One major disadvantage of remote patient monitoring (RPM) is that it can widen health inequities rather than close them. Patients who lack reliable internet access, digital literacy, or the financial means to maintain connected devices are effectively locked out of these programs. But that’s far from the only drawback. RPM introduces a range of challenges for patients and clinicians alike, from cybersecurity risks to alert fatigue to simple device frustration. Understanding these disadvantages helps you weigh whether an RPM program is a good fit for your situation.
It Can Exclude the People Who Need It Most
RPM depends on a chain of technology: a monitoring device, a smartphone or tablet, a home internet connection, and the skills to use all three. When any link in that chain is missing, the patient can’t participate. This disproportionately affects older adults, rural communities, lower-income households, and racial or ethnic minorities. A 2025 analysis in NPJ Digital Medicine found that only about 36% of published RPM programs reported being accessible to adults 65 and older. Even more striking, fewer than 10% addressed varying levels of digital literacy at all.
Recruitment into RPM programs also tends to favor patients who already have what researchers call “higher cultural health capital,” meaning they’re comfortable navigating healthcare systems and technology. People with complex medical needs or limited English proficiency often get left behind. The result is a system that can reinforce existing gaps in care rather than bridge them. Broadband internet access, digital literacy, and device affordability all function as independent barriers to health, layered on top of the social and economic factors that already shape outcomes.
Cybersecurity Risks Are Higher at Home
Inside a hospital, patient data sits behind enterprise-grade firewalls managed by full-time security teams. At home, that same sensitive health data travels through a consumer internet connection, typically protected by an inexpensive router that may be unpatched, misconfigured, or running outdated firmware. This is a fundamental vulnerability of RPM systems.
Even when data is tunneled through a virtual private network (VPN), there’s a risk of health information being intercepted or the home network being used as a foothold by attackers. As more patient data gets digitized and transmitted outside clinical settings, the attack surface grows. A breach doesn’t just expose medical records. It can cause massive financial losses for healthcare organizations and erode the trust patients place in their providers. For patients already skeptical of digital health tools, this risk can be a dealbreaker.
Alert Fatigue Puts Clinicians and Patients at Risk
RPM devices generate a continuous stream of readings: blood pressure, blood glucose, heart rate, oxygen levels. Much of this data is normal or clinically insignificant, but it still triggers alerts that someone has to review. When clinicians are bombarded with frequent, non-actionable alerts, they develop what’s known as alarm fatigue, a gradual desensitization that makes them less responsive to all alerts, including the ones that matter.
The consequences are serious. Research shows that alarm fatigue leads to delayed responses, communication breakdowns, and coping strategies like reducing alert volumes or disabling alarms entirely. This is sometimes called the “crying wolf” effect: so many false alarms erode trust in the system, and a genuine emergency gets missed. Chronic exposure to this flood of notifications also takes a personal toll on healthcare workers, contributing to frustration, burnout, elevated blood pressure, sleep disturbances, and lasting stress. One study of a remote monitoring device clinic identified the large volume of alerts as one of three major operational challenges, alongside poor connectivity and staffing issues.
For patients, the downstream risk is real. If your clinician is sorting through hundreds of alerts a day, a critical change in your readings may not get the timely attention it deserves.
Many Patients Drop Out
Starting an RPM program is one thing. Sticking with it is another. A study of nearly 2,000 diabetes patients invited to participate in remote monitoring found that 13% declined outright and another 16% withdrew before completing the program. Systematic reviews of RPM for chronic conditions report attrition rates ranging from 9% to 21%, with app-based digital health programs seeing even higher dropout.
The most common reason patients gave for declining or quitting was simply not having enough time. Others cited problems with device adhesion, discomfort from wearing a device on the body, pain during use, insurance complications, and mistrust of the system due to inaccurate readings. Timing matters too. One patient explained, “I probably would have participated if you had waited until when I was at home. I was pretty overwhelmed with everything.” Patients suggested they’d be more likely to stick with a program if it included incentives, better upfront information, and a less time-consuming design overall.
Device Accuracy and Data Integration Problems
Consumer-grade RPM devices are not the same equipment your doctor uses in the clinic. While the FDA classifies most non-invasive remote monitoring devices as Class I or Class II (lower-risk categories), the accuracy of home readings can vary by manufacturer, and there’s no universal standard ensuring every device performs identically. Patients who see readings that don’t match how they feel may lose confidence in the system, which is one of the cited reasons for program dropout.
On the clinical side, the data these devices generate often doesn’t integrate smoothly with existing electronic medical records. Different manufacturers use different formats, making it difficult to normalize information across platforms. In many cases, readings from home devices simply aren’t pulled into the patient’s chart automatically, forcing staff to manually review and enter data. This adds to clinician workload and increases the chance that important trends get overlooked in a sea of disconnected numbers.
The Cost Isn’t Trivial
RPM is often pitched as a cost-saving alternative to in-person visits, but running a program requires real investment. A cost analysis from NYU Langone Health estimated the average cost of a remote blood pressure monitoring program at $330 per patient, with an annual program cost of roughly $33,000 to manage 100 patients. The biggest expense wasn’t the devices themselves. It was clinician time: nurse practitioners reviewing home data accounted for about $172 per patient, more than three times the $48 cost of the blood pressure device. Monthly nurse-patient communication added another $36 per patient.
For smaller practices or under-resourced clinics, these costs can be a barrier to adoption. Start-up expenses like staff training and printed educational materials are minimal (under $3 per patient in the NYU analysis), but the ongoing labor of monitoring, reviewing, and acting on data is where the budget adds up. If reimbursement from insurance doesn’t fully cover these costs, the financial math may not work, particularly for the safety-net clinics serving the communities that could benefit most from remote monitoring.
Connectivity Failures Undermine the Whole System
RPM only works when data flows reliably from the patient’s home to the clinical team. Poor connectivity, whether from weak Wi-Fi, cellular dead zones, or device syncing failures, was identified as one of the top challenges in managing remote monitoring clinics. When a blood pressure cuff or glucose monitor can’t transmit its reading, the data gap looks the same as a patient who skipped their measurement. Clinicians can’t tell the difference without follow-up, which adds yet another task to an already heavy workload.
For patients in rural areas or older buildings with spotty internet, connectivity problems aren’t occasional inconveniences. They’re persistent obstacles that can make the entire program unreliable. And when readings fail to sync repeatedly, patients lose motivation to keep using the device, feeding directly into the attrition problem.