Remote Monitoring Model Based on Artificial Intelligence to Optimize DOAC Therapy: A Working Hypothesis for Safer...

Background: Direct oral anticoagulants (DOACs) have become the standard of care for preventing venous thromboembolism (VTE) and cardioembolic stroke in patients with atrial fibrillation, due to their predictable pharmacokinetics and reduced need for frequent laboratory monitoring. However, long-term DOAC use still carries a risk of complications such as gastrointestinal or occult bleeding and progressive renal decline, particularly in elderly and frail patients. Objective: This study proposes a remote monitoring model integrated with AI supports designed to enhance the safety and personalization of chronic DOAC therapy in both inpatient and outpatient settings. Methods: Building on existing national frameworks in which DOAC prescriptions are regulated by experienced physicians through regional digital platforms, we developed a structured model that integrates automatic alerts for abnormal laboratory trends, potential drug interactions, and changes in clinical status. The system uses artificial intelligence to identify high-risk patterns, such as declining hemoglobin or glomerular filtration rate, before symptoms appear, enabling early intervention. Results: The proposed model is presented as an integrated workflow supported by structured components. This conceptual framework facilitates real-time surveillance of patient data, supports clinical decision-making, and is expected to reduce preventable complications. Anticipated benefits include improved clinical appropriateness, better resource allocation, and reduced avoidable emergency visits. Conclusions: remote monitoring system integrated with AI supports for predefinite items for long term treatment with DOACs can significantly improve safety and continuity of care. By replacing passive surveillance with predictive, automated alerts, this model exemplifies how digitalization can enhance the efficiency and responsiveness of the National Health System.