Abstract Body (Do not enter title and authors here): BACKGROUND: Mobile electrocardiogram (mECG) algorithms have been validated for early detection of atrial fibrillation (AF), with a potential for reduced arrhythmia-related healthcare utilization. In this study, we compare healthcare utilization metrics of mECG and non-mECG users who previously underwent AF ablation to provide insights about resource utilization during the COVID 19 pandemic. METHODS: This is a retrospective study of patients with known AF who underwent ablation in 2020. We identified patients as having an mECG device through chart review of the electronic health record. Baseline demographic, clinical, and procedural characteristics were collected in addition to resource utilization outcomes 1-year pre- and post-ablation. COHORT: There were 322 patients who met study criteria, with n=144 being mECG users and n=178 being non-mECG users. The mECG and non-mECG user groups were comparable by age, sex, race, and clinical/ echocardiographic factors (Table 1). There was a greater proportion of persistent AF in the non-mECG user group. RESULTS: mECG users sent a significantly larger number of secure messages to their providers in both the pre- and post-ablation period (Figure 1). There were no statistically significant differences in outpatient provider visits, ED visits, or hospitalizations (Table 2). Success rates of ablation were comparable in both groups. DISCUSSION: mECG users, compared to non-mECG users, sent more secure messages to providers both pre and post ablation, but mECG users did not have a reduction in provider, ED or hospital visits at 1 year post AF ablation. Given that utilization of a mECG device increased the number of communications with providers, more work should be done to develop workflows for clinicians to triage, manage and integrate mobile ECG data into clinical practice. KEY TAKEAWAYS: Mobile ECG users sent significantly more messages to providers, without a reduction in provider, ED, or hospital visits. Integration of wearable device data into clinical care requires further understanding on the provider specific and systemic impacts on resource utilization.