Comparison of OneChoice AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru

Clinical Snapshot

Key Findings

• P Value: p = 0.001 for specialisation as predictor

• Effect Size: 96.1% overall concordance (any suggested treatment), 74.6% for top recommendation

• Primary Outcome: Concordance between AI-CDSS recommendations and physician decisions

• Nnt Or Sensitivity: Cohen's Kappa 0.70 (substantial agreement), ID specialists κ = 0.78 vs non-specialists κ = 0.61

• Confidence Interval: Not reported in abstract

Clinical Application

Requires molecular diagnostic capabilities and AI-CDSS infrastructure, may be challenging in resource-limited settings Potential utility for Australian antimicrobial stewardship programs, though local validation needed given different resistance patterns and TGA-approved antimicrobials compared to Peru Physicians managing bacteremia in hospital settings, particularly relevant for antimicrobial stewardship programs

Abstract

Bacteremia is a major contributor to global morbidity and mortality, particularly in low- and middle-income countries where diagnostic delays and empirical antimicrobial misuse exacerbate resistance. This study assessed the accuracy of OneChoice®, an artificial intelligence (AI)-based Clinical Decision Support System (CDSS), in guiding antimicrobial therapy for bloodstream infections (BSIs) in Lima, Peru. A cross-sectional, observational design was used, comparing therapeutic recommendations generated by OneChoice®-based on molecular (FilmArray®) and phenotypic (MALDI-TOF MS, VITEK2) data-with the clinical decisions of 94 physicians (35 infectious disease [ID] specialists and 59 non-specialists) across 366 survey-based evaluations of bacteremia cases. Concordance between CDSS and physician decisions was analyzed using Cohen's Kappa and logistic regression. The overall concordance rate was 96.1% when considering any suggested treatment, and 74.6% for the top recommendation, with a substantial agreement (κ = 0.70). ID specialists showed significantly higher concordance (κ = 0.78) than non-ID physicians (κ = 0.61), and specialization was the strongest predictor of agreement (OR = 2.26, p = 0.001). Escherichia coli cases had the highest concordance, while Pseudomonas aeruginosa showed the lowest. The CDSS reduced inappropriate antibiotic use, particularly unnecessary carbapenem prescriptions. These findings support the utility of AI-CDSS tools in enhancing antimicrobial stewardship and standardizing care, especially in resource-limited healthcare settings.

References

1. 1.Gómez de la Torre, J. C., Frenkel, A., Chavez-Lencinas, C., Rendon, A., Fabian, M., Caceres-DelAguila, J. A., & Hueda-Zavaleta, M. (2026). Comparison of OneChoice AI-based clinical decision support recommendations with infectious disease specialists and non-specialists for bacteremia treatment in Lima, Peru. PLoS One. https://doi.org/10.1371/journal.pone.0331266