Towards Clinical NLP in a Low-Resource Language: Structuring Estonian Clinical Text

Healthcare sectors generate large volumes of unstructured text, yet mature clinical NLP is lacking for low-resource languages. This project targets Estonian, a morphologically rich, low-resource language, and outlines an end-to-end pipeline that converts free text into interoperable, structured data. A design-science approach guides development of multilingual speech-to-text for clinical dictation, clinical NLP for entity extraction and context handling, terminology binding to the Estonian SNOMED CT extension, and export as HL7 FHIR resources with mappings to EHR fields. Expected benefits include reduced documentation time through dictation with automatic structuring, improved data quality and consistency via controlled terminology, and faster retrieval of clinically relevant information to support decision-making. Pilot evaluations with healthcare partners will assess accuracy, usability, and workflow impact.