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Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival.

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Title: Implementing an AI-enhanced clinical decision support system for Stenotrophomonas maltophilia: a survey-based randomized controlled trial of antibiotic precision and impact on survival.
Authors: Lin, Tai-Han1 (AUTHOR), Chung, Hsing-Yi1,2 (AUTHOR), Jian, Ming-Jr1 (AUTHOR), Chang, Chih-Kai1 (AUTHOR), Perng, Cherng-Lih1 (AUTHOR), Chang, Feng-Yee3 (AUTHOR), Chen, Yuan-Hao4 (AUTHOR), Shang, Hung-Sheng1 (AUTHOR) iamkeith001@gmail.com
Source: Implementation Science. 10/24/2025, Vol. 20 Issue 1, p1-14. 14p.
Subject Terms: *STENOTROPHOMONAS maltophilia, *CLINICAL decision support systems, *MEDICAL personnel, *RANDOMIZED controlled trials, *MASS spectrometry, *DRUG resistance in bacteria, *INAPPROPRIATE prescribing (Medicine), *HOSPITAL mortality
Abstract: Background: The World Health Organization has identified Stenotrophomonas maltophilia (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper therapy and the spread of resistance. This study assessed how an artificial intelligence-clinical decision support system (AI-CDSS) utilizing mass spectrometry data to predict resistance enhances prescribing decisions and boosts survival. Methods: This randomized controlled trial (ISRCTN16278872) involved 400 healthcare professionals, with 1,600 SM infections randomized in a 1:1 ratio to either standard practice (control, n = 800) or an AI-CDSS predicting resistance 1 day earlier (intervention, n = 800). Outcomes were assessed by healthcare professionals using structured surveys on days 3, 5, 7, and 14 after treatment initiation. Patient mortality was analyzed over a 14-day follow-up period. Results: The AI-CDSS group demonstrated significantly higher confidence (p < 0.001) in antibiotic prescription, decision-making efficiency, and appropriate antibiotic selection across all time points. Mortality was lower in the AI-CDSS group (92/800, 11.5%) than in the control group (121/800, 15.1%) (p = 0.03). Effective antibiotic choices and reliance on the AI-CDSS during the critical early stages of treatment contributed to improved patient outcomes. Conclusions: Implementation of the AI-CDSS in a clinical trial setting enhances prescribing confidence, improves decision-making and antibiotic selection, reduces mortality, and demonstrates clinical potential. Trial registration: ISRCTN, ISRCTN16278872. Registered 28 June 2024, https://www.isrctn.com/ISRCTN16278872. [ABSTRACT FROM AUTHOR]
Database: Academic Search Index
Description
Abstract:Background: The World Health Organization has identified Stenotrophomonas maltophilia (SM) as a high-risk antibiotic-resistant pathogen. Notably, determining the effectiveness of current antibiotics against SM is challenging, leading to improper therapy and the spread of resistance. This study assessed how an artificial intelligence-clinical decision support system (AI-CDSS) utilizing mass spectrometry data to predict resistance enhances prescribing decisions and boosts survival. Methods: This randomized controlled trial (ISRCTN16278872) involved 400 healthcare professionals, with 1,600 SM infections randomized in a 1:1 ratio to either standard practice (control, n = 800) or an AI-CDSS predicting resistance 1 day earlier (intervention, n = 800). Outcomes were assessed by healthcare professionals using structured surveys on days 3, 5, 7, and 14 after treatment initiation. Patient mortality was analyzed over a 14-day follow-up period. Results: The AI-CDSS group demonstrated significantly higher confidence (p < 0.001) in antibiotic prescription, decision-making efficiency, and appropriate antibiotic selection across all time points. Mortality was lower in the AI-CDSS group (92/800, 11.5%) than in the control group (121/800, 15.1%) (p = 0.03). Effective antibiotic choices and reliance on the AI-CDSS during the critical early stages of treatment contributed to improved patient outcomes. Conclusions: Implementation of the AI-CDSS in a clinical trial setting enhances prescribing confidence, improves decision-making and antibiotic selection, reduces mortality, and demonstrates clinical potential. Trial registration: ISRCTN, ISRCTN16278872. Registered 28 June 2024, https://www.isrctn.com/ISRCTN16278872. [ABSTRACT FROM AUTHOR]
ISSN:17485908
DOI:10.1186/s13012-025-01453-4