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Objective: The management of drug-drug interactions (DDIs) involves a complex risk-benefit assessment, in which patients' preferences should be taken into account. The aim of this study was to examine the aspects influencing patients' preferences with regard to DDI management options., Methods: A qualitative study consisting of five focus groups with patients chronically using cardiovascular drugs was conducted. Key questions concerned preferences regarding DDI management options for a provided fictitious DDI. Thematic analysis of the verbatim transcripts was performed., Results: Despite their limited knowledge with respect to DDIs, patients easily chose a management option for the presented DDI. When additional information was provided, preferences showed to be fluid. Ten interdependent aspects influencing preferences were derived from patients' argumentations: risk perception, fear, acceptance of uncertainty, openness to change, willingness to take risk, trust in health care professional, financial & practical burdens, health condition, experience, and knowledge & assumptions., Conclusion: Patients' preferences regarding DDI management options were often determined by provided information. Preferences were dependent on an interplay of diverse aspects., Practice Implications: Tailored provision of information and individualized counseling is needed for active patient involvement in DDI decision making., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Introduction: The management of drug-drug interactions (DDIs) is a complex process in which risk-benefit assessments should be combined with the patient's perspective., Objective: The aim of this study was to determine patients' and pharmacists' preferences regarding DDI management., Methods: We conducted a choice-based conjoint survey about a fictitious DDI concerning the combination of a cardiovascular drug and an antibiotic for pneumonia. Patients and pharmacists had to choose 12 times between two management options. The options were described by five attributes, including risk, benefit and practical consequences. Each attribute could have two different levels, which were varied over the choice tasks. Latent class analysis was used to identify potential classes of respondents with distinct patterns of similar preferences., Results: In total, 298 patients and 178 pharmacists completed the questionnaire. The latent class model for both patients and pharmacists resulted in three classes. For patients, in one class the most importance was attached to avoiding switch of medication (class probability 20%), in a second class to fewer adverse events (41%), and in a third class to blood sampling (39%). For pharmacists, again one class attached the highest importance to avoiding switch of medication (31%). The other classes gave priority to curing pneumonia (31%) and avoiding blood sampling (38%)., Conclusion: The results showed diverging preferences regarding DDI management among both patients and pharmacists. Different groups attached different value to risk and benefit versus practical considerations. Awareness of existing variability in preferences among and between pharmacists and patients is a step towards shared decision making in DDI management.

Objective: Drug interaction alerts (drug-drug and drug-disease interaction alerts) for chronic medications substantially contribute to alert fatigue in primary care. The aim of this study was to determine which events require (re)assessment of a drug interaction and whether using these events as triggers in clinical decision support systems (CDSSs) would affect the alert rate., Methods: Two random 5% data samples from the CDSSs of 123 community pharmacies were used: dataset 1 and 2. The top 10 of most frequent drug interaction alerts not involving laboratory values were selected. To reach consensus on events that should trigger alerts (e.g. first time dispensing, dose modification) for these drug interactions, a two-step consensus process was used. An expert panel of community pharmacists participated in an online survey and a subsequent consensus meeting. A CDSS with alerts based on the consensus was simulated in both datasets., Results: Dataset 1 and 2 together contained 1,672,169 prescriptions which led to 591,073 alerts. Consensus on events requiring alerts was reached for the ten selected drug interactions. The simulation showed a reduction of the alert rate of 93.0% for the ten selected drug interactions (comparable for dataset 1 and 2), corresponding with a 28.3% decrease of the overall drug interaction alert rate., Conclusion: By consensus-based better specification of the events that trigger drug interaction alerts in primary care, the alert rate for these drug interactions was reduced by over 90%. This promising approach deserves further investigation to assess its consequences and applicability in daily practice., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: We aimed to investigate to what extent clustering of related drug interaction alerts (drug-drug and drug-disease interaction alerts) would decrease the alert rate in clinical decision support systems (CDSSs)., Methods: We conducted a retrospective analysis of drug interaction alerts generated by CDSSs in community pharmacies. Frequently generated combinations of alerts were analyzed for associations in a 5% random data sample (dataset 1). Alert combinations with similar management recommendations were defined as clusters. The alert rate was assessed by simulating a CDSS generating 1 alert per cluster per patient instead of separate alerts. The simulation was performed in dataset 1 and replicated in another 5% data sample (dataset 2)., Results: Data were extracted from the CDSSs of 123 community pharmacies. Dataset 1 consisted of 841 572 dispensed prescriptions and 298 261 drug interaction alerts. Dataset 2 was comparable. Twenty-two frequently occurring alert combinations were identified. Analysis of these associated alert combinations for similar management recommendations resulted in 3 clusters (related to renal function, electrolytes, diabetes, and cardiovascular diseases). Using the clusters in alert generation reduced the alert rate within these clusters by 53-70%. The overall number of drug interaction alerts was reduced by 11% in dataset 1 and by 12% in dataset 2. This corresponds to a decrease of 21 alerts per pharmacy per day., Discussion and Conclusion: Using clusters of drug interaction alerts with similar management recommendations in CDSSs can substantially decrease the overall alert rate. Further research is needed to establish the applicability of this concept in daily practice., (© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Purpose: The purpose of this study is to investigate the nature, frequency, and determinants of drug therapy alerts generated by a clinical decision support system (CDSS) in community pharmacy in order to propose CDSS improvement strategies., Methods: This is a retrospective analysis of dispensed drugs and drug therapy alerts generated by a CDSS in community pharmacies., Results: Data were extracted from the CDSS of 123 community pharmacies. After taking a 10% random sample of patients with a prescription in the period August 2013-July 2014, 1,672,169 dispensed prescriptions from 81,742 patients were included in the analysis. Of all processed prescriptions, 43% led to one or more drug safety alerts, most frequently drug-drug interaction alerts (15% of all prescriptions), drug-disease interaction alerts (14%), duplicate medication alerts (13%), and dosing alerts (7%). The majority of prescriptions with alerts (80%) were clustered in a minority of patients (16%). The therapeutic drug group of the prescribed drug was the most important determinant of alert generation. Prescriptions for antithrombotic agents accounted for 9.4% of all prescriptions with an alert, beta-blocking agents for 7.5% and angiotensin-converting-enzyme inhibitors for 6.1%., Discussion and Conclusion: The investigated CDSS in Dutch community pharmacy generated one or more drug therapy alerts in nearly half of the processed prescriptions. The majority of alerts were concentrated in a minority of therapeutic drug groups and patients. To decrease the alert burden, CDSS improvements should be directed at the prioritization and integration of drug therapy alerts for these therapeutic groups within patients., (Copyright © 2015 John Wiley & Sons, Ltd.)

The drug interaction between proton pump inhibitors and clopidogrel has been the subject of much study in recent years. Contradictory results regarding the effect of proton pump inhibitors on platelet reactivity and on clinical outcome in clopidogrel-treated patients have been reported in literature. Concomitant use of omeprazole and clopidogrel was found to decrease the exposure (AUC) to clopidogrel's active metabolite by 50% and to sharply increase platelet reactivity, as a result of inhibition by omeprazole of CYP2C19, a cytochrome P450 (CYP) enzyme. Pantoprazole has a much weaker effect on clopidogrel's pharmacokinetics and on platelet reactivity during concomitant use. The influence of the other proton pump inhibitors when used simultaneously with clopidogrel has not yet been investigated in adequately randomized studies. Regulatory agencies state that the combination of clopidogrel and the CYP2C19 inhibitors omeprazole and esomeprazole should be avoided. To date, there is no conclusive evidence of a clinically-relevant interaction between any of the proton pump inhibitors and clopidogrel.