Remote patient monitoring (RPM) has incorporated artificial intelligence (AI) for many years, even before technologies like ChatGPT captured widespread attention. Early implementations weren’t advanced large language models but rule-based systems designed to alert clinicians when patient data indicated potential concerns.
Today, as modern AI reshapes healthcare, RPM is positioned at the forefront of an exciting evolution—offering remarkable potential while demanding careful oversight and responsibility.
The Evolution of RPM: From Intensive Oversight to Scalable Monitoring
To appreciate AI’s role in RPM today, it’s helpful to look at its development. Early RPM programs targeted high-acuity patients with a significant short-term risk of adverse—and costly—events. Examples include recently discharged heart failure patients who received near real-time monitoring from dedicated nurses. These high-touch programs relied heavily on human oversight to ensure safety and positive outcomes.
The COVID-19 public health emergency transformed the landscape, expanding RPM to monitor medium- to high-risk patients on a much larger scale. Programs shifted from tracking a few critically ill individuals to managing hundreds of patients simultaneously.
Today, RPM has shifted its focus toward chronic disease management, targeting medium-risk patients with the goal of preventing long-term complications rather than responding to immediate crises. Monitoring a hypertensive patient’s five-year stroke risk differs greatly from tracking a transplant recipient’s six-month survival. This broader scope and scale create an ideal setting for AI to add value—not by replacing human judgment, but by improving efficiency and detecting patterns that might be overlooked by clinicians and care teams, especially during increasingly busy workdays.
Current AI Applications in RPM: Three Key Focus Areas
AI is already driving tangible improvements in remote patient monitoring across several domains. Here are three areas where AI is helping transform RPM into a more efficient, scalable, and proactive model of care.
Clinical Documentation and Workflow Efficiency
Some of the most advanced AI applications in RPM target “low-risk, high-impact” enhancements to provider workflows. Tools such as automated encounter transcription, structured data extraction from multiple sources, and intelligent documentation assistance are saving clinicians and care teams substantial time while generally improving data accuracy.
These solutions are particularly effective at trend analysis and visualization, using pattern recognition to flag subtle changes in vital signs and biometrics that busy staff might otherwise overlook. The value of AI here isn’t that humans couldn’t detect these trends—it’s that AI consistently prioritizes and surfaces the most critical information, enabling care teams to monitor more patients effectively with the same resources.
AI is also improving compliance in RPM documentation, helping ensure that coding requirements are fully met while reducing manual oversight. These systems can automatically verify that clinicians are spending the right amount of time with each patient at the appropriate intervals, ensuring documentation and billing standards are satisfied before codes are submitted.
Care Management Support
AI is increasingly being applied to the care management component of RPM, helping identify missed opportunities in patient interactions—such as important topics not covered in recent visits that should be addressed in future encounters. By automating these checks, AI not only ensures accurate and defensible billing but also reduces administrative burden, minimizing the need for manual chart audits and follow-ups. This leads to more efficient workflows, keeping programs compliant and financially sustainable.
AI can also assist care managers by suggesting relevant social and community resources. For example, if a patient mentions difficulty accessing healthy food, the system may flag this information and prompt the care manager to recommend a local Meals on Wheels program or an upcoming nutrition class in the patient’s area.
Predictive Analytics and Risk Stratification
This is where AI in RPM becomes both powerful and sophisticated. Traditional systems, like electrocardiogram (ECG) analysis, often relied on hundreds of thousands of readings from a single data source. In contrast, AI-driven RPM can combine vital signs, clinical notes, patient-reported outcomes, and questionnaire responses to generate comprehensive risk assessments.
The true breakthrough comes from AI’s ability to detect subtle, previously unrecognized patterns. Individual patient responses that might not trigger alerts on their own can become clinically meaningful when analyzed collectively over time. This enables an early warning system for patient deterioration and risk, providing insights far beyond what human analysis could achieve at scale.
Need for Human Oversight
Responsible AI deployment is critical, particularly when predictive tools are used for high-acuity patients, where human clinical judgment remains essential. The concern isn’t solely AI accuracy—it’s also the risk of automation bias, where clinicians might over-rely on AI recommendations and reduce their own attentiveness.
For medium- and lower-risk patients, who cannot practically receive continuous human monitoring due to personnel and cost constraints, the question becomes less about AI versus humans and more about AI versus no monitoring. At a population level, having intelligent monitoring is far better than having none.
This highlights a core principle: AI in RPM should serve as decision support, not replace human decision-making. Clinicians must retain ultimate responsibility, verifying and validating AI-generated insights before applying them to patient care.
Challenges and Considerations
As AI becomes increasingly integrated into remote patient monitoring and wider healthcare workflows, it introduces both significant opportunities and added complexity. Organizations must carefully navigate several key challenges when evaluating and implementing AI within their RPM programs.
The “Black Box” Challenge
Even AI tools that perform well can remain opaque in how they reach their conclusions. In testing a summarization tool under development, what initially seemed like AI errors often turned out to be the system accurately detecting human mistakes. While this highlights AI’s potential, it also exposes a fundamental issue: even high-performing AI will occasionally err, and we may not know when or why.
This unpredictability reinforces the need for clinicians to remain vigilant. AI can appear convincingly correct while being wrong, so care teams must avoid over-reliance, regardless of the tool’s track record.
Vendor Selection and Due Diligence
The surge in AI adoption has drawn many companies with cutting-edge technology but limited healthcare experience. Similar to the early days of RPM—when wearable device firms entered the market without fully understanding clinical workflows—today’s AI landscape includes vendors that may lack the expertise required for safe, effective, and compliant care delivery.
Healthcare providers implementing RPM programs need to assess not only the technical capabilities of AI solutions but also the clinical experience and healthcare knowledge of potential vendor partners.
Looking Ahead: Balancing Innovation With Responsibility
Integrating AI into RPM presents a major opportunity to enhance care delivery, improve provider efficiency, reduce clinical risk, and expand monitoring to more patients who can benefit from these services. Achieving these gains, however, requires a careful approach that prioritizes patient safety, preserves human oversight, and addresses a range of ethical and operational concerns.
Success will hinge on providers’ ability to pair innovation with responsibility—using AI as a supportive tool to augment human clinical judgment rather than replace it. The future of AI in RPM is not about choosing between humans and machines; it is about combining both thoughtfully to build remote monitoring systems that are more effective, efficient, and accessible than either could accomplish alone.
