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Monitoring and Follow-Up of Patients on Vitamin K Antagonist Oral Anticoagulant Therapy Using Artificial
Adolfo Romero-Arana1, Nerea Romero-Sibajas2, Elena Arroyo-Bello1
1Fundación Jiménez Díaz School of Nursing, Health Research Institute, Fundación Jiménez Díaz University Hospital, Universidad Autónoma de Madrid (IIS-FJD, UAM), 28040 Madrid, Spain.
Background: Vitamin K antagonist oral anticoagulant (VKA) therapy, using warfarin or acenocoumarol in our health system, is indicated, according to clinical guidelines, for the prophylaxis of thromboembolic events. In Málaga, the VKA patient management program currently includes a total of 856 patients. Hypothesis: The use of an AI-based application can enhance treatment adherence among VKA patients participating in self-monitoring and self-management programs. Furthermore, it can support the comprehensive implementation of the system, leading to reduced costs and fewer interventions for anticoagulated patients. Methods: The study will be conducted in several phases. The first phase involves the development of the application and the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second phase includes preliminary testing and validation of the developed application. The third phase consists of full implementation, along with an assessment of user-identified needs and potential quality improvements. Expected Results: The implementation of the AIto-Control app is expected to reduce healthcare-related costs by decreasing primary care visits and hospital admissions due to thromboembolic or bleeding events. Additionally, it aims to ease the workload on both primary care and hospital services. These outcomes will be achieved through the involvement of advanced practice nurses who will supervise app-based monitoring and patient education.
Background: Vitamin K antagonist oral anticoagulant (VKA) therapy, using warfarin or acenocoumarol in our health system, is indicated, according to clinical guidelines, for the prophylaxis of thromboembolic events. In Málaga, the VKA patient management program currently includes a total of 856 patients. Hypothesis: The use of an AI-based application can enhance treatment adherence among VKA patients participating in self-monitoring and self-management programs. Furthermore, it can support the comprehensive implementation of the system, leading to reduced costs and fewer interventions for anticoagulated patients. Methods: The study will be conducted in several phases. The first phase involves the development of the application and the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms. The second phase includes preliminary testing and validation of the developed application. The third phase consists of full implementation, along with an assessment of user-identified needs and potential quality improvements. Expected Results: The implementation of the AIto-Control app is expected to reduce healthcare-related costs by decreasing primary care visits and hospital admissions due to thromboembolic or bleeding events. Additionally, it aims to ease the workload on both primary care and hospital services. These outcomes will be achieved through the involvement of advanced practice nurses who will supervise app-based monitoring and patient education.
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