In this interview, we speak with David McManus, MD, about his session at HRX 2022.  

Transcripts:

Can you give us a preview of some recent advances in continuous physiological monitoring using wearable technologies, as well as future technologies in development?

I will start with what people conventionally think of when they consider digital health technology, which are point of care or home-based devices. There has been an absolute explosion in the number of sensor-based technologies that have come to market, including the clearance of Fitbit devices for the detection of atrial fibrillation (AF) by the United States Food and Drug Administration.

Both the Apple Watch and Fitbit are now approved for AF detection, but there have been other devices that have been released that enable electrocardiogram monitoring at home, cardiovascular symptom tracking, medication adherence tracking, integration with electronic health records, and a variety of other functionalities. One of the other types of technology that people may not think of when they think of digital health technology are pieces of software. We are increasingly seeing deployments of software enhanced by artificial intelligence (AI) for clinical decision support and other uses within the cardiovascular space. So those are just a few different technologies that we are seeing developed and deployed within cardiology and medicine in general.

How have wearables helped empower health care providers and improve patient outcomes?

That is a great question. First, wearables have empowered health care providers by enabling them to track and interact better and in a more holistic way with their patients. Of course, that has imposed challenges on health care providers, because in many cases, the use of the technology and the desire that patients have to monitor themselves outstrips the capacity for health care providers and their systems to accommodate the data coming from these devices. I think that one of the major ways that technology helps is to connect, and whether it be use of sensor data to track falls, heart rate, symptoms, or recurrence of arrhythmia, we are seeing a variety of different indications and uses, including things such as tracking drug toxicity and other indications, that were not necessarily within the initial use case scenario that the technology was designed for. So clinicians are adapting by using technologies to help keep their patients out of the hospital, keep them well, and keep them from coming back. It has been a remarkable sea change in how technologies are being used, particularly in the context of the COVID-19 pandemic, when an increasing proportion of patients are interested in receiving care in their homes and not coming into a brick and mortar clinic.

In terms of patient outcomes, there are remarkably little data showing that use of health technology actually improves health outcomes. Where the data are strongest is on improving patient satisfaction and connectedness. We will see, as several large trials are examining these questions, including if the use of a wearable helps to prevent stroke, promote adherence to anticoagulation for AF, or reduce toxicity of medication. These are uses of technology within the cardiovascular space that have great promise.

Outside of cardiology, use of point of care home-based technologies have been shown to be extremely helpful and are increasingly being used to augment programs that we know help patients, such as virtual cardiac and pulmonary rehab. It is pairing good old-fashioned medicine for which we know there is great evidence with the use of technology. However, to date, there have been very few clinical trials that have definitively shown whether a patient randomized to receive that technology actually does better than another person. Observational studies tend to suggest that, for example, tracking your steps and being more active is good for you. But there is a selection bias in who owns these technologies, who uses smartphones, and what types of activities we engage in.

These are the types of limitations in observational studies of digital health technology that need to be challenged with either comparative effectiveness or conventional randomized clinical trials.

What challenges still exist with wearable physiological signal monitoring sensor devices?

The biggest challenge is there are just so much data that comes from these devices. It is not only a functional challenge, meaning the petabytes of information that can be generated by one person over a long period of monitoring. Every few seconds and in some cases, milliseconds, a measurement can be made. Therefore, with oodles of data from one person, it can be difficult to see the signal from the noise. When you get a lot of data, who synthesizes that? Who puts that together in the context of a report that is generating insight? We can generate information, but can we generate insight about when the physician really needs to be notified about an AF patient having an episode, when a patient who maybe has a known heart condition is not getting enough activity, or when a home blood pressure is too variable? These are the types of scenarios that are challenging for clinicians who have patients sending them lots of data points, or in some cases, automatically feeding data into the electronic health record. We do not know what to do with that information, nevermind the actual functional challenge of where to store it, how it is linked, where it appears, and the security and privacy implications of that. Also, what is the liability for a physician whose patient is sharing data, but they may not be looking at individual heart rates or events because that is not part of the standard practice, so there is nobody looking at it?

So I think we have to work through a number of these challenges. Again, the solution is not human beings analyzing individual sensor data points, but much in the same way with a Holter or 30-day event monitor, we are often using an algorithm to prioritize the data for the clinician to then look at. I would anticipate use of software, probably AI/machine learning software, to look at data, synthesize it, generate insight, and present the information that the clinician needs to see at the right time for the right patient. That whole process is probably 5 years away at best, but I think we are getting there. There are some examples of clinical decision support and other uses of software as a medical device that have been helpful at generating insight, just not as common as I would like at this stage of the game.

Discuss the potential impact of emerging wearable technologies on clinical practice.

I think that we are seeing a shift from a sort of episodic fee for service-oriented health care to population and panel, in the case of an individual clinician, management. For example, I think clinical practice is going to shift toward me being notified about a patient of mine that I am responsible for and scheduling a visit on the basis of sensor-based measurements or device data, instead of being scheduled routinely at 6 months or 1 year based on some arbitrary cadence that is part of the lore of health care, per se. I think that is one change. I see prioritization of care being informed by technology. The other element is I am going to prescribe it more for particular use cases. I might use an Apple Watch after a cardioversion to monitor my AF patient in lieu of prescribing a conventional medical monitor for a particular set of patients. It might be for the patient who already owns an Apple Watch, for example.

Finally, I think that the role of advanced practitioners in health care is expanding. They are an ideal group to be managing and interacting with people, as almost a “care traffic controller” (instead of air traffic controller). There may be an advanced practitioner who is keeping an eye on, let’s say, 5000 people. They are prioritizing and interacting with people. For example, they may say to a patient, “It looks like your heart rate is slow. We see that you have been taking a new beta blocker, so that is not a problem.” That type of practice role did not exist before, and I see technology as generating this new career for somebody who is quite savvy with devices, who knows how to interpret the data from them, and can help prioritize and synthesize information that can only be obtained from the individual patient.