Integrating Nursing and Pharmacy Practices in Early Detection of Silent Cardiac Diseases Using Artificial Intelligence and ECG Analysis
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Abstract
Silent cardiac diseases present a significant yet overlooked public health challenge due to their asymptomatic nature. Artificial intelligence combined with electrocardiogram analysis provides a promising strategy for early detection. This initiative identifies specific interprofessional roles for nursing and pharmacy in the early screening, triage, and referral of cases that warrant further investigation. An integrated approach is applied with real ECG evidence and data-driven physician decision support. Pilot studies assess feasibility and clinical performance. Silent cardiac diseases—especially arrhythmias and ischaemia—affect millions worldwide and contribute to thousands of avoidable deaths every year. Despite this burden, there is an absence of dedicated screening programs. Rapid telephone screening by nursing staff, combined with electrocardiogram analyses of ECG data—especially those provided by patients via mobile phones—could enable early detection. This role aligns with the traditional goal of reducing health inequalities, as nursing and pharmacy staff are often the most accessible health-care professionals. Although automatic AI-based decision support is available, clinical adoption remains limited due in part to understandable fears of injury associated with incorrect displays. The integration of nursing and pharmacy staff enables safe access to unregulated analysis and rapid exploration of adverse pharmacotherapy.
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