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As remote monitoring programs expand and clinics face the realities of growing transmission volume and workflow strain, device clinics are facing a new challenge: scale.
Patient volumes are increasing, and so is the number of transmissions that need to be reviewed.
Traditional workflows rely on manual review, documentation, and follow-up coordination. These processes take time, and they do not scale efficiently as programs grow.
The question is straightforward. How can clinics manage more patients without needing more staff?
Why Scaling Remote Monitoring Is Difficult
Scaling a remote monitoring program is not simply a matter of enrolling more patients. As transmission volume increases, so does the amount of work required to review data, document findings, and coordinate next steps.¹,²
In practice, remote monitoring alert management alone can account for roughly 30% of total clinic workload, placing sustained pressure on existing teams
Much of that work still depends on manual processes. Clinicians and support staff must sort through incoming transmissions, determine whether follow-up is needed, and ensure that relevant information is documented appropriately.¹
These tasks are clinically important, but they also consume time that does not always correspond to meaningful patient action, particularly in workflows shaped by non-actionable alerts and alert fatigue.
In real-world device clinics, a large proportion of transmissions do not require intervention. In one 2024 analysis, 85% of patient-initiated transmissions were clinically non-actionable, and only 3.6% resulted in an in-person visit.
Time and resource utilization studies have shown that remote device follow-up can place substantial demands on clinic workflows, particularly when growing transmission volume is managed within traditional review models.² As programs expand, that burden can make it difficult for existing teams to keep pace without adding staff.
The challenge, then, is not only clinical. It is operational. Without a more efficient workflow, growth in patient volume can quickly translate into growth in workload.
How AI-Assisted Triage Improves Efficiency
Scaling remote monitoring requires reducing the amount of manual work tied to each patient. AI-assisted triage addresses this by limiting how many transmissions require human review and how work flows through the system.
Reducing the Volume of Manual Review
Scaling remote monitoring requires reducing the amount of manual work tied to each patient. AI-assisted triage addresses this by limiting how many transmissions require human review.
Instead of routing every transmission through the same workflow, the system evaluates incoming data and filters out non-actionable events through AI-assisted triage in cardiac device monitoring. This reduces the total volume of transmissions that need to be reviewed by device clinic staff.
This is particularly important given that a very small percentage of transmissions ultimately require clinical action. In some cohorts, fewer than 2% of unscheduled transmissions led to a management change.
In a multicenter evaluation of AI-assisted device monitoring workflows, more than one-quarter of all transmissions were handled without human review.³ By removing routine data from the workflow, AI decreases the amount of time required per patient.
Increasing Capacity Without Increasing Staff
This change is what enables scale. When fewer transmissions require manual review, existing teams can manage larger patient populations without a proportional increase in workload.
Rather than expanding headcount to match growth, clinics can absorb additional volume by reducing the amount of work required per transmission.
Reducing Review Time for Escalated Alerts
AI-assisted triage does not only reduce the number of transmissions that require review. It also changes how those transmissions are presented to clinicians.
Structured Data Instead of Raw Transmissions
In traditional workflows, clinicians often review raw device data to determine whether follow-up is needed. This process can be time-intensive, particularly when interpreting large volumes of routine or low-priority information.¹
AI-assisted triage introduces a more structured approach. For escalated alerts, clinicians receive curated summaries that highlight clinically relevant findings, rather than reviewing full transmission data.
Faster Clinical Decision-Making
By reducing the need to interpret raw data, structured summaries can shorten the time required to assess each alert.¹ Clinicians can move more quickly from review to decision, improving workflow efficiency without changing clinical responsibility.
This shift further supports scalability. When each alert takes less time to evaluate, total review capacity increases even if staffing levels remain the same.
Enabling Clinical Teams to Operate at Scale
Improvements in workflow efficiency are only meaningful if they translate into increased capacity for clinical teams. In remote monitoring, that means enabling nurses, advanced practice providers, and electrophysiologists to manage larger patient populations without a proportional increase in workload.
Expanding Capacity Across Clinical Roles
By reducing both the number of transmissions that require review and the time required to evaluate each alert, AI-assisted triage changes how work is distributed across the care team.
Routine transmissions are filtered out before reaching clinical queues, while escalated alerts are presented in a more structured format. This allows clinical staff to focus on higher-priority cases rather than dividing attention across all incoming data.
As a result, each role within the device clinic can operate more efficiently, with less time spent on low-value tasks and more time allocated to clinically meaningful decisions.
Supporting Scalable Remote Monitoring Models
Professional society guidance recognizes that sustaining remote monitoring programs at scale requires workflow optimization, including the use of automation and external support models.³
AI-assisted triage aligns with this approach by reducing the operational burden associated with growing transmission volume. Rather than relying solely on additional staffing, clinics can increase capacity by improving how work is processed and prioritized.
This creates a more scalable model of care, where growth in patient volume does not require a matching increase in clinical headcount.
Scaling Without Increasing Headcount
Traditional remote monitoring models scale linearly. As patient volumes increase, the amount of work increases with them, often requiring additional staff to maintain performance.
AI-assisted triage introduces a different model. By reducing the number of transmissions that require review and shortening the time needed to evaluate each alert, it changes the relationship between volume and workload.
Growth no longer has to result in proportional increases in staffing. Instead, clinics can expand patient panels by improving how work is processed, prioritized, and completed.
This shift is what makes large-scale remote monitoring programs sustainable. Capacity is no longer defined solely by headcount, but by the efficiency of the workflow itself.
The Next Phase: Measuring the Impact of Scale
AI-assisted triage makes it possible to manage growing remote monitoring programs without a proportional increase in staffing.
By reducing manual workload and improving review efficiency, clinics can expand capacity while maintaining performance.
This shift changes how remote monitoring programs are designed. Scaling is no longer limited by headcount alone, but by how efficiently clinical workflows are structured and executed.
As these models mature, the impact extends beyond operations. Changes in staffing requirements, workflow efficiency, and program scale begin to influence the financial performance of remote monitoring programs.
In the next article, we examine the financial impact of intelligent remote monitoring and what these operational improvements mean for cost, reimbursement, and long-term sustainability.
You can read that article here.