Decision Assist During Mechanical Ventilation

Abstract

Mechanical ventilation management remains one of the most complex and critical interventions in intensive care medicine. As ventilation strategies continue to evolve from gas exchange optimization to comprehensive lung and diaphragm protection, the cognitive burden on clinicians has increased substantially. Decision assist systems represent a promising technological advancement that can help bridge the gap between evidence-based protocols and individualized patient care. Unlike fully automated closed-loop systems, decision assist tools maintain clinician oversight while providing real-time, data-driven recommendations to optimize ventilator management. This narrative review examines the current landscape of decision assist systems in mechanical ventilation, exploring their potential benefits in improving health equity, reducing practice variation, and enhancing adherence to protective ventilation strategies. We discuss the technical challenges, implementation barriers, and future directions for these systems, with particular emphasis on balancing the competing priorities of lung protection and diaphragm preservation. We illustrate these principles using examples from the development and implementation of a decision assist system called REDvent (real-time effort driven ventilator management) which was recently tested in a phase 2 clinical trial. While fully automated ventilation remains challenging due to the complexity of clinical decision-making and the need for contextual judgment, decision assist systems offer a pragmatic approach to improving ventilator management in the contemporary intensive care unit.