Telemedicine and health care pathway of atrial fibrillation patient
Authors: Isodiana IA , Abate AG , Escorcia EMJA , Camilletti CV , Bianco BLG , Cappella CD , Lettieri LE , Ruzzolini M 
Published: May 2022

Abstract

Funding Acknowledgements
Type of funding sources: None.

Background
Among cardiovascular diseases, atrial fibrillation is the most found arrhythmia in clinical practice and appears to have a significant impact on morbidity and mortality, which in turn represents a growing socio-economic burden for our health care system. COVID-19 pandemic has witnessed a pre-existing weakness of healthcare structures which has led to an irreversible collapse of hospitals’ performance and a turning point for the implementation of telemedicine.

Purpose
The goal is to produce a literature review of applications and benefits which adoption of telemedicine in patients affected by atrial fibrillation could bring. The review presents implemented solutions in telemedicine field according to their respective key performance indicators (KPIs) to bring scientific evidence to papers and technological solutions.

Methods
Using PubMed electronic database, 47 articles were selected by entering keywords connected with both AF and Telemedicine and then skimmed-conducted through SCImago Journal Rank. They were divided into two AF-patient care pathways: diagnosis (18 articles) and follow-up (29 articles). The analysis was based on the key performance indicators KPI of several technologies proposed.

Results
Several innovative solutions implemented were found and that can be mainly clustered in two areas: wearables and apps for smartphone. For diagnosis, KPIs considered are accuracy(90,52-99,62%), sensitivity(62,5-100%), specificity(64,5-100%), PPV(64,7-95%) and NPV(95-100%). All these indicators show promising values. Regarding follow-up, KPIs are: adherence(70-99%), atrial fibrillation alert (AFA), specificity(83,3-100%), all-cause death, PPV (39,9-99,78%), sensitivity(98%), main adverse events (-3,3%), were calculated related to patient’s sphere; time investments, time to clinical decision in response to clinical events, unscheduled follow up and emergency admission of urgent follow-up were considered related to hospitals, while KPIs related to Healthcare system range of action are non-hospital and hospital costs, time rehospitalization and clinical follow-up and reduction in average hospitalization time. KPIs reveal an improvement in efficiency at a patient’s health status, hospitals, and health system level. Finally, some solutions have witnessed versatile functionality in both diagnosis and follow-up phases.

Conclusions
MHealth devices show feasibility for the detection of AF since they promote mass screening of pathology and represent a user-friendly approach to monitor diseases by population. Diagnostic performance reported depends also on population attitude towards usefulness of that technology, their risk of AF, and kind of technology used. Follow-up revision reveals a broader perspective of results which remarkably declares the implementation of remote monitoring using multiple technologies through adherence to treatments, quantity, and quality of data available for the analysis of the progress of the patient.