Personalized Decision-Making in Spine Surgery: From Predictive Algorithms to Clinical Implementation

Spine surgery has advanced in technical precision, yet recovery remains heterogeneous and is only partly explained by anatomical findings. This narrative review examines how personalized decisions can be advanced by integrating multidimensional patient data with predictive models. A structured PubMed/MEDLINE was conducted, focusing on prediction, patient-reported outcomes, quantitative imaging, psychosocial factors, and clinical implementation in spine surgery and related fields. Models estimate complications, discharge needs, pain relief, functional recovery, and recovery trajectories. Their clinical value depends less on algorithmic complexity than on reliable inputs, decision-relevant outcomes, external validation, calibration, interpretability, and workflow. We propose a practical implementation framework: define the decision and target population; select relevant inputs and outcomes; assess performance and transportability; translate estimates into shared decisions; and monitor clinical use. Standard core outcomes should preserve flexibility for emerging continuous measures. Predictive tools should augment surgeon judgment and, with local validation and governance, may improve patient selection, expectation management, perioperative planning, and longitudinal care.