ArticleAntimicrobial stewardship & healthcare epidemiology : ASHE2026
EMERGE: Early antiMicrobial stEwaRdship for GEneral medicine patients-targeting inpatient intravenous therapy greater than 24 hours.
Article in Antimicrobial stewardship & healthcare epidemiology : ASHE, 2026. 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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Authors and funding
10 authors.
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Abstract
Objective: Early review of intravenous (IV) antimicrobial therapy is central to antimicrobial stewardship (AMS), however scalable models for general medical patients are limited. We evaluated a pharmacist-led digital intervention to optimize IV antimicrobial prescribing. Methods: A prospective, quasi-experimental before-and-after study was conducted between May 2022 to February 2023 across six general medicine units at a tertiary hospital. AMS recommendations were delivered electronically via Microsoft Teams®. Adult inpatients receiving IV antimicrobials for >24 hours were included, excluding those with COVID-19, under Infectious Diseases consultation or receiving palliative care. The primary outcome was median IV antimicrobial duration. Secondary outcomes included AMS recommendation type, recommendation acceptance, length of stay (LOS), 30-day infection-related readmission, IV therapy recommencement, and inpatient mortality. Antibacterial consumption was analyzed from July 2021 to through December 2024 to evaluate sustained impact. Results: Among 723 antimicrobial orders (474 treatment episodes in 458 patients), median IV duration was comparable between phases (intensive: 2.75 days; baseline: 3.00 days). LOS was shorter during the intensive phase compared to baseline (5.5 vs 7.6 days; Conclusions: The digital pharmacist-led AMS intervention did not reduce IV duration, likely reflecting strong baseline prescribing, but was associated with shorter LOS and a reduction in total antibacterial use. This program offered a scalable, sustainable alternative to resource-intensive face-to-face models.
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