LLMs Extract Drug Discontinuations From Estonian EHRs

Per a JMIR preprint by Suvalov et al., the authors merged prescription data with free-text clinical anamneses from a 10% sample of the Estonian population covering 2012-2019. The study targeted discontinuations for statins and antidiabetic medications and applied two large language models, Llama-3.1-70B and GPT-4o, to:

• •extract discontinuation phrases
• •classify reasons using a clinician-developed taxonomy
• •identify whether the patient or clinician initiated the discontinuation. Performance was measured on 100 randomly chosen cases per drug group, as reported in the preprint

The preprint documents using Llama-3.1-70B and GPT-4o for information extraction and classification from Estonian-language clinical notes. The authors developed a taxonomy of discontinuation reasons with clinician input and applied the models to link free-text evidence to structured prescription records. The manuscript presents validation on a held-out sample; exact performance metrics are reported in the preprint.

Applying LLMs to extract clinically relevant events from free text addresses a long-standing barrier in pharmacoepidemiology: important discontinuation rationale is frequently recorded only in narrative notes. Systems that successfully pair prescriptions with extracted reasons can enable higher-fidelity signal detection for side effects, inefficacy, or access barriers. A concurrent Harvard / Brigham and Women's Hospital preprint (arXiv 2506.11137) covers the same problem on English EHR datasets, demonstrating that LLM-based medication status extraction scales without human annotation - reinforcing the broader applicability of this approach.

Observers should watch for the peer-reviewed final JMIR publication for full performance metrics and error analysis, replication on other languages or EHR systems, and whether the authors publish the taxonomy, annotation guidelines, or evaluation code to enable reproducibility. External replication and transparent error breakdowns (false positives versus false negatives, initiator misclassification) will determine practical utility for downstream clinical research.