Evidence map›Paper›PMID 39347841›Full record

ArticleJournal of medical systems2024

The Impact of Customized Screening Intervals on the Burden of Drug-Drug Interaction Alerts: An Interrupted Time Series Analysis.

Greet Van De Sijpe, Karolien Walgraeve, Eva Van Laer, Charlotte Quintens, Christophe Machiels, Veerle Foulon, Minne Casteels, Lorenz Van der Linden, Isabel Spriet

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Article in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Greet Van De SijpePharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium. greet.vandesijpe@uzleuven.be.ORCID http://orcid.org/0000-0003-1475-6178
Karolien WalgraevePharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
Eva Van LaerPharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
Charlotte QuintensPharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.
Christophe MachielsDepartment of Information Technology, nexuzhealth, Belgium.
Veerle FoulonClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
Minne CasteelsClinical Pharmacology and Pharmacotherapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
Lorenz Van der LindenPharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.ORCID http://orcid.org/0000-0001-6342-0676
Isabel SprietPharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fixed and broad screening intervals for drug-drug interaction (DDI) alerts lead to false positive alerts, thereby contributing to alert fatigue among healthcare professionals. Hence, we aimed to investigate the impact of customized screening intervals on the daily incidence of DDI alerts. An interrupted time series analysis was performed at the University Hospitals Leuven to evaluate the impact of a pragmatic intervention on the daily incidence of DDI alerts per 100 prescriptions. The study period encompassed 100 randomly selected days between April 2021 and December 2022. Preceding the intervention, a fixed and broad screening interval of 7 days before and after prescribing an interacting drug was applied. The intervention involved implementing customized screening intervals for a subset of highly prevalent or clinically relevant DDIs into the hospital information system. Additionally, the sensitivity of the tailored approach was evaluated. During the study period, a mean of 5731 (± 2909) new prescriptions per day was generated. The daily incidence of DDI alerts significantly decreased from 9.8% (95% confidence interval (CI) 8.4;11.1) before the intervention, to 6.3% (95% CI 5.4;7.2) afterwards, p < 0.0001. This corresponded to avoiding 201 (0.035*5731) false positive DDI alerts per day. Sensitivity was not compromised by our intervention. Defining and implementing customized screening intervals was feasible and effective in reducing the DDI alert burden without compromising sensitivity.

Indexed as

Drug InteractionsInterrupted Time Series AnalysisMedical Order Entry SystemsAlert Fatigue, Health PersonnelBelgiumHospital Information SystemsHumansMedication ErrorsTime FactorsAdvanced clinical decision support systemsAlert fatigueAlert specificityClinical decision support systemsDrug interactions

Identifiers

PMID39347841

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.