Ensuring accurate mapping between medical procedure names used by different healthcare providers and a standardized terminology set maintained by health insurance companies is a challenging task, with real-world applications. Discrepancies in naming conventions can lead to administrative inefficiencies, misclassification of procedures, and potential barriers for patients seeking insurance coverage. These mismatches can result in denied claims, increased processing times, and overall inefficiencies in the healthcare reimbursement process. For example, "The State of Claims: 2024" report \footnote{\href{https://www.experian.com/healthcare/resources-insights/thought-leadership/white-papers-insights/state-claims-report}{The State of Claims: 2024, Accessed 19.03.2025}} reveals that 46\% of denied claims are due to missing or innacurate data and coding errors. 

\begin{figure}[t!]
    % Link: https://drive.google.com/file/d/15TE-DXieF8yplRzHsOD9jJ-5RcZ9hx4k/view?usp=sharing 
    \centering
    \includesvg[width=\linewidth]{images/medmatch-diagram.drawio.svg}
    \caption{Diagram of the medical procedure matching problem. Clinics often have their own local names for medical procedures that are changed annually, for which a central insurance agency must match to a standardized list of procedures for reimbursement.}
    \label{fig:medmatch-diagram}
\end{figure}

Matching procedure names is similar to the well-known problems of entity resolution and text matching, yet it presents unique challenges in the medical domain. The complexity stems from several factors: \textit{(i)} medical terminology is highly domain-specific and varies across institutions, \textit{(ii)} data distributions are often imbalanced due to the frequency of common procedures overshadowing rare ones, \textit{(iii)} nomenclatures evolve over time, necessitating adaptive matching techniques, and \textit{(iv)} the presence of noise in text data, including typographical errors and abbreviations further complicates standardization efforts. Figure \ref{fig:medmatch-diagram} illustrates this problem. While previous studies have addressed similar challenges \cite{tavabiSystematicEvaluationCommon2024,levyComparisonMachineLearningAlgorithms2022,zaidatCanNovelNatural2024}, most focus on healthcare systems in the United States or other widely studied regions \cite{alexanderOverviewInpatientCoding2003}. International standards are typically adapted by each country, and private insurance companies may develop their own coding schemes, making a universal solution impractical.

This issue is particularly pressing for underrepresented languages such as Romanian. Despite growing interest in NLP for low-resource languages \cite{nigatu-etal-2024-zenos}, Romanian remains significantly underrepresented in medical NLP research. Existing language models such as RoBERT \cite{masala-etal-2020-robert} and RoLLaMA \cite{masala2024vorbecstiromanecsterecipetrain} provide general-purpose Romanian embeddings, but they lack the necessary specialization for medical text processing. Often, for real-world scenarios, multilingual models are ubiquitously used \cite{wangMultilingualE5Text2024,wang2020minilm}, even if they might fail to capture language-specific nuances.

In this paper, we propose a retrieval-based architecture for medical procedure matching. By leveraging metric learning and dense vector representations of procedure names \cite{ramesh-kashyap-etal-2024-comprehensive}, our method can handle a variable number of input-output mappings, can be expanded without retraining the entire model, and integrate efficiently with scalable vector search frameworks such as Milvus \cite{wangMilvusPurposeBuiltVector2021}. This makes retrieval an attractive paradigm for medical name matching, as it enables continuous updates and adaptation to changing medical taxonomies without extensive human intervention. We empirically evaluate three sentence embedding models \cite{wangMultilingualE5Text2024,masala-etal-2020-robert,alsentzer2019clincalbert}, comparing their effectiveness in Romanian medical name matching. 

By focusing on the Romanian healthcare system, our study highlights the broader challenges of medical terminology standardization and provides insights that can inform similar efforts in other low-resource languages. We aim to contribute to the development of robust, scalable, and language-aware retrieval methods for healthcare applications, ultimately improving the efficiency and accessibility of medical insurance systems.

Our contributions are as follows:

\begin{enumerate}
    \item We propose a retrieval-based architecture for matching medical procedure names across different healthcare providers and insurance companies, addressing a pressing real-world problem in the Romanian healthcare system.
    \item We conduct an extensive evaluation of various sentence embedding models, both Romanian \cite{masala-etal-2020-robert}, multilingual \cite{wangMultilingualE5Text2024} and specialized in the medical domain \cite{alsentzer2019clincalbert}, highlighting their performance in the context of Romanian medical text matching.
\end{enumerate}