Seven Ways to Reduce Data Burden in Cardiology Clinics
Quick tips to streamline device data management.
Executive Summary
Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has become central to electrophysiology practice. The benefits are well-documented: earlier detection, improved adherence, and fewer hospitalizations.
But many U.S. device clinics are struggling under the weight of their own success. Alert volumes have doubled in less than a decade, staff turnover is rising, and burnout is at an all-time high.¹⁻²
In 2023, the Heart Rhythm Society (HRS), European Heart Rhythm Association (EHRA), Asian Pacific Heart Rhythm Society (APHRS), and Latin American Heart Rhythm Society (LAHRS) issued a joint consensus: *remote monitoring is essential but critically under-resourced.*³
Below are seven evidence-backed strategies to make RM scalable, efficient, and sustainable, without compromising clinical quality.
1. Shift to Alert-Based Monitoring, Not Scheduled Downloads
The problem:
Many clinics still rely on routine scheduled transmissions every 1-3 months, regardless of clinical need. This creates unnecessary data volume and staff workload.
The evidence:
Studies show that up to 70% of RM transmissions are non-actionable.⁴ The 2023 HRS/EHRA consensus recommends an alert-based model that prioritizes clinically meaningful events.³
The action:
- Reprogram devices and portals to suppress repetitive or trivial notifications.
- Define high-value alerts such as arrhythmia onset, lead issues, or device performance problems.
The payoff:
Clinics adopting alert-based monitoring have reported 40–60% reductions in workload with no safety compromises.⁴⁻⁵
2. Optimize Device Programming and Transmission Parameters
The problem:
Default device settings often flood clinics with low-priority data.
The evidence:
Targeted reprogramming reduced alert volume by 65% without affecting safety, according to JACC: Clinical Electrophysiology.⁴
The action:
- Partner with electrophysiologists and vendor reps to tune alert thresholds.
- Re-evaluate settings annually or after software updates.
The payoff:
Smarter programming yields fewer false positives and more time for meaningful reviews.
3. Implement Tiered Triage and Role-Based Escalation
The problem:
When every alert lands on a clinician’s desk, bottlenecks and burnout follow.
The evidence:
A three-tier model automation, technician review, clinician oversight, streamlines response and improves outcomes.³⁻⁶
The action:
- Document escalation thresholds and triage protocols.
- Use decision trees and automation to route alerts efficiently.
The payoff:
Clinics using this model report 30% faster resolution of actionable alerts.⁵
4. Build Proactive Connectivity and Patient Education Workflows
The problem:
Lost device connectivity creates silent workload and clinical risk.
The evidence:
Patient education and connection monitoring cut reconnection calls and missed transmissions by up to 50%.⁷
The action:
- Educate patients on RM function, timing, and troubleshooting.
- Automate “no signal” reminders to address issues early.
The payoff:
Better engagement reduces workload, enhances compliance, and improves patient safety.
5. Track Operational Metrics and Refine Workflows
The problem:
Without visibility into performance, inefficiencies persist.
The evidence:
Tracking KPIs, such as alerts per patient, review time, and actionable transmission rates, drives measurable improvement.³⁻⁸
The action:
- Build a simple dashboard to monitor metrics monthly.
- Suppress alert types dismissed over 95% of the time.
The payoff:
Data-driven insight helps clinics optimize staffing and reduce wasted effort.
6. Pilot Before Scaling Across the Clinic
The problem:
Overhauling workflows across large patient populations can overwhelm teams.
The evidence:
Incremental pilots yield faster adoption and fewer errors.⁹
The action:
- Start small, one site, one device type, or one patient group.
- Measure pre- and post-implementation metrics before scaling.
The payoff:
Phased rollouts lower risk, build confidence, and enable continuous improvement.
7. Staff Strategically and Standardize SOPs
The problem:
Remote monitoring is often added “on top” of existing duties, leading to understaffed teams.
The evidence:
Dedicated RM teams increase accuracy and throughput.³⁻⁶ Recommended staffing: 3 FTEs per 1,000 active RM patients.³
The action:
- Assign defined roles for triage, reconciliation, and outreach.
- Create clear SOPs, escalation trees, and coverage schedules.
The payoff:
Dedicated staffing leads to sustainable workflows, lower turnover, and higher morale.
Conclusion
Remote monitoring has transformed cardiac care, but its success hinges on sustainability. By embracing alert-based workflows, smart programming, structured triage, proactive patient engagement, continuous metrics tracking, and dedicated staffing, clinics can thrive both operationally and clinically.
The Lehigh Valley Heart & Vascular Institute offers proof: optimizing workflows and adding AI-assisted triage improved billing efficiency from 68% to 94%, recovered $365,000 in annual revenue, and cleared report backlogs within 90 days.
“We went from drowning in data to operating with precision. It completely changed how our clinic functions.”
– Electrophysiologist, Lehigh Valley Heart and Vascular Institute
As patient volumes rise and technology evolves, scalable RM workflows are no longer optional – they’re essential for the sustainability of cardiac care teams.
References
Slotwiner DJ, et al. HRS/EHRA/APHRS/LAHRS Expert Consensus on Remote Monitoring of CIEDs. Heart Rhythm. 2023;20(5):e101–e145.
Varma N, et al. Challenges in Managing a Remote Monitoring Device Clinic. Heart Rhythm O2. 2021;2(5):440–447.
O’Shea CJ, et al. Clinical Burden of Device Alerts and Implications for Remote Monitoring. JACC Clin Electrophysiol. 2020;6(9):1100–1110.
Maines M, et al. Remote Monitoring: How to Maximize Efficiency. J Cardiovasc Electrophysiol. 2024;35(2):376–384.
Slotwiner DJ, et al. Performance Metrics in Remote Monitoring. Heart Rhythm. 2023;20(5):e101–e145.
Varma N, et al. Ensuring Connectivity in Remote Monitoring Programs. Europace. 2023;25(5):euad123.
Maines M, et al. Implementing Incremental Workflow Change in Remote Monitoring Clinics. J Cardiovasc Electrophysiol. 2024;35(2):376–384.
Varma N, et al. Staffing and Operational Models for RM Efficiency. Europace. 2023;25(5):euad123.
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