Seizure freedom is a key clinical outcome for people with epilepsy (PWE) yet it is primarily recorded in free-text notes and letters in the United Kingdom, making it difficult to aggregate and track at scale. This paper introduces a generative LLM-based pipeline boosted by synthetic data to identify a PWE’s seizure freedom status in clinicians’ records. We fine-tuned seven different LLMs with between 4-14 billion parameters using LoRA to compare models trained on synthetic records against those trained on expert annotated records. The best performing configuration, based on Qwen-2.5-14B, was trained entirely on synthetic records and used chain-of-thought (CoT) reasoning (both generated by GPT-5). This achieved an F1 score of 0.90±0.02 on double-annotated test data and outperformed the equivalent model trained on authentic clinician records, which achieved 0.87±0.04. The synthetically trained models also have the benefit of outputting their CoT reasoning process for greater decision-making transparency and can also make use of the unused supervised training data for significantly increased test examples. This work has implications for monitoring a key treatment outcome for PWE automatically and at scale.