Monitoring of patients with existing or impending critical illness routinely involves the recording of multiple physiological waveforms. However, tracking response to interventions, gauging clinical trajectory and making informed clinical decisions still mostly rely on vital signs and laboratory tests summarized and charted over hours to days. Utilizing the currently untapped information contained in waveform data has the potential to reduce the diagnostic and prognostic uncertainty inherent in critical care, even when patients are managed by trained intensivists. This uncertainty results in delayed diagnosis, unnecessary or inappropriate therapy and increased complications, mortality and cost of care.Heart rate variability (HRV) and respiratory rate variability (RRV) time series derived from the continuous physiological waveforms help characterize the degree and complexity of the patterns of the inter-beat and inter-breath interval time series. Decreased variability is associated with age and illness and correlates with illness severity, indicating reduced adaptability and/or increased stress. Combining waveform-based variability analysis with predictive modelling, we can enhance timely clinical decision-making at the bedside by providing probabilistic prediction of upcoming clinical events. We demonstrate this approach using data from our recent large prospective study on optimal weaning from mechanical ventilation in the ICU.Finally, we show how these clinical decision support tools can integrate within the current processes of care to optimize individual patient care and manage resources more efficiently.