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08/03/26

Real-time clinical decision support system for early identification of infection and sepsis in the intensive care unit: a retrospective development and prospective deployment study.

Sun, Fang-Ju; Liu, Yen-Yu; Kuo, Li-Kuo; Hu, Ting-Yu; Cheng, Kuang-Hua; Chen, Hung-Ting; Chang, Po-Jen; Wu, Min-Ching; Yeh, Hung-I; Wu, Kun-Pin. BMJ Qual Safety.

Collection Topics
AI and Diagnosis Big Three Conditions Culture, Work System, and Implementation Equity, Access, and Timeliness Health IT Tests, Screening, and Laboratory Processes
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Summary

Early recognition of infection and sepsis is challenging because symptoms can be nonspecific and test results delayed. Taiwanese researchers developed and externally validated an AI-supported clinician decision-support tool using ICU patient data to identify possible infection and sepsis risk hours earlier than usual recognition. In thousands of real-world admissions, it prioritized sensitivity and provided bedside risk information. While the results are encouraging, clinical impact and workflow effects of the system will require further evaluation.
Read the paper
Sun F-J, Liu Y-Y, Kuo L-K, et al. Real-time clinical decision support system for early identification of infection and sepsis in the intensive care unit: a retrospective development and prospective deployment study. BMJ Qual Saf. Epub 2026 Aug 3.