A noninvasive of detecting patient-ventilator asynchrony that is easily adaptable to existing ventilator monitoring systems and provides timely and actionable information on the degree of patient asynchrony both during invasive and non-invasive ventilation. Capture, analysis or display of, frequency spectra and the use of a measure of spectral organization, such as H1/DC, allows for both manual and automatic adjustment of a ventilators to prevent or correct patient-ventilator asynchrony via interventions. Embodiments use artificial intelligence or machine learning to predict interventions predicted to result in positive outcomes, based on analysis of a large number of epochs, captured by an electronic monitor of a mechanical ventilator, where the monitor continuously monitors, captures and transfers, epochs of data for aggregated machine learning analysis, of such epochs associated with positive outcomes. Scheduling processes that seek to overbook or double book to overcome negative effects of no shows, on clinician productivity in a medical setting.
Detection and Display of Respiratory Rate Variability, Mechanical Ventilation Machine Learning, and Double Booking of Clinic Slots, System, Method, and Computer Program Product