Literature DB >> 31128828

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

Habibollah Pirnejad1, Parasto Amiri2, Zahra Niazkhani3, Afshin Shiva4, Khadijeh Makhdoomi5, Saeed Abkhiz5, Heleen van der Sijs6, Roland Bal7.   

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.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Adverse drug events; Alert fatigue; Clinical decision support systems; Kidney transplantation; Medication alerting system; Patient safety; Potential drug-drug interaction

Mesh:

Year:  2019        PMID: 31128828     DOI: 10.1016/j.ijmedinf.2019.04.006

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  8 in total

1.  High-priority drug-drug interaction clinical decision support overrides in a newly implemented commercial computerized provider order-entry system: Override appropriateness and adverse drug events.

Authors:  Heba Edrees; Mary G Amato; Adrian Wong; Diane L Seger; David W Bates
Journal:  J Am Med Inform Assoc       Date:  2020-06-01       Impact factor: 4.497

Review 2.  Clinical Decision Support and Implications for the Clinician Burnout Crisis.

Authors:  Ivana Jankovic; Jonathan H Chen
Journal:  Yearb Med Inform       Date:  2020-08-21

3.  Designing and evaluating contextualized drug-drug interaction algorithms.

Authors:  Eric Chou; Richard D Boyce; Baran Balkan; Vignesh Subbian; Andrew Romero; Philip D Hansten; John R Horn; Sheila Gephart; Daniel C Malone
Journal:  JAMIA Open       Date:  2021-03-19

4.  Predicting the risk of drug-drug interactions in psychiatric hospitals: a retrospective longitudinal pharmacovigilance study.

Authors:  Jan Wolff; Gudrun Hefner; Claus Normann; Klaus Kaier; Harald Binder; Katharina Domschke; Christoph Hiemke; Michael Marschollek; Ansgar Klimke
Journal:  BMJ Open       Date:  2021-04-09       Impact factor: 2.692

5.  Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment.

Authors:  Syed Imran Ali; Su Woong Jung; Hafiz Syed Muhammad Bilal; Sang-Ho Lee; Jamil Hussain; Muhammad Afzal; Maqbool Hussain; Taqdir Ali; Taechoong Chung; Sungyoung Lee
Journal:  Int J Environ Res Public Health       Date:  2021-12-26       Impact factor: 3.390

6.  dfgcompare: a library to support process variant analysis through Markov models.

Authors:  Amin Jalali; Paul Johannesson; Erik Perjons; Ylva Askfors; Abdolazim Rezaei Kalladj; Tero Shemeikka; Anikó Vég
Journal:  BMC Med Inform Decis Mak       Date:  2021-12-20       Impact factor: 2.796

7.  Contextualized Drug-Drug Interaction Management Improves Clinical Utility Compared With Basic Drug-Drug Interaction Management in Hospitalized Patients.

Authors:  Arthur T M Wasylewicz; Britt W M van de Burgt; Thomas Manten; Marieke Kerskes; Wilma N Compagner; Erik H M Korsten; Toine C G Egberts; Rene J E Grouls
Journal:  Clin Pharmacol Ther       Date:  2022-06-27       Impact factor: 6.903

8.  Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system.

Authors:  Zahra Niazkhani; Mahsa Fereidoni; Parviz Rashidi Khazaee; Afshin Shiva; Khadijeh Makhdoomi; Andrew Georgiou; Habibollah Pirnejad
Journal:  BMC Med Inform Decis Mak       Date:  2020-08-20       Impact factor: 2.796

  8 in total

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