Automated Model Predicts Surgical Complications Across 14 Health Systems

Postoperative complications remain a substantial driver of hospital morbidity and mortality, often occurring despite advancements in surgical technique and perioperative care [1]. While systemic interventions such as standardized safety checklists have successfully reduced global mortality rates, individual risk stratification remains a challenge for clinicians managing patients with multiple comorbidities [2]. The complexity of the perioperative period, including the inherent risks associated with anesthesia handovers and the management of acute physiological shifts, necessitates more precise tools for identifying patients at highest risk for adverse outcomes [3]. Accurate preoperative assessment is critical for the implementation of targeted interventions, such as prehabilitation or optimized fluid management, which can significantly improve functional recovery and reduce hospital length of stay [1, 4]. To address this need, a recent multicenter analysis evaluated an automated framework designed to enhance predictive capabilities by leveraging longitudinal electronic health record data across a diverse network of healthcare institutions.