ArticleCritical care explorations2025
Quantifying Healthcare Provider Perceptions of a Novel Deep Learning Algorithm to Predict Sepsis: Electronic Survey.
Article in Critical care explorations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Improving sepsis best practice utility and clinical acceptance using an LLM-enhanced prediction system.npj health systems · 2026Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
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Authors and funding
10 authors.
Funding
Abstract
importanceSepsis is a major cause of morbidity and mortality, with early intervention shown to improve outcomes. Predictive modeling and artificial intelligence (AI) can aid in early sepsis recognition, but there remains a gap between algorithm development and clinical use. Despite the importance of user experience in adopting clinical predictive models, few studies have focused on provider acceptance and feedback.
objectivesTo evaluate healthcare worker perception and acceptance of a deep learning sepsis prediction model in the emergency department (ED). DESIGN, SETTING, AND
participantsCOnformal Multidimensional Prediction Of SEpsis Risk (COMPOSER), a deep learning algorithm, is used at two EDs of a large academic medical center to predict sepsis before clear clinical presentation. An internally developed survey following the Checklist for Reporting Results of Internet E-Surveys was distributed to team members who received a COMPOSER alert. ANALYSIS: Mann-Whitney U testing was performed on results stratified by provider experience.
resultsA total of 114 responses were received: 76 from doctors of medicine/doctors of osteopathic medicine, 34 from registered nurses, and four from nurse practicioners/physician assistants. Of these, 53% were from providers with fewer than 5 years of experience. Seventy-seven percent of respondents had a positive or neutral perception of the alert's usefulness. Providers with 0-5 years of experience were more likely to expect sepsis after the alert (p = 0.021) and found the alert more useful (p = 0.016) compared with those with 6+ years of experience. Additionally, physicians with 0-5 years of experience were more likely to say the alert changed their patient management (p = 0.048).
conclusionsLess experienced providers were more likely to perceive benefit from the alert, which was overall received favorably. Future AI implementations might consider tailored alert patterns and education to enhance reception and reduce fatigue.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.