Abstract
BackgroundMultiple databases provide ratings of drug-drug interactions. The ratings are often based on different criteria and lack background information on the decision making process. User acceptance of rating systems could be improved by providing a transparent decision path for each category.MethodsWe rated 200 randomly selected potential drug-drug interactions by a transparent decision model developed by our team. The cases were generated from ward round observations and physicians’ queries from an outpatient setting. We compared our ratings to those assigned by a senior clinical pharmacologist and by a standard interaction database, and thus validated the model.ResultsThe decision model rated consistently with the standard database and the pharmacologist in 94 and 156 cases, respectively. In two cases the model decision required correction. Following removal of systematic model construction differences, the DM was fully consistent with other rating systems.ConclusionThe decision model reproducibly rates interactions and elucidates systematic differences. We propose to supply validated decision paths alongside the interaction rating to improve comprehensibility and to enable physicians to interpret the ratings in a clinical context.
Highlights
Multiple databases provide ratings of drug-drug interactions
Design of decision model In designing the DM, we developed a list of binary questions which we considered would impact on the interaction rating
The pharmacist, physician and the clinical pharmacologist independently assessed all cases of potential drug interactions (n = 200). 62 of the interactions yielded no information from Micromedex DrugDex (MMX) regarding possible drugdrug interactions (DDI)
Summary
Multiple databases provide ratings of drug-drug interactions. The ratings are often based on different criteria and lack background information on the decision making process. User acceptance of rating systems could be improved by providing a transparent decision path for each category. The management of adverse drug events (ADEs) is an important issue in healthcare [1]. While some ADEs are unpredictable (e.g. anaphylaxis), ADEs caused by drugdrug interactions (DDI) are likely to be preventable [2]. Clinical decision support software (CDSS) has been used as a supportive measure to improve medication safety [5,6]. The information provided by CDSS focuses on management advice rather than alerts, since more prevalent alerts may dominate less common but dangerous ones [4]
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