Pulse Check: How digitally denoising ECGs clears the way for greater efficiency

We’ve been measuring electrical activity in the heart muscle for over 150 years: The first human ECG was recorded all the way back in 1870. During that century and a half, ECG technology and the field of cardiology has steadily progressed, allowing clinicians to diagnose heart conditions with more and more sensitive hardware and precise readings.

But while ECG technology has been steadily marching forward, other forces have complicated providers’ ability to deliver great cardiac care. This blog is the first in a series that will explore these complicating forces, and will focus on the increasing need for efficiency in cardiac care. Between sector-wide staffing shortages and increasingly thin operating margins for healthcare organizations, we can no longer afford some of the baseline inefficiencies to which we’ve become accustomed.

When people’s lives hang in the balance, efficiency can’t come with a compromise in care. Here is where providers have an exciting opportunity to increase efficiency and provide better cardiac care at the same time: Digital denoising, or the process of cleaning ECG signals so that they are easier to interpret.

What’s the link between staffing shortages and ECG interpretation?

By and large, ECGs are still interpreted by humans—which we actually think is a good thing. When we develop ECG algorithms, we do so for the purpose of empowering healthcare professionals with better tools, not replacing them with sentient technology (which, of course, is only as insightful as the interpretive data we train it on in the first place). The human professionals who are interpreting ECGs are able to bring their own clinical expertise and judgment to analysis and diagnosis.

That’s all to say that one route to greater efficiency would be relying on Artificial Intelligence and Machine Learning solutions more heavily, taking the human staffing factor out of the equation. But while A.I. and Machine Learning are valuable tools to complement human expertise, they are still only as valuable as the data sets they have been trained on. Those data sets have flaws and biases as well—which is why those tools are still even stronger when combined with real-time human clinical judgment.

We can see the value of this human experience in the variation in interpretation accuracy that appears when you compare clinicians of varying expertise levels. Experienced, well-trained clinical staff bring a wealth of good judgment and talent to ECG interpretation.

Unfortunately, the wave of early retirements (driven by the pandemic, patient overload, and general burnout), are poised to hit the cardiac specialty particularly hard. More than 40% of physicians will be 65 or older within the decade ahead; the average age of general practice providers in the United States is 59, and the average age of cardiology providers is 55.

With staffing shortages, we’re not just losing capacity—we’re experiencing a brain drain that means we need to find ways to make ECG interpretation easier and faster for the clinicians that remain.

Doing more with less: When thinner margins force clarity

It would be better if all providers had more resources. But between a workforce that’s stretched thin and back-offices that are trying to balance budgets, we have to think differently about using the resources that hospitals and health systems have.

When it comes to efficiency in cardiac care, the stars align for providers and patients: Increasing speed-to-diagnosis gives clinicians time back in their day and makes sure that patients are benefiting from more timely, proactive care.

That’s another benefit of our HeartKey algorithms: Healthcare professionals agreed that they sped up diagnosis in 71% of cases. That means clinicians are escalating or deescalating patients more efficiently, making better use of hospital resources while also guiding patients towards better health, faster.

Of course, speed to diagnosis only matters if it’s the right diagnosis. For more than 50% of acute myocardial infarctions, for example, misdiagnosis—often stemming from incorrect ECG interpretation—means that patients die after leaving the Emergency Department without acute treatment.

By removing excess noise from the signal, HeartKey algorithms also improve ECG accuracy, with over 98% ambulatory QRS detection sensitivity. HeartKey enables high accuracy on dry and wet electrodes alike, and increases clinical confidence while raising the bar for patient care.

Meanwhile, the rise of wearable digital health devices that measure heart health remotely could equip clinicians with more data than ever for proactive, efficient care and reduced inpatient testing—if only they were reliable. But when 1,600 healthcare professionals were asked about the challenges of these remote ECGs, more than half of them reported noisy readings, nearly half named how time-consuming the readings were, and 41.5% reported the need for additional testing to confirm the results.

HeartKey Rhythm powers medical-grade accuracy in digital health devices so that when a clinician takes the effort to review an ECG, they can trust it the first time around—and avoid the cost and time of additional testing.

Stressful directive or golden opportunity?

“Increase efficiency” sounds like a business-oriented directive that is divorced from providing patient care. But in the case of ECG interpretation, the pressure that providers and healthcare businesses face provides a real opportunity. By eliminating signal noise and empowering clinicians with cleaner, most trustworthy data, we can equip this generation of patients and providers with better health insights and a smoother overall experience.

Staffing shortages and budget shortfalls are absolutely a real threat—but we already have the technology and solutions to rethink our workflows, increase speed and accuracy, and rise to the occasion.