Search papers, labs, and topics across Lattice.
This paper explores the feasibility of local, privacy-preserving clinical question answering (QA) over electronic health records (EHRs) using commodity hardware. The authors participated in the ArchEHR-QA 2026 shared task, evaluating various approaches for grounded EHR QA within the constraints of a single notebook environment, without relying on cloud-based models or external APIs. Their results demonstrate competitive performance compared to cloud-based systems, achieving above-average results in two subtasks and showing that smaller, properly configured models can approach the performance of larger systems.
You don't need a cloud to ask EHRs questions: surprisingly competitive clinical question answering is possible with commodity hardware and local models.
Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.
