---
title: "Experience Retrieval-Augmentation with Electronic Health"
description: "They propose ExpRAG, which draws on discharge reports from the MIMIC-IV EHR database in two… 51 references, cited by 4 papers here."
authors: ["Justice Ou", "Tinglin Huang", "Yilun Zhao", "Ziyang Yu", "Peiqing Lu", "Yifei Shen", "Rex Ying"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1073/"
doi: "10.18653/v1/2026.acl-long.1073"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ou-2026-experience-retrieval-augmentation-electronic-health/"
---
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# Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

**Authors:** [Justice Ou](https://md.research.elamur.ai/free/en/authors/justice-ou/), [Tinglin Huang](https://md.research.elamur.ai/free/en/authors/tinglin-huang/), [Yilun Zhao](https://md.research.elamur.ai/free/en/authors/yilun-zhao/), Ziyang Yu, Peiqing Lu, [Yifei Shen](https://md.research.elamur.ai/free/en/authors/yifei-shen/) and [Rex Ying](https://md.research.elamur.ai/free/en/authors/rex-ying/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1073/) · [PDF](https://aclanthology.org/2026.acl-long.1073.pdf) · [DOI 10.18653/v1/2026.acl-long.1073](https://doi.org/10.18653/v1/2026.acl-long.1073) · [OpenAlex](https://openalex.org/W7166857309)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/ou-2026-experience-retrieval-augmentation-electronic-health/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

LLMs used for clinical question answering often hallucinate. Standard RAG mostly supplies generic medical facts, such as a drug's usual dose, which ignore the combination of conditions a real patient has. The authors argue that models, like experienced clinicians, benefit from looking at comparable past cases. They propose ExpRAG, which draws on discharge reports from the MIMIC-IV EHR database in two stages. First, a ranker shortlists similar patients by comparing structured EHR codes for diagnoses, medications and procedures. Then a conventional text retriever pulls query-relevant passages from those patients' reports and passes them to the LLM. To evaluate this and other retrieval systems, the authors build DischargeQA, a multiple-choice benchmark covering final diagnosis, medication prescription and discharge instructions. Its answer options are generated from EHR data, so the wrong choices are plausible and the questions are harder. Across five LLM backbones, ExpRAG outperforms text-based report ranking. This suggests that case-based context from structured records usefully complements factual knowledge retrieval.

## Key points

- **DischargeQA** contains 1,280 questions in three task types: diagnosis, medication and instruction. To avoid label leakage, only the report content that precedes the answer is used as background. The dataset is released via PhysioNet, and the code is on GitHub (jou2024/EXPRAG).
- **Report ranking** measures patient similarity with ICD-10 diagnosis codes, NDC medication codes and procedure records. The three components are combined with balancing coefficients λ1/2/3, and uniform weighting worked best in most settings.
- **Passage extraction** in the second stage uses existing text retrievers such as BM25 or auto-merging.
- **Main result:** ExpRAG achieves an average relative improvement of 5.2% over text-based rankers. When compared against GPT-4o-mini similarity annotations, the EHR ranker reached Pearson 0.669 and Spearman 0.648. The best text-embedding baseline reached 0.640 and 0.623.
- **Limitations:**
  - The evaluation uses a single MIMIC-IV cohort, so cross-institution generalization is untested.
  - Only diagnoses, medications and procedures are used; lab results are not.
  - All questions are multiple-choice rather than open-ended.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Justice Ou, Tinglin Huang, Yilun Zhao, Ziyang Yu, Peiqing Lu, Yifei Shen and Rex Ying. 2026. Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ou2026experience,
  title = {{Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA}},
  author = {Justice Ou, Tinglin Huang, Yilun Zhao, Ziyang Yu, Peiqing Lu, Yifei Shen and Rex Ying},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1073},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1073}
}
```

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*Updated 31 October 2026*