Remote Patient Monitoring and AI in 2025: the top four use cases
Remote patient monitoring (RPM), long a promising but underutilised tool in healthcare, is undergoing a quiet revolution. The catalyst? Artificial intelligence. By marrying AI's analytical prowess with RPM's continuous health surveillance capabilities, healthcare providers are witnessing a sea change in patient care delivery. The technology not only reduces the burden of routine clinic visits but also serves as a vigilant digital sentinel, sifting through torrents of patient data to spot troubling patterns before they become critical. As 2025 unfolds, several applications of this marriage between AI and RPM are proving particularly consequential.
At Axenya, this fusion of AI and remote patient monitoring lies at the core of our mission. We've built our entire platform around the principle that proactive, AI-driven healthcare monitoring can transform patient outcomes while reducing costs for healthcare providers. Our commitment to this approach stems from years of seeing how traditional reactive healthcare often intervenes too late, leading to poorer outcomes and higher costs. By leveraging AI's analytical capabilities with continuous remote monitoring, we're not just observing health data—we're actively participating in preventing health crises before they occur.
The results speak for themselves: 95% of our clients in 2024 received a price increase lower than the market average—up from 85% in 2023 and 80% in 2022. These numbers demonstrate the growing effectiveness of our AI-driven approach to healthcare cost management. Our claim ratio now stands at 70%, compared to the market average of 85–90%.
How do we achieve these results? Here are the four key use cases at Axenya:
AI Watches for the First Signs of Trouble
The days of waiting for patients to report worrying symptoms are numbered. Artificial intelligence, coupled with remote monitoring devices, is revolutionising how healthcare providers detect deteriorating health conditions. Like a vigilant guardian, AI systems now continuously process streams of vital signs from wearable sensors, spotting subtle changes that even experienced clinicians might miss.
The technology works by establishing a personalised baseline for each patient—taking into account everything from heart rhythms to sleep patterns. When anomalies appear, the system raises the alarm. A gradually decreasing heart rate variability, for instance, might signal an impending cardiac event long before traditional symptoms manifest.
Early results are promising. Healthcare providers report fewer emergency hospital admissions among monitored patients, as potential crises are caught and addressed before they escalate. The cost savings are substantial: one large hospital network estimates a 30% reduction in acute care admissions for patients under AI surveillance.
Yet challenges remain. False alarms must be minimized to prevent alert fatigue among medical staff. Data security concerns loom large, as these systems process vast amounts of sensitive health information. Perhaps most crucially, the technology requires patient buy-in—the most sophisticated monitoring system is useless if patients don't wear their devices.
Despite these hurdles, the marriage of AI and remote monitoring represents a significant leap forward in preventive care. As algorithms improve and devices become less obtrusive, this digital sentinel may well become as fundamental to modern medicine as the stethoscope was to its Victorian predecessors.
AI Personalises Treatment: A Digital Doctor's Touch
Gone are the days when medical treatment was a one-size-fits-all affair. Artificial intelligence, working in tandem with remote patient monitoring (RPM), is ushering in an era of bespoke healthcare that would have seemed like science fiction a decade ago. The technology acts as a tireless medical assistant, sifting through mountains of patient data—from genetic markers to daily vital signs—to craft treatment plans as unique as the individuals they serve.
The system's genius lies in its ability to learn. By analyzing vast troves of medical records, it can predict which treatments are likely to succeed based on outcomes from similar cases. When a patient's blood pressure spikes or their glucose levels wobble, the AI adjusts their care plan in real time, rather than waiting for their next check-up. For healthcare providers, this means more efficient resource allocation; for patients, it translates into more effective treatment.
Yet this digital revolution in healthcare is not without its pitfalls. Questions of data privacy loom large, and there are justified concerns about algorithmic bias potentially perpetuating healthcare inequalities. Moreover, as sophisticated as these systems become, they cannot—and should not—replace the human touch in medicine. The challenge lies in striking the right balance: using AI to enhance, rather than replace, the doctor-patient relationship.
Predictive Analytics: AI's Crystal Ball for Patient Care
In the high-stakes world of healthcare, knowing which patients might take a turn for the worse has long been more art than science. But artificial intelligence is changing that calculus. By marrying AI with remote patient monitoring (RPM), healthcare providers are now wielding powerful predictive tools that can spot trouble before it arrives.
The system works rather like a sophisticated weather forecast for human health. RPM devices collect a constant stream of vital signs, medication adherence data and lifestyle metrics. AI algorithms then sift through this digital deluge, hunting for subtle patterns that might escape even the most eagle-eyed clinician. When they spot something amiss—perhaps a slight change in heart rhythm or an unusual sleep pattern—they raise the alarm.
Early results are promising. Hospitals using these systems report catching potential crises before they develop, leading to fewer emergency admissions and lower costs. The technology also helps healthcare providers deploy their resources more efficiently, focusing attention on patients most likely to need it. For the patients themselves, it means more personalized care and timely interventions.
Yet challenges remain. The quality of predictions depends heavily on the quality of data fed into the system. Privacy concerns loom large, as does the need for transparency in how AI makes its decisions. Perhaps most thorny is the question of equity: ensuring that algorithmic biases don't perpetuate existing healthcare disparities.
Despite these hurdles, the marriage of AI and predictive analytics in healthcare appears here to stay. As one doctor puts it, "It's like having a tireless colleague who never sleeps, constantly watching for the first sign of trouble." In an aging world with stretched medical resources, such digital vigilance could prove invaluable.
Smart Pills, Smarter Care
Doctors have long wrung their hands over patients who fail to take their medicine. Some 50% of prescriptions go unfilled or unused, costing health systems billions and patients their wellbeing. But artificial intelligence, paired with remote monitoring, may finally crack this stubborn problem.
The solution combines several innovative approaches. Currently, pharmacy data tracks medication purchases. Soon, smart pills and patches will monitor when medicines are actually taken. AI algorithms analyze this information alongside vital signs and behavioral patterns to identify patients likely to miss doses before it happens. When red flags appear—such as a patient's schedule becoming erratic—the system activates automatically. It sends timely reminders to phones, delivers targeted educational content, and alerts care teams about patients who may need additional support.
Early results are promising. One large health network reports a 40% jump in medication adherence among monitored patients. Hospital admissions have fallen as chronic conditions stay better controlled. The savings are substantial: each dollar spent on AI monitoring saves an estimated three in avoided complications. For patients, it means better health without the hassle of constant clinic visits.
Yet challenges remain. Privacy concerns linger over the intimate health data these systems collect. Some worry about "surveillance medicine" going too far. And the technology is only as good as patients' willingness to use it. But as the population ages and healthcare costs spiral, such digital vigilance may prove vital medicine indeed.
At Axenya, we're not just observing the future of healthcare—we're actively building it. Our integration of AI and remote patient monitoring represents more than technological advancement; it embodies a fundamental shift in how healthcare is delivered and experienced. By combining cutting-edge technology with human expertise, we're creating a healthcare ecosystem that's more proactive, personalized, and cost-effective than ever before.
The success of our approach lies in its holistic nature. We understand that true healthcare innovation isn't just about implementing new technologies—it's about reimagining the entire patient journey. From early detection to treatment adherence, our AI-driven platform ensures that patients receive the right care at the right time, while healthcare providers can work more efficiently and effectively.
As we look to the future, our commitment remains unwavering: to leverage technology in ways that make healthcare more accessible, efficient, and human-centered. The results we've achieved so far—from improved patient outcomes to significant cost savings—are just the beginning. We're building a future where technology doesn't replace the human element in healthcare but enhances it, creating a more connected, responsive, and effective healthcare system for everyone.