Systems and methods for clinician decision support during mechanical ventilation of a patient comprise evaluating a shape and/or characteristics of a waveform to detect an anomaly. While mechanical ventilators are equipped with a number of alarms and alerts when monitored patient data breaches various alarm thresholds, some anomalies in patient data may go unnoticed by clinicians. These anomalies, however, may provide relevant information regarding patient condition. Accordingly, in response to detecting an anomaly, the ventilator captures at least a portion of the waveform. The waveform capture, which may be annotated with various labels and educational information, may be reviewed by a clinician to obtain additional information regarding the anomaly. In this way, clinicians may be trained to recognize and address anomalies associated with waveform data and thereby be armed with information to optimize patient-ventilator interaction.
