ArticleBMJ health & care informatics2026
Open-source large language model-based on-premises pipeline for automated data extraction from unstructured electronic health records: a pilot study.
Article in BMJ health & care informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesWe evaluated an on-premises, open-source large language model (LLM)-based data extraction pipeline for automated data extraction from unstructured electronic health records (EHRs).
methodsAutomated script-based EHR data preprocessing extracted 50 medical texts in German, which were entered into the LLM pipeline. 4-bit and 8-bit quantizations of 14 mid-sized LLMs (30B-90B parameters) were evaluated in 6 information extraction, 11 binary classification and 5 multilevel classification tasks comprising all variables of the European System for Cardiac Operative Risk Evaluation 2 (EuroSCORE 2) model for 1100 predictions each. LLM response consistency was assessed over three same-prompt iterations.
resultsIn overall accuracy, Qwen3-30b-a3b-q8 presented the highest value (0.954) and 13 LLMs had values over 0.90. In information extraction accuracy, 12 LLMs exhibited a value of 1.0 and all 14 LLMs had values over 0.96. In binary classification accuracy, Llama3.2-vision-90b-q4 exhibited the highest value (0.972), 5 LLMs had values of at least 0.95 and all 14 LLMs showed values over 0.93. In multilevel classification accuracy, Qwen3-30b-a3b-q8 exhibited the highest value (0.940), four LLMs had values over 0.90 and all LLMs presented values over 0.80. Nine LLMs exhibited perfect response consistency and the remaining five LLMs had a Krippendorff's alpha value of 0.999. DISCUSSION: Multiple LLMs exhibited high accuracy in information extraction, binary classification, multilevel classification and response consistency and seem able to reliably automate data extraction from EHRs.
conclusionThis pilot study demonstrates the feasibility of on-premises, privacy-preserving, LLM-based automated EHR data extraction pipelines. Larger-scope studies are warranted to validate their potential in healthcare.
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