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. STUDY DESIGN: Prospective study over two months. SETTING: Five hundred and ten-bed university paediatric hospital. MAIN OUTCOME MEASURES: 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.
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. STUDY DESIGN: Prospective study over two months. SETTING: Five hundred and ten-bed university paediatric hospital. MAIN OUTCOME MEASURES: 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.
Authors: Buster Mannheimer; Björn Wettermark; Michael Lundberg; Hans Pettersson; Christer von Bahr; Erik Eliasson Journal: Br J Clin Pharmacol Date: 2010-04 Impact factor: 4.335