Human performance evaluation of a pediatric artificial intelligence sepsis model.

Objective: To assess the influence of an implemented artificial intelligence model predicting pediatric sepsis (defined by IPSO-Improving Pediatric Sepsis Outcomes collaborative) in the emergency department (ED) on human performance measures.

Materials and methods: Two ED sites within a large pediatric health system in the Southeastern United States between January 1, 2021 and April 1, 2024. We interviewed ED providers and nurses within 72 hours of caring for a patient identified as potentially having sepsis by the predictive model. Thematic analysis of qualitative data was combined with electronic health record queries to assess measures of human performance, including situation awareness, explainability, human-computer agreement, workload, trust, automation bias, and relationship between staff and patients.

Results: We interviewed 40 clinicians. Participants found that the sepsis alert improved situation awareness, leading to changes in patient care management, resource allocation, and/or monitoring. Participants reported an average trust in the model-based alert of 3.8/5. Only 28% (555/1977) of sepsis huddles were done without alert firing, suggesting some automation bias. Treatment with antibiotics for IPSO sepsis cases was similar pre- and post-intervention without a huddle (9.3% vs 10.5%), though treatment doubled with huddle intervention (22.7%). NASA Task Load Index increased from 43 to 57 post-intervention. There was no report of adverse relationships with patients post-intervention.

Discussion: Human performance appeared to be generally positive with improved situation awareness and satisfaction with the alert-driven huddle. However, there was some evidence of automation bias and a slight increase in workload with the intervention.

Conclusion: This study demonstrates the feasibility of evaluating multiple dimensions of human performance using a mixed methods approach for an AI model implemented in clinical practice. Future studies should aim to reduce the measurement burden of human performance metrics associated with AI implementation in acute care settings and assess the correlation between human performance measures and clinical outcomes.