Stephanie Allman

Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSSs) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. A cross-sectional survey was conducted with 65 unique specialist physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and 90 corresponding AI-CDSS recommendations. Participants assessed the perceived helpfulness of AI decision-making, the impact of AI recommendations on their own clinical judgment, and the concordance between AI recommendations and their own clinical judgment, as well as the validity of changing therapy based on CDSS recommendations. The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Findings show that 97.8% [(95% CI: 92.2–99.7%)] of physicians reported that AI facilitated decision-making and substantial concordance (87.8% [95% CI: 79.2–93.7%; Cohen’s κ = 0.76]) between AI recommendations and physicians’ therapeutic recommendations. Stratification by pathogen revealed the highest concordance for Escherichia coli bacteremia (96.6%, 28/29 cases). Implementation analysis revealed a meaningful clinical impact, with 68.9% [(95% CI: 58.3–78.2%)] of cases resulting in AI-guided treatment modifications. These findings indicate that AI-powered CDSSs effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship, providing clinicians with evidence-based therapeutic recommendations that can be integrated into routine practice to optimize antibiotic selection, particularly in settings with limited access to infectious disease specialists. For optimal clinical integration, we recommend that clinicians utilize AI-CDSS recommendations as an adjunct to clinical judgment rather than a replacement, particularly in complex cases involving immunocompromised hosts or polymicrobial infections. Future research should prioritize prospective clinical trials that evaluate direct patient outcomes to establish evidence of broader clinical effectiveness and applicability across diverse healthcare settings.

Background: Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSS) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. Methods: A cross-sectional survey was conducted with 65 physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and corresponding 90 AI-CDSS recommendations. Participants assessed perceived helpfulness, the impact of AI decision-making, concordance between AI recommendations and their own clinical judgment, and whether changing therapy based on CDSS recommendations is valid. Results: The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Results demonstrated 97.8% of physicians reported that AI facilitated decision-making and substantial concordance (87.8%) between AI recommendations and physicians’ therapeutic recommendations. Implementation analysis revealed a meaningful clinical impact, with 68.9% of cases resulting in AI-guided treatment modifications. Conclusions: These findings indicate that AI-powered CDSS effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship. Future research should prioritize prospective clinical trials evaluating direct patient outcomes to establish evidence of broader clinical effectiveness and application across diverse healthcare settings.

Background: Antimicrobial stewardship programs (ASPs) are essential in combating antimicrobial resistance (AMR); however, limited resources hinder their implementation. Arkstone, a biotechnology company, developed a machine learning (ML)-driven clinical decision support system (CDSS) to guide antimicrobial prescribing. While AI (artificial intelligence) applications are increasingly used, each model must be carefully validated. Methods: Three components of the ML system were assessed: (1) A prospective observational study tested its ability to distinguish trained from novel data using various validation techniques and BioFire molecular panel inputs. (2) An anonymous retrospective analysis of internal infectious disease lab results evaluated the recognition of novel versus trained complex datasets. (3) A prospective observational validation study reviewed clinical recommendations against standard guidelines by independent clinicians. Results: The system achieved 100% accuracy (F1 = 1) in identifying 111 unique novel data points across 1110 tests over nine training sessions. It correctly identified all 519 fully trained and 644 novel complex datasets. Among 644 clinician-trained reports, there were no major discrepancies in recommendations, with only 100 showing minor differences. Conclusions: This novel ML system demonstrated high accuracy in distinguishing trained from novel data and produced recommendations consistent with clinical guidelines. These results support its value in strengthening CDSS and ASP efforts.

Background: Antimicrobial stewardship programs (ASPs) are essential in combating antimicrobial resistance (AMR), however, limited resources hinder their implementation. Arkstone, a biotechnology company, developed a machine learning (ML)-driven clinical decision support system (CDSS) to guide antimicrobial prescribing. Though widely used, the model had not been previously evaluated. Methods: Three components of the ML system were assessed: (1) A prospective observational study tested its ability to distinguish trained from novel data using various validation techniques and BioFire molecular panel inputs; (2) An anonymous retrospective analysis of internal infectious disease lab results evaluated recognition of novel versus trained complex datasets; and (3) A prospective observational validation study reviewed clinical recommendations against standard guidelines by independent clinicians. Results: The system achieved 100% accuracy (F1=1) in identifying 111 unique novel data points across 1,110 tests over nine training sessions. It correctly identified all 519 fully trained and 644 novel complex datasets. Among 644 clinician-trained reports, there were no major discrepancies in recommendations, with only 100 showing minor differences. Conclusion: This novel ML system demonstrated high accuracy in distinguishing trained from novel data and produced recommendations consistent with clinical guidelines. These results support its value in strengthening CDSS and ASP efforts.