A Retrieval-Based Approach to Medical Procedure Matching in Romanian
This addresses administrative and insurance claim issues in private Romanian healthcare, but it is incremental as it applies existing retrieval-based methods to a new language and domain.
The paper tackled the problem of mapping medical procedure names to standardized terminology in Romanian healthcare to reduce administrative inefficiencies, achieving results by evaluating multiple embedding models to identify the most effective solution for this task.
Accurately mapping medical procedure names from healthcare providers to standardized terminology used by insurance companies is a crucial yet complex task. Inconsistencies in naming conventions lead to missclasified procedures, causing administrative inefficiencies and insurance claim problems in private healthcare settings. Many companies still use human resources for manual mapping, while there is a clear opportunity for automation. This paper proposes a retrieval-based architecture leveraging sentence embeddings for medical name matching in the Romanian healthcare system. This challenge is significantly more difficult in underrepresented languages such as Romanian, where existing pretrained language models lack domain-specific adaptation to medical text. We evaluate multiple embedding models, including Romanian, multilingual, and medical-domain-specific representations, to identify the most effective solution for this task. Our findings contribute to the broader field of medical NLP for low-resource languages such as Romanian.