Background: Large language models (LLMs) contain limited professional medical knowledge, as large-scale training on clinical text has not yet been possible due to restricted access. Objectives: To continue pre-training an open-access instruct LLM on de-identified medical notes and evaluate the resulting impact on real-world clinical decision-making tasks and standard benchmarks. Methods: Using 500K de-identified clinical notes from Cedars-Sinai Health System, we fine-tuned a Qwen3-4B Instruct model with supervised learning to generate medical decision-making (MDM) paragraphs from patient presentations, and evaluated it on assigned-diagnosis prediction, in-hospital cardiac-arrest mention detection, and a suite of general and biomedical benchmarks. Results: The fine-tuned model produced MDMs that closely resembled those written by physicians and outperformed the base-instruct model and larger clinically untrained models (Qwen3-32B and Llama-3.1-405B Instruct) on assigned-diagnosis prediction, the task most aligned with its training objective. On the task of detecting in-hospital cardiac arrest mentions, the model initially exhibited mild label collapse, but a brief task-specific fine-tuning stage resolved this issue and allowed it to surpass all competitors. The model also demonstrated global general knowledge retention on biomedical and general-domain evaluation benchmarks compared to the baseline. Conclusion: Supervised full fine-tuning on clinical notes allowed the model to incorporate medical knowledge without sacrificing general-domain abilities, and to transfer this knowledge to unseen biomedical tasks without wholesale loss of general-domain abilities, while revealing collapse-related failure modes that motivate more principled strategies for clinical specialization.

Dr. Campbell has received research funding from Google Cloud unrelated to the work presented here; the funder had no influence on the current study. The authors declare no other competing interests.

The research reported in this publication was supported by a Centers for Disease Control and Prevention (CDC) Cooperative Agreement Funding Opportunity Announcement (FOA) U54 CK000610, Epicenters for the Prevention of Healthcare-Associated Infections. Dr. Berdahl receives salary support from the Agency for Healthcare Research and Quality under K08HS029534, and he is also supported by the Emergency Medicine Foundation award on Diagnostic Excellence.

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This study was conducted using de-identified EHR data and was reviewed by the Cedars-Sinai Medical Center Institutional Review Board, which determined that it met criteria for exemption from human subjects research oversight.

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