Stud Health Technol Inform. 2025 Apr 24;324:234-239. doi: 10.3233/SHTI250194.
ABSTRACT
BACKGROUND: Standardizing laboratory data is essential for interoperability and secondary use in clinical research and healthcare. However, many laboratory information systems (LIS) still rely on internal codes rather than internationally recognized terminologies, hindering data exchange, queryability, and integration into health data infrastructures.
OBJECTIVES: This study aimed to automate the extraction and mapping of internal lab codes to LOINC to improve structured data integration by utilizing web scraping and terminology mapping, we sought to create a FHIR-compliant ConceptMap.
METHODS: Guided by key requirements for structured data integration, we developed a Python-based workflow to extract and process laboratory data from an internal lab index. Using Selenium, BeautifulSoup, and Pandas, the extracted data was mapped to LOINC codes and transformed into a FHIR-compliant ConceptMap.
RESULTS: The workflow extracted 2,870 analytes, mapping 768 (27%) to LOINC. The automated process demonstrated feasibility and scalability.
CONCLUSION: The approach enables structured laboratory data integration but highlights the need for direct LIS integration and expanded LOINC coverage for legacy data.
PMID:40270418 | DOI:10.3233/SHTI250194
