![]() The pharmacists also registered information on the situation underlying the alert generation and its management (retrieval of additional information and performed actions). These included data of the patient (date of birth, sex) and of the alert itself: drugs involved, type of prescriber (general practitioner, specialist, or other), handwritten or electronic prescription, first dispensing or refill. During four allocated timeslots of 2 h each, spread over the week, the pharmacists analyzed the first six DM alerts that occurred during the dispensing process.įor each alert, details were registered on a structured, pretested form (online Supplementary Appendix 1). ![]() ![]() Each pharmacist was assigned to register a total of 24 DM alerts in daily practice. METHODS Data collectionĭM alerts were collected by community pharmacists who were participating in a postmaster training program between March 2013 and March 2014. Moreover, we aimed to identify characteristics of DM alerts that lead to interventions by pharmacists. Our objective was to investigate the nature of DM alerts and their management by community pharmacists. In community pharmacies there might be even more alerts on duplicate medication than in hospitals, because there is increased likeliness that outpatients receive prescriptions from different prescribers, because community pharmacists are not always informed about changes in therapy, and because of the logistic process-for example, early refill of prescriptions because of holidays. 11 Research on DM alerts in community pharmacy is lacking. 12, 16 In a Dutch hospital setting, 80% of DM alerts were overridden, 1 and only 4.1% of the DM alerts were rated as clinically relevant. Studies in several settings have estimated the proportion of relevant DM alerts from a few percent 15 to up to 70%. 11, 12ĭM alerts are intended to detect inappropriate duplication of therapeutic groups or active ingredients (e.g., the unintentional combination of two different NSAIDs (non-steroidal anti-inflammatory drugs), or the concurrent use of a branded drug and a generic version containing the same active ingredient). 7 Yet, duplicate medication (DM) alerts contribute substantially to the total number of alerts, so their specificity is of similar relevance as that of drug–drug interaction alerts. 2, 4, 8–10 Little is known about most other types of drug therapy alerts. 1–3, 5, 7 Drug–drug interaction alerts have been investigated extensively in this respect. 1–6 When too many alerts are not followed by a clinical intervention and are overridden (apart from the question whether this is appropriate), this contributes to “alert fatigue.” A potential consequence is that relevant drug therapy alerts may be overridden mistakenly. In the majority of cases, however, the alert is judged irrelevant for a particular patient. In some cases these alerts lead to clinical interventions. As the current DM alerts are diverse and nonspecific in detecting situations where external action is considered relevant, other ways of alerting should therefore be considered.Ĭlinical decision support systems, duplicate medication, medication errors, medical order entry systems, community pharmacy services BACKGROUND AND SIGNIFICANCEĬlinical decision support systems (CDSS) generate a continuous flow of drug therapy alerts. 001).ĭiscussion and Conclusion: In community pharmacy, prescription modifications based on DM alerts are rare, but DM alerts lead with some regularity to other actions-for example, patient instruction and update of the electronic patient record. Alerts concerning first dispensing were more likely to be followed by an external action than alerts concerning refills (40% vs 14%, P <. In 32% of the alerts the pharmacists decided that one or more actions were needed: the electronic patient record was updated in 15% of the alerts and in 19% of the alerts the pharmacists performed an external action-for example, informing the patient or modifying the prescription (including 5 therapeutic prescription modifications and 22 logistic prescription modifications). In 17% of the alerts, there was a risk for unintentional concurrent use. In 32% of the alerts, the DM alert was generated for an intentional combination. In half of the 1272 registered alerts, the pharmacists judged that there was no risk for concurrent use of both prescriptions. Results: On average, the clinical decision support systems generated 20.4 DM alerts per 100 dispensed drugs. Each pharmacist registered the nature and management of 24 DM alerts on a structured form. Methods: Observational study in 53 community pharmacies. Objective: To investigate the nature of duplicate medication (DM) alerts, their management by community pharmacists, and potential characteristics of DM alerts that lead to interventions by pharmacists.
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