Background: Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Under suspicion of infection, sepsis can be identified as an acute increase in Sequential Organ Failure Assessment (SOFA) score of 2 points or more. Professional critical care societies have called for early detection and treatment of sepsis; however, the fundamental tool to address the need remains unmet. Objectives: The present study aims at exploring the possibility of a solution to bridging clinical information system with AI medicine and supporting decision-based medical tasks through integrated data-intensive intelligence. Patients and methods: We extracted data from a well-recognized database and explored the feasibility of a real-time solution to sepsis identification for adult ICU patients under suspicion of infection. To analogize the requirement of a randomized controlled trial, we adopted propensity score matching to tackle the imbalance of baseline covariates between study groups frequently encountered in observational studies. Results: Our study indicates that the hourly assessment protocol outperforms the 24-h assessment counterpart in terms of the timing of sepsis identification by a median of 14.5 h earlier and the change of total SOFA score by a median of 1.0 point lower. Conclusions: We conclude that real-time SOFA score to signal emerging sepsis becomes feasible through the introduction of data-intensive intelligence and data processing technologies.