By now, you've heard Apple's Watch Series 4 has an EKG sensor that will soon allow its users to detect if their heart rhythm is normal or is in Atrial Fibrillation (AF) -- a common type of heart arrhythmia that affects up to 6.1 million people in the US.
While Watch Series 4 and its successors will undoubtedly bring this life-saving technology to millions of people over the next several years, Apple was not the first to bring it to market.
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That distinction goes to AliveCor, which is known for its Kardia series of mobile EKG products for smartphones and Apple Watch -- and for enlisting Vic Gundotra, formerly the social media platform officer of Google, as its CEO.
The Mountain View, Calif.-based company, which formed in 2011, currently produces the credit card-sized KardiaMobile Bluetooth connected accessory that works with any iOS or Android mobile device, and KardiaBand, which provides single-electrode EKG for Apple Watch.
Both products have been on the market for over a year and have algorithmic-based detection of AF, although the company has had other mobile EKG products for sale to clinicians since 2012.
Each device costs $99 and works with AliveCor's Kardia Premium cloud service (it keeps a record of patient EKGs, which can be shared with a medical professional) and can be interpreted by the company's affiliated physicians at additional cost. That service costs $99 a year.
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But with the release of Apple Watch Series 4, which provides a fully integrated solution starting at $399 with no monthly service requirement, that continued market leadership in mobile EKG (and the company's future) has been up for speculation.
I spoke with AliveCor's commercial officer, Ira Bahr, to find out what new developments the company has in store. They are quite substantial, and will almost certainly preserve its future for some time to come.
While the company has sold a great number of KardiaMobile devices and a fewer number of KardiaBands -- which are bought primarily by an older demographic than what Apple is targeting -- it continues to innovate in order to bring increased levels of electrophysiological detail to mobile EKGs so that they are closer in accuracy to an actual clinical 12-lead diagnostic you might find in a hospital or a doctor's office.
In the spring of next year, the company expects to be able to receive FDA clearance on its next-generation KardiaMobile EKG product, which will have three electrodes and is capable of recording six leads of data rather than one.
With six leads, not only is accuracy and fidelity increased significantly, but it permits a much more detailed data stream to be collected and analyzed.
Certain heart conditions, like AF, will continue to be easily detected with the 1-lead method, but in addition, other asymptomatic diseases, like warning signs of a heart attack, will also be detected with the newer 6-lead devices -- something that the single-lead EKG is not capable of detecting.
It's not just about the more advanced mobile EKG hardware itself, though. AliveCor combines the data collection capabilities of its sensors with the ability to do machine learning processing on mobile devices and in its cloud -- so algorithms can be applied to diagnose other types of conditions that aren't necessarily exposed by looking at the raw stream of electrophysiological data.
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One such condition is "Long QT Syndrome" (LQTS), which causes sudden death in 4,000 children and young adults every year in the US alone.
In its work with the Mayo Clinic, AliveCor can now use a single-lead EKG (like the one used on its current-generation products and Apple Watch Series 4) to detect this condition using artificial intelligence.
Another condition that can be detected with AliveCor's machine learning capabilities is hyperkalemia, which is elevated levels of potassium in the blood and is typically asymptomatic and difficult to diagnose. It is associated with devastating effects such as congestive heart failure, diabetes, and chronic kidney disease.
Although hyperkalemia is a blood condition, AliveCor's neural networks on its new KardiaK platform can analyze electrophysiological data from the heart using EKG -- with no blood whatsoever -- and detect it.
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This advanced detection technology, which until recently was only in the realm of science fiction, for other medical conditions is extremely futuristic and will almost certainly save many lives.
However, it's the more mundane medical technology that doesn't make such great sound bites that is likely to make a much larger difference.
Apple Watch Series 4 will have the ability to store EKG diagnostic data on the device and will be accessible through a HealthKit application running on iPhone. Although we have not yet seen the final version of this app, it has been demonstrated that data can only be shared with a physician through an export function.
AliveCor is taking EKG diagnostics a step further with its Kardia Pro service, which allows medical practitioners to monitor all their mobile EKG-equipped patients using a single cloud-based dashboard, and it gives them access to all patient rhythm strips in addition to connected blood pressure monitoring devices, such as those made by Omron, which is one of the company's investors.
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AliveCor remains open to integrating its cloud service with other types of medical sensor products, should the interest from potential third-party partners exist.
It has also enhanced Kardia Pro with the billing infrastructure needed so that medical practices can do this as an ongoing service. In 2018, the Center for Medicare and Medicaid Services (CMS) began reimbursing claim codes for remote patient monitoring, and Kardia Pro now has this capability built-in.
Ultimately, with remote patient monitoring, this means fewer visits to the hospital and the doctor's office. For elderly patients, who may not be ambulatory or mobile, and those with immune deficiencies, this is a godsend.
With Watch Series 4, Apple may very well become the EKG provider for the masses -- but it will be AliveCor that leads in clinician remote monitoring for patients and advanced machine learning diagnostics.
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