Incidence of potential drug interactions in a transplant centre setting and relevance of electronic alerts for clinical practice support

Abstract

Background Adverse drug events may occur as a result of drug-drug interactions (DDIs). Information technology (IT) systems can be an important decision-making tool for healthcare workers to identify DDIs. Objective The aim of the study is to analyse drug prescriptions in our main hospital units, in order to measure the incidence and severity of potential DDIs. The utility of clinical decision-support systems (CDSSs) and computerised physician order entry (CPOE) in term of alerts adherence was also assessed. DDIs were assessed using a Micromedex

Real clinical impact of drug–drug interactions of immunosuppressants in transplant patients

“…To this end, we have previously analyzed four databases of interactions and in order to compare and try to correct possible discrepancies between them, we have consulted technical data sheets and tertiary sources. This integration allows the physician to detect pDDIs at the time of prescribing, without excessive alarms 14 , 50 reducing alert fatigue, and allowing the pharmacist to validate the prescription of all transplanted patients admitted to the hospital. In addition, once the integration is complete, the assisted prescribing system enables an update in case new clinically important pDDIs appear in the literature.…”

Physicians' acceptance of drug-drug interaction alerts from clinical decision support systems: A systematic review and meta-analysis

“…Most studies selected for this systematic review were conducted in the United States (n = 4) [21,22,23,24]. The remaining studies were performed in various countries, including South Korea (n = 2) [25,26], Japan (n = 1) [27], Israel (n = 2) [28, 29], Germany (n = 1) [30], Switzerland (n = 1) [31], Belgium (n = 1) [32], Spain (n = 1) [33], Italy (n = 1) [34], Sweden (n = 1) [35], and the Kingdom of Saudi Arabia (n = 1) [36] (Table 1). All studies were conducted from 2012 to 2022; the oldest ones were carried out by Polidori et al (Italy) [34] and Fritz et al (Switzerland) [31] in 2012, and the most recent by Alsaidan et al and Tukukino et al in 2022 [35, 36].…”

“…The remaining studies were performed in various countries, including South Korea (n = 2) [25,26], Japan (n = 1) [27], Israel (n = 2) [28, 29], Germany (n = 1) [30], Switzerland (n = 1) [31], Belgium (n = 1) [32], Spain (n = 1) [33], Italy (n = 1) [34], Sweden (n = 1) [35], and the Kingdom of Saudi Arabia (n = 1) [36] (Table 1). All studies were conducted from 2012 to 2022; the oldest ones were carried out by Polidori et al (Italy) [34] and Fritz et al (Switzerland) [31] in 2012, and the most recent by Alsaidan et al and Tukukino et al in 2022 [35, 36]. The follow-up time of prescriptions made by physicians using a clinical physician order entry (CPOE) varied, with the shortest being just seven days [30] and the longest being 46 months [25].…”

“…The follow-up time of prescriptions made by physicians using a clinical physician order entry (CPOE) varied, with the shortest being just seven days [30] and the longest being 46 months [25]. As for the study design, there was one longitudinal observation study [26], ten cross-sectional studies [21,23,24,25,27,30,32,34,35,36], four prospective longitudinal studies [22,29,31,33], and one study that performed retrospective cross-sectional and prospective longitudinal studies [28] (Table 1).…”

“…Regarding the studies' research settings, two were conducted in three primary care hospitals [21,35], six studies in a tertiary care hospital [25,26,28,29,31,33], three studies in a quaternary care hospital [22,30,34], and ve studies did not report the healthcare settings [23,24,27,32,36], so we counted them as general hospitals. Among these healthcare settings, most employed commercial CDSS and drug information databases (n = 12) [21,22,23,24,28,29,30,31,32,33,34,36], and four used a system and database, at least partly, developed by the public health system [25,26,27,35] (Table 1).…”