HealthNLP_Retrievers at ArchEHR-QA 2026: Cascaded LLM Pipeline for Grounded Clinical Question Answering

Problem
This paper addresses the challenge of grounded clinical question answering (QA) over electronic health records (EHRs) in the context of the ArchEHR-QA 2026 shared task. The authors highlight a gap in existing literature regarding the effective interpretation of patient-authored questions and the retrieval of relevant clinical information, which is crucial for enhancing patient understanding of their health data. The work is presented as a preprint and has not yet undergone peer review.

Method
The proposed system employs a multi-stage cascaded pipeline utilizing the Gemini 2.5 Pro large language model (LLM). The architecture consists of four integrated modules:

1. Few-shot Query Reformulation: This module summarizes verbose patient queries to enhance interpretability.
2. Heuristic-based Evidence Scorer: It ranks clinical sentences based on heuristics to maximize recall of relevant information.
3. Grounded Response Generator: This component synthesizes answers strictly based on the retrieved evidence, ensuring professional quality.
4. High-Precision Many-to-Many Alignment Framework: This module links generated answers to the supporting clinical sentences, enhancing the grounding of responses.

The training compute specifics are not disclosed, but the architecture’s design emphasizes structured processing to improve the quality of patient-oriented health communication.

Results
The HealthNLP_Retrievers system achieved notable rankings across various tracks in the shared task: 1st in question interpretation, 5th in answer generation, 7th in evidence identification, and 9th in answer-evidence alignment. These results indicate a significant improvement in grounding and precision compared to baseline models, although specific numerical performance metrics (e.g., F1 scores, accuracy) are not provided in the abstract.

Limitations
The authors acknowledge that their approach may be limited by the inherent complexity of clinical language and the potential for ambiguity in patient queries. Additionally, the reliance on a single LLM (Gemini 2.5 Pro) may restrict generalizability across diverse clinical contexts. The paper does not discuss the computational efficiency of the pipeline or the scalability of the approach in real-world applications.

Why it matters
This work has significant implications for the development of patient-centric health technologies, particularly in enhancing the accessibility and comprehensibility of EHRs. By integrating LLMs within a structured pipeline, the authors demonstrate a viable approach to improving the quality of health communication, which could lead to better patient engagement and outcomes. Future research could build on this framework to explore more robust models or alternative architectures that further enhance the interpretability and usability of clinical information for patients.

Authors: Md Biplob Hosen, Md Alomgeer Hussein, Md Akmol Masud, Omar Faruque, Tera L Reynolds, Lujie Karen Chen
Source: arXiv:2604.26880
URL: https://arxiv.org/abs/2604.26880v1