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    <abstract>Accurate diagnosis depends not only on clinical expertise but also on how patients describe their symptoms at first contact. Yet large English corpora of patient-authored self-reports are scarce, limiting advances in natural, context-aware narrative modeling. We address this gap by generating first-person self-reports from structured EHR content conditioned on persona attributes that capture social and clinical context. Reports are produced by two generators and scored by two independent graders using a rubric with four dimensions, complemented by a rubric-free preference test. Across 10k stratified cases, we compare two generators under a reliable evaluation protocol and select the higher-scoring one based primarily on Clinical Correctness and Faithfulness, yielding a dataset composed of narratives from the stronger system. Our contributions are threefold: (I) we developed and release a large, persona-conditioned dataset of patient-style self-reports grounded in patient-stated EHR facts, (II) we introduce a transparent evaluation framework that combines rubric-based scoring with rubric-free preference to mitigate grader bias and enable cross-validation, (III) we find that graders exhibit systematic stylistic preferences in rubric-free approach that influence scores independent of clinical content, and (IV) we study large language models for producing first-person self-reports from structured EHRs, highlighting where they succeed, where they fail, and how this affects use in telemedicine and triage.</abstract>
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%0 Conference Proceedings
%T Persona-Conditioned Generation of Patient Self-Reports from EHRs
%A Wu, Yuexin
%A Wei, Jianming
%A Rus, Vasile
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F wu-etal-2026-persona
%X Accurate diagnosis depends not only on clinical expertise but also on how patients describe their symptoms at first contact. Yet large English corpora of patient-authored self-reports are scarce, limiting advances in natural, context-aware narrative modeling. We address this gap by generating first-person self-reports from structured EHR content conditioned on persona attributes that capture social and clinical context. Reports are produced by two generators and scored by two independent graders using a rubric with four dimensions, complemented by a rubric-free preference test. Across 10k stratified cases, we compare two generators under a reliable evaluation protocol and select the higher-scoring one based primarily on Clinical Correctness and Faithfulness, yielding a dataset composed of narratives from the stronger system. Our contributions are threefold: (I) we developed and release a large, persona-conditioned dataset of patient-style self-reports grounded in patient-stated EHR facts, (II) we introduce a transparent evaluation framework that combines rubric-based scoring with rubric-free preference to mitigate grader bias and enable cross-validation, (III) we find that graders exhibit systematic stylistic preferences in rubric-free approach that influence scores independent of clinical content, and (IV) we study large language models for producing first-person self-reports from structured EHRs, highlighting where they succeed, where they fail, and how this affects use in telemedicine and triage.
%R 10.63317/34prj6o9qfrk
%U https://aclanthology.org/2026.lrec-1.845/
%U https://doi.org/10.63317/34prj6o9qfrk
%P 10792-10801

Markdown (Informal)

[Persona-Conditioned Generation of Patient Self-Reports from EHRs](https://aclanthology.org/2026.lrec-1.845/) (Wu et al., LREC 2026)

• Persona-Conditioned Generation of Patient Self-Reports from EHRs (Wu et al., LREC 2026)

ACL

• Yuexin Wu, Jianming Wei, and Vasile Rus. 2026. Persona-Conditioned Generation of Patient Self-Reports from EHRs. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10792–10801, Palma de Mallorca, Spain. ELRA Language Resource Association.