Evaluation of Digital Decision-Support Tools in Operating Room Anesthesia Management

Abstract

Digital decision-support tools have moved from simple rule-based reminders embedded in anesthesia information management systems to predictive algorithms that forecast intraoperative hypotension and closed-loop systems that autonomously titrate anesthetic depth or vasopressor infusion. This review synthesizes the comparative evidence for these tools across three broad categories: rule-based point-of-care alerting, machine-learning predictive analytics exemplified by the Hypotension Prediction Index, and closed-loop automated drug delivery. Narrowly scoped alerts tied to a single well-defined action show the most consistent improvement in perioperative process measures, while predictive hypotension indices reliably reduce the physiologic surrogate they target but show weaker and more heterogeneous evidence for reducing hard postoperative complications. Closed-loop anesthetic-depth control has the deepest randomized evidence base of any category reviewed and generally outperforms manual titration on time-in-target metrics without an apparent safety signal against it, though multi-variable autonomous systems and the question of sustained clinician vigilance during automated control remain comparatively unstudied. We conclude that closing the gap between well-demonstrated process and surrogate benefits and more rigorously established hard clinical outcomes, rather than adding further predictive parameters, is the field's most pressing next step.