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Vol. 30. Issue S1.
XXIV Brazilian Congress of Infectious Diseases 2025
(March 2026)
Vol. 30. Issue S1.
XXIV Brazilian Congress of Infectious Diseases 2025
(March 2026)
100
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ARTIFICIAL INTELLIGENCE IN RECOMMENDING ORAL SWITCH OF ANTIMICROBIALS: PRELIMINARY CLINICAL AND ECONOMIC IMPACT ANALYSIS IN A GENERAL HOSPITAL

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Flavia Fernandes Falcia,
Corresponding author
flaviafalci@hotmail.com

Corresponding author:
, Hugo Manuel Paz Moralesb, Wesley Valloto Minarib, Raquel Carneiro Machado Higaa, Lucilia Freireb, Cristian Rochab
a Hospital Unimed Guarulhos, Guarulhos, SP, Brazil
b Munai Health, São Paulo, SP, Brazil
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Vol. 30. Issue S1

XXIV Brazilian Congress of Infectious Diseases 2025

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Introduction/Objective

Optimizing antimicrobial use remains a key challenge in hospitals, particularly amid the need for efficiency and high bed turnover. This study aimed to evaluate the clinical and economic impact of implementing an artificial intelligence (AI) algorithm for automated recommendation of antimicrobial route switch from parenteral to oral administration.

Methods

A retrospective analysis was conducted over a 90-day period following implementation of Munai Health’s AI-driven stewardship solution. The platform integrates laboratory data, vital signs, prescriptions, and clinical notes into a unified digital interface, generating automated alerts for early antimicrobial route switch. Clinical pharmacy, infectious disease, and infection control teams were trained prior to deployment and began evaluating alerts from day one. Patients were divided into two groups: accepted alerts (with prescription modification) and pending alerts (without modification). Mean length of stay and economic impact were compared between groups.

Results

During the study period, 109 valid alerts were generated, of which 72 (66.1%) were accepted. Mean hospital stay was significantly shorter in the accepted-alert group (7.32 days) compared to the pending-alert group (9.41 days), a mean difference of 2.09 days (p = 0.0379). Based on an average daily hospital cost of R$1,088.86, direct savings were estimated at R$2,271.32 per patient and R$163,535.00 for the analyzed period. The projected annual savings totaled R$663,225.28. In addition to cost reduction, benefits included decreased venous exposure, reduced healthcare-associated infection risk, and increased bed availability.

Conclusion

Implementation of an AI algorithm to support clinical decision-making in antimicrobial route switch demonstrated positive clinical and economic impact. The tool proved effective in promoting rational antimicrobial use, enhancing hospital efficiency, and improving patient safety, representing a promising strategy for healthcare institutions seeking to combine technological innovation with sustainability.

Keywords:
Oral Switch
Artificial Intelligence
Efficiency
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