Cardiovascular disease remains the #1 cause of death globally, responsible for ~20 million deaths per year.
In the U.S. alone, it drives hundreds of billions of dollars in healthcare costs annually.
So demand for cardiac RPM (Remote Patient Monitoring) is not the problem.
Yet many heart monitoring companies still struggle to reach profitability.
Why?
Reimbursement complexity
Heavy upfront R&D and regulatory spend
High clinical operations costs
But of these, high clinical operations costs are solvable by reducing false positives and fixing the underlying algorithms. Today, high false-positive rates force technicians to log thousands of manual hours reviewing repetitive, low-value ECG events. Every unnecessary AFib flag means more clicks, delayed reports, wasted clinical hours, and inflated costs.
This is finally changing. State-of-the-art AI, powered by improved neural architectures and post-training on device-specific data, can suppress noise while preserving true positives.
So whatโs actually holding you back from hitting a positive bottom line?