Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group...

Area of Science:

• Cardiovascular Medicine
• Artificial Intelligence
• Health Economics

Background:

• Cardiovascular diseases (CVDs) are leading causes of morbidity, mortality, and healthcare costs.
• Diagnosis-Related Group (DRG) payment models pose financial challenges for hospitals managing CVD.
• Deep learning (DL) presents novel strategies for optimizing CVD care and cost control.

Purpose of the Study:

• To review the applications of deep learning in cardiovascular disease diagnosis, treatment planning, and prognostic modeling.
• To emphasize DL's role in reducing unnecessary imaging, predicting high-cost complications, and optimizing resource utilization.
• To explore DL's potential for risk stratification and tailored interventions within DRG budgets.

Main Methods:

• Analysis of medical images using deep learning algorithms.
• Forecasting adverse patient events from comprehensive patient data.
• Dynamic optimization of treatment plans and critical resource allocation (e.g., ICU beds).

Main Results:

• Deep learning enables data-driven management of high-cost procedures and prolonged hospital stays.
• AI-optimized strategies have demonstrated a reduction in estimated mortality by 3.13%.
• Measurable improvements in cost control and patient outcomes are evident from real-world evidence.

Conclusions:

• Deep learning offers a pathway to more sustainable, high-quality, and cost-effective cardiovascular care under DRG payment models.
• Effective integration requires multidisciplinary collaboration, robust data governance, and transparent model design.
• Future research should address challenges like data quality, scalability, and ethical considerations for equitable deployment.