Preventing potential drug-drug interactions through alerting decision support systems: A clinical context based methodology. (July 2019)

• Record Type: Journal Article
• Authors: Pirnejad, Habibollah; Amiri, Parasto; Niazkhani, Zahra; Shiva, Afshin; Makhdoomi, Khadijeh; Abkhiz, Saeed; van der Sijs, Heleen; Bal, Roland
• Is Part Of: International journal of medical informatics. Volume 127(2019)
• Page Start: 18
• Page End: 26
• Publication Date: 2019-07
• Subjects: Patient safety -- Potential drug-drug interaction -- Alert fatigue -- Adverse drug events -- Kidney transplantation -- Clinical decision support systems -- Medication alerting system; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.04.006
• Languages: English
• ISSNs: 1386-5056

Abstract

Abstract: Background: The effectiveness of the clinical decision support systems (CDSSs) is hampered by frequent workflow interruptions and alert fatigue because of alerts with little or no clinical relevance. In this paper, we reported a methodology through which we applied knowledge from the clinical context and the international recommendations to develop a potential drug-drug interaction (pDDI) CDSS in the field of kidney transplantation. Methods: Prescriptions of five nephrologists were prospectively recorded through non-participatory observations for two months. The Medscape multi-drug interaction checker tool was used to detect pDDIs. Alongside the Stockley's drug interactions reference, our clinicians were consulted with respect to the clinical relevance of detected pDDIs. We performed semi-structured interviews with five nephrologists and one informant nurse. Our clinically relevant pDDIs were checked with the Dutch "G-Standard". A multidisciplinary team decided the design characteristics of pDDI-alerts in a CDSS considering the international recommendations and the inputs from our clinical context. Finally, the performance of the CDSS in detecting DDIs was evaluated iteratively by a multidisciplinary research team. Results: Medication data of 595 patients with 788 visits were collected and analyzed. Fifty-two types of interactions were most common, comprising 90% of all pDDIs. Among them 33 interactions (comprising 77% of all pDDIs) were rated as clinically relevant and were included in the CDSS's knowledge-base. Of these pDDIs, 73% were recognized as either pseudoduplication of drugs or not a pDDI when checked with the Dutch G-standard. Thirty-three alerts were developed and physicians were allowed to customize the appearance of pDDI-alerts based on a proposed algorithm. Conclusion: Clinical practice contexts should be studied to understand the complexities of clinical work and to learn the type, severity and frequency of pDDIs. In order to make the alerts more effective, clinicians' points of view concerning the clinical relevance of pDDIs are critical. Moreover, flexibility should be built into a pDDI-CDSS to allow clinicians to customize the appearance of pDDI-alerts based on their clinical context.