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Updated: Sep 14, 2025
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A Neonatal Imaging Model of Gram-Negative Bacterial Sepsis
Published on: August 12, 2020
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Human performance evaluation of a pediatric artificial intelligence sepsis model.
Swaminathan Kandaswamy1, Naveen Muthu1,2, Nikolay Braykov2
1Pediatrics, Emory University School of Medicine, Atlanta, GA, 30307, United States.
Summary
An artificial intelligence (AI) sepsis alert improved clinician situation awareness in the emergency department (ED), but also showed some automation bias and increased workload. This study highlights the feasibility of mixed-methods evaluation for AI in clinical practice.
Area of Science:
- Clinical Informatics
- Artificial Intelligence in Healthcare
- Patient Safety
Background:
- Sepsis remains a critical challenge in pediatric emergency care.
- Predictive artificial intelligence (AI) models offer potential for early sepsis detection.
- Evaluating the impact of AI on human performance is crucial for successful implementation.
Purpose of the Study:
- To assess the influence of an implemented AI model for predicting pediatric sepsis on human performance measures in the emergency department (ED).
- To evaluate clinician situation awareness, trust, workload, and automation bias related to the AI sepsis alert.
Main Methods:
- A mixed-methods approach combining qualitative interviews with quantitative electronic health record (EHR) data analysis.
- Interviews with 40 ED providers and nurses within 72 hours of a patient being flagged by the AI sepsis model.
- Assessment of human performance metrics including situation awareness, explainability, human-computer agreement, workload, trust, automation bias, and staff-patient relationships.
Main Results:
- The AI sepsis alert improved clinician situation awareness, influencing patient care management, resource allocation, and monitoring.
- Clinicians reported an average trust of 3.8/5 in the AI alert; however, 28% of sepsis huddles occurred without an alert, indicating potential automation bias.
- Antibiotic treatment rates for sepsis cases were similar pre- and post-intervention without a huddle, but doubled with the intervention; NASA Task Load Index increased from 43 to 57.
Conclusions:
- The AI sepsis prediction model generally showed positive impacts on human performance, enhancing situation awareness and satisfaction with alert-driven sepsis huddles.
- Evidence of automation bias and a slight increase in workload were observed.
- The study demonstrates the feasibility of a mixed-methods approach for evaluating AI in clinical practice and suggests future research should focus on reducing measurement burden and correlating human performance with clinical outcomes.
