Therapeutic Monitoring Systems (TMS) solutions address rising ICU costs and patient safety challenges by predicting clinical outcomes. The first three problems addressed are the optimal timing of mechanical ventilation extubation; the prediction and assessment of risk of subsequent deterioration in ER sepsis patients; and the identification of candidates for organ donation after circulatory death including the estimated time of circulatory death. The respective clinical care decision support software products are the Extubation Advisor, the Sepsis Advisor and the Donation Advisor. TMS products are designed to yield high returns on investment that reduce ICU costs and improve patient care.

The vital signs of healthy patients manifest both a high degree of complexity of variation. This data indicates adaptability and the ability to augment workload. However, the vital signs of deteriorating patients manifest a reduced degree of complexity of variation indicating stress, illness and decreased physiological reserve. Machine learning converts variability measures to predictive models that provide measures of risk. TMS harnesses big data analytics to complement clinician judgement thus improving outcomes in complex situations arising in ICUs and emergency departments.

TMS big data analytics draws upon research collaboration with the University of Ottawa's Department of Physics lab of Dr. André Longtin. Understanding the association between fractals and entropy production has broad scientific implications for monitoring and impacting the system level properties of far-from thermodynamic equilibrium dynamical systems.

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