Negation detection is a critical component in extracting clinical insights from unstructured texts, especially within ETL (Extract, Transform, Load) pipelines for healthcare data integration. Traditional regex-based approaches, while effective, demand substantial effort, including extensive data annotation and iterative feedback from clinicians to craft and maintain domain-specific rules. In contrast, this paper introduces an encoder-based method for negation assertion detection in Italian clinical texts, achieving comparable performance while significantly reducing the need for clinical input. By leveraging pre-trained transformers and neural translation on public data for domain alignment and language localization, our approach offers a low-effort alternative to clinical negation detection on real-world, unseen entities from multiple Italian hospitals, maintaining high accuracy. These findings suggest an advanced maturity of encoder-based methods that can be leveraged by medical institutions to reduce development overhead, offering a scalable alternative to regular expressions for fundamental text processing bricks of clinical ETL workflows.