Automated structuring of medical prescriptions is critical for downstream safety checks in pharmacies, yet remains challenging due to heterogeneous layouts, OCR noise, and dense clinical abbreviations in real-world documents. Existing language models either ignore layout information, rely on computationally expensive image-based architectures, or cannot operate under strict privacy and hardware constraints such as GDPR and HDS-certified environments.We present a lightweight (<10M parameters), privacy-preserving transformer specifically designed for Entity Extraction (EE) and Entity Linking (EL) in French medical prescriptions. The model uses only OCR text and normalized 2D word coordinates, enabling robust pseudonymisation and real-time CPU-level inference while preserving essential spatial cues. It is pretrained on a large corpus of pseudonymised OCR outputs using objectives tailored to prescription structure, including a novel Token-to-Line Alignment (TLA) task, and fine-tuned on the Rx-PAD dataset (Pattin Cottet et al., 2025).Empirical results show that our approach matches or surpasses larger document-understanding models and rivals multimodal LLMs on strict extraction metrics, while achieving sub-second latency suitable for operational deployment. The system is currently used in 230 pharmacies, demonstrating both scalability and practical relevance. These findings highlight the importance of specialized, domain-aware, lightweight models for safe, efficient, and legally compliant prescription verification.