Enhancing Early Chronic Disease Detection Through Coordinated Efforts In Medical Coding, Epidemiological Monitoring, Radiologic Imaging, And Nursing Assessment

Abstract

The global epidemiological landscape has shifted decisively from acute infectious pathologies to chronic non-communicable diseases (NCDs), which now constitute the primary burden on healthcare systems worldwide. As the economic impact of conditions such as cardiovascular disease, diabetes, and malignancy approaches an estimated $47 trillion by 2030, the imperative for health systems to transition from reactive treatment models to proactive early detection frameworks has never been more acute. This systematic review examines the mechanisms by which early detection can be enhanced through the integration of four critical pillars: Medical Coding, Epidemiological Monitoring, Radiologic Imaging, and Nursing Assessment. By synthesizing evidence from diverse global contexts—including the technologically advanced Healthier SG initiative in Singapore, the community-anchored Hiperdia system in Brazil, and integrated care models in China and the West—this report demonstrates that the coordination of these disciplines reduces fragmentation, improves risk stratification, and significantly shortens the time-to-diagnosis. The analysis reveals that while technological advancements in Artificial Intelligence (AI) and predictive analytics provide the necessary tools for population health management, their efficacy is contingent upon high-fidelity data input from medical coding, structural visualization through advanced radiology, and the navigational support provided by nursing professionals. The convergence of these fields into "Medical Digital Twins" and integrated multidisciplinary teams offers a viable pathway to mitigate the rising tide of chronic disease morbidity and mortality.