Deep Learning for Cardiovascular Management: Optimizing Pathways and Cost Control under Diagnosis-Related Group Models

Haohao CHEN^1,2Ying Zeng¹De Cai^1*

• ¹First Affiliated Hospital of Shantou University Medical College, Shantou, China
• ²Department of Pharmacy, College of Medicine, Shantou University, Shantou, Guangdong Province, China

Cardiovascular diseases (CVDs) remain the leading causes of morbidity, mortality, and healthcare expenditures, presenting substantial challenges for hospitals operating under Diagnosis-Related Group (DRG) payment models. Recent advances in deep learning offer new strategies for optimizing CVD management to meet cost control objectives. This review synthesizes the roles of deep learning in CVD diagnosis, treatment planning, and prognostic modeling, emphasizing applications that reduce unnecessary diagnostic imaging, predict high-cost complications, and optimize the utilization of critical resources like ICU beds. By analyzing medical images, forecasting adverse events from patient data, and dynamically optimizing treatment plans, deep learning offers a data-driven strategy to manage high-cost procedures and prolonged hospital stays within DRG budgets. Deep learning offers the potential for earlier risk stratification and tailored interventions, helping mitigate the financial pressures associated with DRG reimbursements. Effective integration requires multidisciplinary collaboration, robust data governance, and transparent model design. Real-world evidence, drawn from retrospective studies and large clinical registries, highlights measurable improvements in cost control and patient outcomes; for instance, AI-optimized treatment strategies have been shown to reduce estimated mortality by 3.13%. However, challenges—such as data quality, regulatory compliance, ethical issues, and limited scalability—must be addressed to fully realize these benefits. Future research should focus on continuous model adaptation, multimodal data integration, equitable deployment, and standardized outcome monitoring to validate both clinical quality and financial return on investment under DRG metrics. By leveraging deep learning’s predictive power within DRG frameworks, healthcare systems can advance toward a more sustainable model of high-quality, cost-effective CVD care.