From Protocol to Practice: Graded Sepsis Bundle Compliance and Actionable Insights from Real-World ICU Data

Abstract Sepsis is a leading cause of in-hospital mortality, yet systematically evaluating temporal adherence to the Surviving Sepsis Campaign (SSC) bundle across large patient populations remains difficult due to semantic variability in electronic health records and the loss of clinical nuance inherent in binary pass/fail compliance judgments. We present an expert-guided neuro-symbolic pipeline that pairs LLM-based semantic normalization with a Sugeno fuzzy inference system encoding eight SSC bundle rules, producing graded per-episode compliance scores whose clinical decision boundaries are set through domain expert consultation. Applied to 2,438 sepsis episodes from MIMIC-IV v3.1, the dual-classifier normalization layer achieves substantial inter-system agreement with high embedding-based confirmation, resolving hundreds of clinically relevant drug strings that purely symbolic systems miss. The graded framework reveals that Hour-1 bundle failures, particularly antibiotic timing, are the dominant driver of low overall compliance, and that higher bundle adherence is associated with notably shorter ICU stays, with antibiotic delays beyond six hours increasing median stays by 61%. These results demonstrate that neuro-symbolic graded assessment can surface actionable compliance patterns that binary evaluation frameworks cannot capture. Github https://tinyurl.com/yy2zvpd9