Use of an On-demand Drug–Drug Interaction Checker by Prescribers and Consultants: A Retrospective Analysis in a Swiss Teaching Hospital

Abstract

BACKGROUND: Offering a drug-drug interaction (DDI) checker on-demand instead of computertriggered alerts is a strategy to avoid alert fatigue. OBJECTIVE: The purpose was to determine the use of such an on-demand tool, implemented in the clinical information system for inpatients. METH-ODS: The study was conducted at the University Hospital Zurich, an 850-bed teaching hospital. The hospital-wide use of the on-demand DDI checker was measured for prescribers and consulting pharmacologists. The number of DDIs identified on-demand was compared to the number that would have resulted by computer-triggering and this was compared to patient-specific recommendations by a consulting pharmacist. RESULTS: The on-demand use was analyzed during treatment of 64,259 inpatients with 1,316,884 prescriptions. The DDI checker was popular with nine consulting pharmacologists (648 checks/consultant). A total of 644 prescribing physicians used it infrequently (eight checks/prescriber). Among prescribers, internists used the tool most frequently and obtained higher numbers of DDIs per check (1.7) compared to surgeons (0.4). A total of 16,553 DDIs were identified on-demand, i.e., <10 % of the number the computer would have triggered (169,192). A pharmacist visiting 922 patients on a medical ward recommended 128 adjustments to prevent DDIs (0.14 recommendations/patient), and 76 % of them were applied by prescribers. In contrast, computer-triggering the DDI checker would have resulted in 45 times more alerts on this ward (6.3 alerts/patient). CONCLUSIONS: The on-demand DDI checker was popular with the consultants only. However, prescribers accepted 76 % of patient-specific recommendations by a pharmacist. The prescribers' limited on-demand use indicates the necessity for developing improved safety concepts, tailored to suit these consumers. Thus, different approaches have to satisfy different target groups. Running TitleUse of an On-demand DDI Checker Keywords (MeSH terms)

Pharmacogenomics‐Based Point‐of‐Care Clinical Decision Support Significantly Alters Drug Prescribing

“…Other studies have suggested that infrequent alerts containing embedded clinical recommendations prove more useful and are less likely to be ignored(39). This also diminishes the problems of alert fatigue(40). We do not, however, propose that an entirely passive pharmacogenomic CDS approach is optimal, and in fact, we are currently studying ways to re-engineer our system to combine passive and active(41) alerts to maximize adoption.…”

Negligible impact of highly patient-specific decision support for potassium-increasing drug-drug interactions – a cluster-randomised controlled trial

“…-non-interruptive notification bar linking to information window that is accessible on demand, -distinct colours for each notification type within the respective information windows of reminders, warnings, and alerts, indicating seriousness, -presentation of interacting drug pair and patient-specific context including laboratory values, -concise instructions with minimal text, signal words and provision of potential clinical consequences, -suggested options (e.g., order potassium measurement) directly available from the information window, -further details available via links, -possibility to enter override reasons which were routinely collected, -and further patient-specific DDI checks -for all potential DDIs featured in the comprehensive knowledge base used -could be triggered on demand [15], while other recommended features were lacking, i.e.,…”

“…The regularly updated medication knowledge base galdat/ hospINDEX (distributed to customers within the Swiss market by HCI Solutions AG, Bern, Switzerland), derived from ABDATA Pharma-Daten Service (Werbe-und Vertriebsgesellschaft Deutscher Apotheker, Eschborn, Germany), is automatically loaded as part of the EHR backend and integrated into the frontends of the EHR system at our institution. This knowledge base is available to clinicians and pharmacologists for reference [15] and was used in this study to identify potassium-increasing DDIs. Of note, all DDIs mentioned in this study should be considered as potential DDIs that did not necessarily induce adverse events.…”

“…Among frequently observed DDIs are potassium-increasing DDIs [12,13], which are of high clinical significance because hyperkalaemia can induce potentially fatal cardiac arrhythmias [14]. Potassium-increasing DDIs are the most frequent high-priority DDIs at our institution [15]. In our previous analyses of inpatients' electronic health record (EHR) data, we identified patient-and physician-related risk factors for hyperkalaemia [16] in order to develop patient-specific models for calculating the risk for hyperkalaemia over time during potassium-increasing DDIs [10].…”

Patient- and physician-related risk factors for hyperkalaemia in potassium-increasing drug–drug interactions

“…However, they are often based on DDI knowledge bases which do not take patient characteristics into account, resulting in a low specificity of alerts, overalerting and alert fatigue [7][8][9][10]. In order to avoid alert fatigue on-demand DDI checks have been implemented into the electronic health record of the University Hospital Zurich [11].…”