A number of variables influence the remote patient monitoring (RPM) market, including payers and providers’ desire to reduce expenses and improve operational efficiency, the growing usability of home-based RPM devices, the growing need for better healthcare in rural regions, the growing elderly population, and the rapid rise of chronic diseases outside of the US and Europe.
In response to the COVID-19 pandemic, international healthcare systems revamped existing products and services or developed new virtual care delivery models. It was during the pandemic that 37 firms introduced new RPM products, according to GlobalData’s Marketed Products database. Changes in regulations, licensing, and reimbursement restrictions accelerated RPM adoption in the US.
According to GlobalData’s latest Thematic Research: Remote Patient Monitoring Devices report, the RPM market will grow from $548.9 million in 2020 to $760 million in 2030 at a compound annual growth rate (CAGR) of 3.3%.
How do you see remote patient monitoring five and ten years from now?
This trend was accelerated by the pandemic by demonstrating the need and value of remote patient monitoring, which is still in its early stages of adoption and integration in the healthcare system. With the advent of clinical-grade devices, patients can now measure and monitor various vital signs, including EKGs, heart rates, heart rate variability, blood pressure, and oxygen levels.
One of the organizations that sets the standard of care in the U.S. healthcare system is the Centers for Medicare and Medicaid Services, which launched CPT codes more than four years ago for remote physiological monitoring. Over the past few years, CMS has added new and updated CPT codes to increase coverage and specificity.
In 2022, CMS launched CPT codes for remote therapeutic monitoring (RTM). These codes cover RTM for respiratory and musculoskeletal (MSK) conditions, such as remote physical therapy and COPD inhaler tracking. This is a big step forward in helping patients get the most out of their treatments on a daily basis since most healthcare happens in their daily lives.
Wearables from mainstream companies have been cleared by the FDA, blurring the line between healthcare companies and consumer tech companies. In addition to Apple, Amazon, Google and Samsung, these giants all have launched mainstream wearables that can shift consumer habits nationwide.
Apple Watch has outsold all Swiss watchmakers multiple times in a row, and its EKG monitor has been cleared by the FDA for use by adults over 22 without a history of arrhythmias.
This trend is great news because many people may already be tracking something about their health, whether that’s blood pressure monitoring, continuous glucose monitoring or even a simple accelerometer for step counting. It increases the probability that if their healthcare professional recommends the device and has access to its data, the patient will continue to use it.
In ten years, remote patient monitoring will be mainstream, and likely reimbursed by all the major payers. We already see that RPM can catch hospital readmissions days before they occur. There is a revolution in vital-sign measurement devices in the healthcare industry, with many companies innovating new ways to collect vital signs.
In addition to taking vital signs using a smartwatch, a smartphone or laptop camera, breathalyzer devices for standard vital signs such as blood pressure and oxygen saturation, sensors in clothing, and epidermal and subcutaneous sensors, new innovations are emerging.
How did artificial intelligence first come into play with RPM? What was the connection?
Some of these FDA-cleared devices continuously measure vital signs, so they collect thousands of data points on each patient each day.
In order to manage, monitor, analyze and interpret the thousands of daily data points per patient, these clinical-grade wearables and sensors use artificial intelligence software to manage, monitor, analyze and interpret them. In most cases, the AI software flags or alerts the healthcare team and the patient when vital signs go outside predetermined ranges, tailored to each patient’s needs.
This trend is still in its infancy, but there are examples of new innovations that have only been made possible by continuous, personalized data collection. With January AI, glucose response to individual foods is predicted in real time based on data from a continuous glucose monitor over the past three days, along with vital-signs data. At the point of decision-making, the patient is educated.
As opposed to the standard reactive approach to diabetes currently used, this helps manage diabetes in a more personalized and predictive way. However, January AI isn’t just for diabetics. In addition to athletes, they work with people who have pre-diabetes, metabolic syndrome, and just want to be healthy.
Real-time education does not assume there is one diet that is best for everyone. People react differently to food than others, or even to themselves.
There are many factors that determine a person’s glucose response to food, including activity level, sleep, amount of fiber, stress, weight and age. AI-based software combined with RPM allows for 24/7 personalized care.
In today’s world, how does AI work with RPM to improve care and outcomes for patients?
When used for serious conditions, RPM can mean the difference between life and death. It can make the difference between life and death for patients with cancer by catching serious problems such as neutropenia, sepsis, and cytokine storm early on.
Diabetes patients who develop hotspots on their feet could develop skin ulcers, which could eventually lead to amputations if they do not heal. Using continuous data, the software can alert clinicians and patients when a problem is occurring so that the skin is treated before it breaks.
RPM promises to keep patients safe in their homes and catch problems early, before they become serious or emergency situations.
How does the theme of AI and RPM fit in with your new book with Michael Ferro, “How AI Can Democratize Healthcare?”?
There are some major issues with traditional healthcare datasets that exist today to train software. Most healthcare data is locked in silos, whether in the EHR, faxes, payers, or clinical notes.
In fact, when I get lab results from my physician through the hospital’s patient portal, they are uploaded as a scanned fax and saved as a PDF that isn’t machine readable, and sometimes isn’t even human readable. The interoperability process is moving forward, but there is still much more to be accomplished.
A person’s healthcare data is usually collected at one point in their life, such as at their annual physical or when they are hospitalized. This means most clinical-grade vital-sign data is collected on people who are already in a hospital, so it does not include their baseline data, taken on a daily basis.
With RPM, clinical-grade data can be collected when people are at all stages of their health and at all ages by shifting the collection to their daily lives. RPM can dwarf EHR data collected in hospitals and health systems when it is collected continuously in machine-readable databases.
Training data like that can help healthcare professionals gain a much deeper understanding of normal vital signs across ages, genders, and genetics.
Many people do not live close to a doctor or clinic. RPM helps democratize healthcare in a way that has never been possible before. Many factors make it difficult for people to get to a clinic during their open hours, including not being able to take time off work, school, transportation, distance, childcare, and other obstacles to getting to a clinic.
It is common for specialist doctors to be booked out one to three months in advance, which gives medical problems time to progress and worsen. Thus, if and when a patient is ever seen and treated by a healthcare professional, the likelihood of a successful outcome is lowered.
By utilizing RPM, someone can determine when they need to see a healthcare professional and can make a virtual care visit more effective than physically visiting a clinic.
