EMRGuide-Zero: Synthesizing Electronic Medical Records via Dual-LLM Adversarial Guideline Induction from Samples

Electronic medical records (EMRs) are a valuable source for clinical research, yet privacy restrictions make them difficult to access and share. Synthetic EMRs offer a practical alternative, and recent methods steer large language models (LLMs) with manually summarized synthesis guidelines, often alongside real examples. Because the generator follows what these guidelines specify, fidelity hinges on them, yet manual summaries leave many context-specific requirements unstated and omit the conditions under which others apply. To address these limitations, we introduce EMRGuide-Zero, a dual-LLM adversarial framework that induces fine-grained synthesis guidelines from samples with zero manual guideline authoring. At each round, an Inducer LLM contrasts real and synthetic records to propose candidate guidelines, which are tested on broader samples to filter out local coincidences. Each guideline also specifies where it applies, allowing a Generator LLM to impose it only on the cases it concerns. The validated guidelines are incorporated into subsequent generation rounds, where new contrasts expose requirements that earlier rounds missed. Experiments across multiple EMR corpora and LLM backbones show that EMRGuide-Zero discovers fine-grained, clinically meaningful guidelines and improves fidelity and downstream utility while keeping privacy risk close to generation without direct use of real record text. These results establish guideline induction from samples as an effective alternative to manual authoring for synthesizing information-rich EMRs across clinical contexts.