Comprehensive Summary
This article evaluates the cost-effectiveness of the Anemia Control Model, an AI-powered decision-support system designed to improve anemia management in patients undergoing in-center hemodialysis in Germany. Using a Markov cohort state-transition model from the perspective of German statutory health insurance, the study projected lifetime costs and quality-adjusted life years, discounting both at 3% annually. Results showed that ACM consistently produced higher QALYs while lowering overall costs compared with standard anemia care. The system achieved a net monetary benefit of €38,423 per patient in the base case, and sensitivity analyses confirmed that outcomes were robust, with the annual cost of erythropoiesis-stimulating agents identified as the most influential variable. Overall, the study concludes that the ACM represents a dominant strategy, both more effective and less costly, than standard care.
Outcomes and Implications
The findings suggest that integrating AI-based decision-support tools into routine hemodialysis care could significantly improve patient outcomes while reducing healthcare costs, offering clear benefits for both providers and payers. By optimizing anemia therapy, particularly the use of costly erythropoiesis-stimulating agents, the ACM may help health systems allocate resources more efficiently. For policymakers, the results provide strong economic justification for adopting AI-driven systems in dialysis management, potentially informing broader strategies for digital health innovation. Clinically, widespread implementation could enhance achievement of hemoglobin targets, support personalized treatment decisions, and reduce complications associated with suboptimal anemia control. While promising, these benefits will require real-world validation and thoughtful integration into clinical workflows to ensure that the predicted effectiveness and cost savings translate effectively into practice.