC
D004347:Drug Interactions; D006776:Hospitals, Pediatric; D006785:Hospitals, University; D006801:Humans; D008508:Medication Errors / Q000517:prevention & control; D008509:Medication Systems; D011446:Prospective Studies; D018570:Risk Assessment