RTM is addressing the opioid crisis with a wearable device to save lives and reduce healthcare costs
Global access to health care is far from adequate, with health disparities widening; the rise of economic inequities and shortages in the health workforce are contributors. Indeed, in the United States, the opioid crisis has reached new heights:
- Drug overdose is the most common cause of death in the USA in people under 50 years old
- The Centers for Disease Control (CDC)estimate the economic burden of prescription opioid misuse exceeds $78.5 Billion/year
- The total economic cost of the “Opioid Crisis” was estimated by the Council of Economic Advisors (CEA, November 2017) to be ~ $500 Billion
Sensor Prevents an Opioid Overdose
RTM is developing the miniature wearable acoustic sensor that uses a microphone to monitor the adequacy of an ambulatory patient’s breathing- capable of predicting hypoventilation and preventing death from an opioid overdose. We are developing machine learning (AI) diagnostic algorithms that accurately detect & predict opioid-induced respiratory depression in real-time from the tracheal sound. A continuous risk-index score will alert providers when the algorithm predicts impending moderate hypoventilation (PaCO2 >50 mm Hg) and will alarm when the algorithm predicts impending severe hypoventilation (PaCO2 > 60 mm Hg).
Non-invasive Wearable Vital Sign Monitoring System with an acoustic sensor located over the trachea. Real-time data displayed and analyzed by Cell Phone, Table or PC.
RTM’s long-term goal is to commercialize a series of wearable vital sign sensors that continuously monitor and analyze an ambulatory patient’s blood pressure, electrocardiogram, hemoglobin oxygen saturation, temperature, respiratory rate, tidal volume, sounds of the upper airway/heart/lungs, body position, and activity level. The wearable sensors can be used to monitor ambulatory out-patients with asthma, COPD, pneumonia, ischemic heart disease, congestive heart failure, arrhythmias, and other chronic diseases. Machine learning (AI) algorithms will analyze the real-time vital sign sensor trend data to increase the accuracy and timeliness of diagnosis and the safety/effectiveness of medical therapy.