PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 24, 2026Cureus0 citationsOpen Access

Use of an Artificial Intelligence Algorithm to Increase Productivity in Implantable Loop Recorder Monitoring: A Multicentre Observational Study

View Full Paper
CACherry AlexanderARAlan RobertsonSBSophie Bagnall

Key Points

  • This study aims to evaluate the effectiveness of an artificial intelligence algorithm in reducing alert burden in implantable loop recorder monitoring services.
  • Conducted a retrospective multicentre before-and-after cohort analysis of patients with 12-month ILR monitoring before and after AI activation.
  • Counted transmitted alerts during both periods and analyzed differences using paired t-tests.
  • Estimated workflow impact based on established time-and-motion data for remote review of alerts.
  • Total transmitted alerts decreased from 4,261 to 2,509, representing a 29% reduction post-AI activation.
  • The mean paired change in alerts showed a significant decrease of −3.94 (95% CI −7.5 to −0.4; p<0.05).
  • The reduction in alerts translated to a release of 185-218 hours of physiologist review time annually.

Abstract

Background: Insertable loop recorder (ILR) services are increasingly constrained by high volumes of transmitted episodes, many of which are false, clinically irrelevant, or non-actionable. Cloud-based artificial intelligence (AI) algorithms have the potential to suppress false atrial fibrillation (AF) and pause alerts while preserving clinically relevant events. We present the first real-world impact of an AI algorithm activated across a fixed multicentre ILR cohort. Methods: We performed a retrospective, multicentre before-and-after cohort analysis of consecutive patients. Eligible patients had continuous ILR monitoring for 12 months before and 12 months after service-wide activation ("switch-on") of the Medtronic AccuRhythm AI platform (Medtronic plc, Galway, Ireland), enabling within-patient comparison. All clinician-facing transmitted alerts were counted in each period. Secondary analyses examined the concentration of alert burden across patients and estimated workflow impact using published time-and-motion data for remote transmission review. Differences in alert counts were tested using paired t-tests, reporting mean paired differences with 95% confidence intervals (CI). Results: The cohort included 445 patients (Reveal LINQ n=438; LINQ II n=7); 440 (99%) were implanted for syncope (mean age 67±15 years; 49.6% male). Total transmitted alert volume fell from 4,261 pre-AI to 2,509 post-AI (29% reduction). The mean paired within-patient change was −3.94 alerts (95% CI −7.5 to −0.4; p90% of alerts, and 104 patients had intermittent disconnection from remote monitoring. Applying established workflow timings (11-13 minutes per remote transmission review) translated the reduction into 185-218 hours of physiologist review time released annually: approximately four hours per week of "virtual physiologist" capacity. Conclusions: In routine practice, AI activation in a multicentre observational study was associated with a statistically significant reduction in ILR alert burden and a clinically meaningful release of staff capacity. Parallel management of high-alert patients and connectivity optimisation may further amplify the operational benefit.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Alexander et al. (2026) studied this question.

synapsesocial.com/papers/69eb0b25553a5433e34b4ff7https://doi.org/10.7759/cureus.107519
Ask AI
Helpful
Bookmark
Share
View Full Paper