Clinical perspectives on remote cardiac monitoring
Practical writing on ECG patch workflows, AI classification, cardiology practice operations, and patient monitoring from the ElectroKare team.
Why Patient Adherence Determines Your Monitoring Yield
Detection rates for paroxysmal arrhythmias are directly tied to whether patients keep the patch on for the full monitoring window.
Where AI ECG Classification Gets It Wrong and Why Clinical Review Still Matters
Automated ECG analysis is accurate for the common arrhythmias but predictably struggles with artifact patterns and borderline rhythms.
Closing the Gap Between Continuous Monitoring and the Follow-Up Visit
The value of a 14-day ambulatory ECG patch erodes if clinically significant findings are not reviewed until the next scheduled appointment.
HIPAA Considerations When Selecting Remote Patient Monitoring Software
What cardiology practices need to verify before routing de-identified ECG data to a third-party analysis platform.
AFib Burden vs. Episode Detection: What Your Monitoring Report Should Tell You
Detecting an AFib episode is not the same as quantifying AFib burden. The difference matters for anticoagulation decisions and ablation candidate selection.
Designing a Clinical Workflow for Remote ECG Monitoring at Scale
When a cardiology practice grows its remote monitoring panel beyond 30 patients, informal review processes break down.
How to Build an Alert Queue That Cardiologists Will Actually Use
Alert fatigue is the leading reason cardiac monitoring programs stall. The difference between a useful alert and noise comes down to severity tiering and context.
ECG Patch Signal Quality: What Causes Noise and How AI Handles It
Motion artifact, electrode lift, and baseline wander can corrupt significant portions of a 14-day patch recording. A look at preprocessing steps before classification begins.
Remote Cardiac Monitoring and Readmission Risk: Where Is the Evidence?
Post-discharge cardiac monitoring programs have produced mixed results in the readmission literature. A critical look at the evidence by patient population.
How AI Detects Paroxysmal AFib in Long-Duration Ambulatory ECG Data
Paroxysmal AFib can account for fewer than 2 percent of total recording time in a 14-day study. How automated analysis finds brief intermittent episodes.
What Cardiologists Miss in the Weeks Between Scheduled Visits
The gap between a patient's patch-wear period and their next office appointment is where most paroxysmal arrhythmia events go unreviewed.