This paper presents a retrieval-based approach for medical procedure matching in the Romanian healthcare system, addressing the challenges posed by inconsistent naming conventions across clinics and insurance providers. We demonstrate that dense sentence embeddings, particularly fine-tuned multilingual models, significantly outperform traditional sparse methods such as BM25. Our experiments show that a fine-tuned mE5 model achieves the highest retrieval accuracy, with an Acc@1 of 85.2\% when using both masterlist entries and clinical mappings. The real-world evaluation further confirms the efficacy of our approach, achieving a validated accuracy of 94.7\% in a doctor-reviewed dataset. Furthermore, our systems enables significant labor efficiency: using our automated matching systems results in 1200$\times$ speedup compared to manual matching. Our findings contribute to the broader domain of medical NLP for low-resource languages and offer a viable solution for improving the Romanian healthcare system. 