From Predictive Feasibility to Embedded Surgical Intelligence: Implementing Anastomotic Leak Risk Stratification in Routine Clinical Workflow

Abstract

Risk stratification tools in surgery often demonstrate statistical promise long before they can alter care delivery. Anastomotic leak prediction is a clear example: the complication is infrequent enough to challenge model development, severe enough to justify surveillance, and temporally dynamic enough that a single static score rarely matches clinical reality. The distance between a proof-of-concept classifier and a clinically integrated decision support system is therefore not a matter of packaging alone. It is a problem of representation, calibration, workflow timing, accountability, and organizational adaptation. This paper develops a technical and translational framework for moving anastomotic leak prediction from retrospective model performance toward operational use in perioperative care. The argument is that clinical implementation requires reframing the task from isolated binary classification to sequential risk estimation under uncertainty, embedded within a bounded alerting environment and linked to explicit downstream actions. The paper examines phenotype fidelity, temporal data design, multimodal model structure, threshold governance, calibration maintenance, interface design, human oversight, subgroup reliability, and prospective evaluation strategy. It proposes a workflow-centered architecture in which risk estimation is repeatedly updated across preoperative, intraoperative, and early postoperative phases, while preserving interpretability and auditability at each stage. It also outlines monitoring and validation mechanisms required for safe deployment under changing patient mix and documentation patterns. The central claim is modest: meaningful implementation is achievable only when predictive performance, clinical utility, and sociotechnical fit are treated as a single design problem rather than as separate stages delegated to different teams.