The healthcare industry is beginning to examine Internet of Things (IoT) connected wearable medical devices to help prevent the onset of heart problems, equip physicians and patients with the data they need to better manage heart problems.

Heart disease is the number one cause of death in the world and the leading cause of death in the United States, killing over 375,000 Americans a year. Heart disease and stroke combined cost the U.S. nearly $1 billion a day in medical costs and lost productivity. What stuns in the face of such sobering statistics is the fact that heart disease is preventable in many cases. Early detection of warning signs, a commitment to long-lasting lifestyle changes, and the right diagnosis and medication can help prevent heart disease from claiming so many lives each year.

What is desperately needed is nothing short of a radical shift in our contemporary approach to cardiac care. Patients at risk for heart disease display a lackadaisical attitude towards their own health, rarely making positive lifestyle changes or adhering to medications that can thwart the onset of heart problems. Research suggests that 24% of patients who suffer a heart attack do not fill their medications within seven days of discharge, and 34% of heart attack patients with multiple prescriptions stop taking at least one of them within one month of discharge. On the flip side, physicians with the best intentions are faced with insurmountable barriers to providing the best care to their cardiac patient—staff and resource shortages, limited time, and high patient congestion. In the face of this growing crisis, the healthcare industry is beginning to examine Internet of Things (IoT) connected wearable medical devices to help prevent the onset of heart problems, equip physicians and patients with the data they need to better manage heart problems post-diagnosis, and ultimately drive a reduction in heart-related fatalities.

Faster diagnosis and early prevention

Medical wearables can aid in earlier diagnosis of heart conditions, particularly those that are difficult to identify. In order to accurately diagnose an arrhythmia, for example, doctors need to continuously monitor a patient’s ECG for a minimum of a full day, to capture any and all symptoms accurately and comprehensively. Arrhythmias are notoriously difficult to detect and can be intermittent, so detection and diagnosis are improved through long-term continuous monitoring, enabled by IoT. Mobile cardiac telemetry (MCT) devices that provide real-time monitoring of a patient’s heart rhythm over a longer period of time are vital for AFib detection. MCT devices are the only heart monitoring devices that provide complete arrhythmia detection and offer the highest diagnostic yield at 61%, compared to Event monitors at 23% and Holter monitors at 24%.

Post Diagnosis: Better care management and health outcomes

IoT devices are also enabling physicians and patients to turn data into action. Health data that can be measured can also be improved. With real-time medical wearables, physicians can compare a baseline measurement of patient metrics against subsequent patient data collected over a longer period of time, watching as the metrics fluctuate in response to treatment, lifestyle, stress, and other day-to-day factors. The feedback loop is immeasurably shortened, and treatment plans can be implemented earlier for maximum effectiveness. Sensor-fed information can also send out alerts to patients and physicians in real-time so they get event-triggered alerts for an elevated heart-rate or for signs precipitating a heart attack or stroke—speeding up response time in emergency situations and helping avert fatalities.

Medical wearables can also support patients in their self-care and medication management because when patients are able to access their real-time health data with ease and immediacy, they can be empowered to better participate in their own healthcare and in the management of their long-term condition. According to a new report from the Centers for Disease Control and Prevention, about 80% of deaths from coronary artery disease can be attributed to preventable factors like obesity, poor physical activity, heavy drinking, eating unhealthy foods, and not keeping blood pressure and cholesterol under control. These lifestyle changes could also prevent about 50% of stroke deaths. Medical wearables for cardiac patients can collect clinical-grade data on a patient’s ECG, respiration, physical activity, caloric burn, and other vital signs in real-time—equipping patients with the actionable feedback required in preventing a heart condition or managing a condition once manifest.


Today, health disparities and access gaps between urban and rural communities continue to widen, because rural hospitals and health facilities are closing while those in more urban areas remain open. In fact, one-fifth of Americans live in areas that have physician and healthcare specialist shortages. In Texas, for instance, the closure of rural hospitals affects 20%  of the state’s entire population. This problem is compounded by the fact that there is about a 25%  higher death rate for ischemic heart disease within these already vulnerable populations. As patients with heart problems are discharged from hospitals and begin recovery at home, medical wearables can remotely deliver monitored rehabilitation programs.

Driving long distances and coping with lengthy wait times is burdensome for patients whose health is already frail. Physical distances between healthcare facilities and patients’ homes can be overcome with medical wearables as care services are extended to remote areas that traditional services are not able to reach.

Taking medical wearables a step further: Artificial intelligence

Medical wearables with artificial intelligence can exponentially build upon the speed and accuracy of heart disease diagnoses through remote patient monitoring. Researchers from Oxford University are using AI to improve diagnostic accuracy for heart disease. Cardiologists who assess echocardiograms, which are ultrasonic scans of the heart, miss signs of heart disease 20% of the time. Inadvertently, patients can be sent home and then go on to have a heart attack. The researchers are training a machine learning system to recognize signs of heart disease by studying scans from previous patients, along with data about whether or not the patients went on to have a heart attack in the future. Already, this new system has improved diagnostic accuracy by 90%. We have trained our AI on tens of thousands of patient ECG data to identify abnormalities with the same 90% accuracy, improving data screening for ECG technicians and speeding up response time.

The combined benefits of affordability, flexibility, and increased access to healthcare make medical wearables a feasible and scalable alternative to traditional cardiac care involving lengthy hospital stays for observation and intermittent clinic visits. The importance of both the aforementioned cannot be disregarded; rather, the approach to cardiac care must expand to encompass a preventative mindset and sage triage. Patients, both at risk for heart disease and those already managing a heart condition, must be monitored at home with real-time medical wearables. This will not only massively improve workflow optimization but also ensure that non-acute conditions are managed effectively from the comfort of home.


Waqaas Al-Siddiq is founder and CEO of Biotricity.