Access to electronic health record (EHR) warehouses is limited by SQL expertise and complex clinical schemas. We present an open-source OMOP Common Data Model text-to-SQL benchmark (CDM v5.4) with a safety contract: output one executable SQL statement or the abstention token (<NO_SQL>) for unanswerable requests. Inputs are concept-normalized (entities as OMOP concept IDs) to decouple SQL generation from entity linking. We evaluate by executing predicted and reference queries on a synthetic OMOP PostgreSQL database, reporting Execution Accuracy (result equivalence) and a reliability score that rewards correct abstention and penalizes unsafe attempts. The dataset includes 6,690 paraphrases from 75 OMOP-adapted templates with leakage-resistant template/SQL-variation splits. LoRA-tuned Llama-3-8B-Instruct achieves 93.55% execution accuracy with improved abstention reliability, while schema-injected baselines fail the contract. We release the dataset, splits, database dump, and a reproducible evaluation pipeline to support reliable clinical analytics assistants.

Transformer-based Large Language Models (LLMs) have achieved remarkable success across various domains, including clinical language processing, where they enable state-of-the-art performance in numerous tasks. Like all deep learning models, LLMs are susceptible to inference attacks that exploit sensitive attributes seen during training. AnonCAT, a RoBERTa-based masked language model, has been fine-tuned to de-identify sensitive clinical textual data. The community has a responsibility to explore the privacy risks of these models. This work proposes an attack method to infer sensitive named entities used in the training of AnonCAT models. We perform three experiments; the privacy implications of generating multiple names, the impact of white-box and black-box on attack inference performance, and the privacy-enhancing effects of Differential Privacy (DP) when applied to AnonCAT. By providing real textual predictions and privacy leakage metrics, this research contributes to understanding and mitigating the potential risks associated with exposing LLMs in sensitive domains like healthcare.

We present our system for the ArchEHR shared task, which focuses on answering clinical and patient-facing questions grounded in real-world EHR data. Our core contribution is a 2-Stage prompting pipeline that separates evidence selection from answer generation while employing in-context learning strategies. Our experimentation leveraged the open-weight Gemma-v3 family of models, with our best submission using the Gemma-12B model securing 5th place overall on the unseen test set. Through systematic experimentation, we demonstrate the effectiveness of task decomposition in improving both factual accuracy and answer relevance in grounded clinical question answering.