ER-Reason: A Benchmark Dataset for LLM Clinical Reasoning in the Emergency Room

Paper: https://arxiv.org/abs/2505.22919 | Dataset: PhysioNet

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Overview

ER-Reason is a benchmark for evaluating large language models (LLMs) on clinical reasoning across key stages of the emergency room (ER) workflow. Unlike benchmarks based on medical licensing exams, ER-Reason evaluates not just what decisions models make, but how their reasoning evolves as clinical evidence accumulates.

ER-Reason consists of two components:

• Longitudinal clinical notes from 3,984 hospital encounters comprising 25,174 de-identified clinical notes across discharge summaries, progress notes, H&Ps, consult notes, imaging reports, and ER provider notes — supporting evaluation across triage intake, disposition planning, and final diagnosis.
• SCT reasoning evaluation comprising 194 physician-authored patient cases annotated by two ER physicians (2,555 total annotations), with three metrics — DxUpdate, DxTrajectory, and FinalDx — that measure sequential belief updating against physician consensus.

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Dataset Access

The ER-Reason dataset is hosted on PhysioNet and requires credentialed access:

1. Register at physionet.org and complete CITI training.
2. Request access at: https://physionet.org/content/er-reason/1.0.0/
3. Once approved, download the dataset files.

Note: The dataset contains de-identified patient data and is governed by a PhysioNet data use agreement. Do not share or redistribute.

Key dataset files

File	Description	Key columns
er_reason.csv	Main dataset — one row per encounter, includes all clinical note text, demographic fields, acuity level, disposition, and primary ED diagnosis	patientdurablekey, encounterkey, primarychiefcomplaintname, primaryeddiagnosisname, acuitylevel, eddisposition, ED_Provider_Notes_Text, Discharge_Summary_Text, One_Sentence_Extracted
icd_10_codes.csv	Ground-truth ICD-10 codes per encounter — one row per code, multiple rows per encounter	patientdurablekey, encounterkey, value (ICD-10 code), displaystring (diagnosis name)
annotated_sct.csv	SCT evaluation cases derived from er_reason.csv — one row per encounter, includes the one-sentence patient summary and up to 5 differential/evidence pairs per case	encounterkey, One_Sentence_Extracted, differential_1–differential_5, evidence_1–evidence_5
sct-annotations.csv	Master list of the 194 SCT case encounterkeys — used to filter er_reason.csv to SCT encounters	encounterkey
sct_cleaned_annotations.csv	Physician rationales for each differential/evidence step — one row per (encounter, differential) pair	encounterkey, differential, rationale
gt_clean.csv	Ground-truth physician consensus scores for SCT evaluation — one row per (encounter, differential) pair	encounterkey, differential, dxupdate (ordinal score −2 to +2), dxtrajectory (ranked dict of all differentials)

The CCSR reference file used for diagnosis evaluation must be downloaded separately from AHRQ: https://hcup-us.ahrq.gov/toolssoftware/ccsr/dxccsr.jsp

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Repository Structure

ER-Reason/
├── Experiments/
│   ├── Standard Clinical Tasks/
│   │   ├── acuity.py               # Acuity prediction (zero-shot + step-back)
│   │   ├── disposition.py          # Disposition prediction (zero-shot + step-back)
│   │   ├── final_diagnosis.py      # Final diagnosis prediction (zero-shot + step-back)
│   │   ├── diag_evaluation.py      # ICD-10 exact match + CCSR accuracy
│   │   └── cross_stage_analysis.py # Cross-stage workflow accuracy (Table 5)
│   └── SCT Reasoning/
│       ├── clinical_knowledge.py   # Clinical knowledge baseline (Table 3 CK column)
│       ├── sct.py                  # SCT evaluation — baseline, single oracle, full oracle
│       └── sct_eval.py             # DxUpdate, DxTrajectory, FinalDx, coherence, Figure 3
├── ER-Reason-V1-Archive/           # Original codebase (archived)
├── ER-Reason_Column_Descriptions.md
├── README.md
└── requirements.txt

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Setup

1. Clone the repository

git clone https://github.com/AlaaLab/ER-Reason.git
cd ER-Reason

2. Install dependencies

pip install -r requirements.txt

3. Set your OpenRouter API key

All experiment scripts use OpenRouter, so you can swap in any supported model with a single line change.

export OPENROUTER_API_KEY="sk-or-..."

All scripts also enable Zero Data Retention ("provider": {"zdr": True}) by default, routing requests only to ZDR-compliant providers.

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Running Experiments

Swapping models

Each script has a MODEL_NAME variable at the top. Replace it with any OpenRouter model string:

MODEL_NAME = "openai/gpt-5.2-20251211"
# MODEL_NAME = "openai/o4-mini"          # remove temperature parameter for this model
# MODEL_NAME = "deepseek/deepseek-r1"
# MODEL_NAME = "google/gemini-2.5-flash"
# MODEL_NAME = "anthropic/claude-sonnet-4-5"
# MODEL_NAME = "microsoft/phi-4"

To enable Claude thinking mode, add to the API call:

extra_body={"thinking": {"type": "enabled", "budget_tokens": 10000}}

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Standard Clinical Tasks

All three scripts run zero-shot and step-back conditions in a single pass, saving results to a CSV with a condition column.

Acuity prediction

python Experiments/Standard\ Clinical\ Tasks/acuity.py
# Output: acuity_results.csv

Disposition prediction

python Experiments/Standard\ Clinical\ Tasks/disposition.py
# Output: disposition_results.csv

Final diagnosis prediction

python Experiments/Standard\ Clinical\ Tasks/final_diagnosis.py
# Output: diagnosis_results.csv

Diagnosis evaluation (ICD-10 exact match + CCSR accuracy)

python Experiments/Standard\ Clinical\ Tasks/diag_evaluation.py
# Reads: diagnosis_results.csv, icd_10_codes.csv, DXCCSR_v2025-1.CSV

Cross-stage workflow accuracy (Table 5)

python Experiments/Standard\ Clinical\ Tasks/cross_stage_analysis.py
# Reads: acuity_results.csv, disposition_results.csv, diagnosis_results.csv,
#        icd_10_codes.csv, DXCCSR_v2025-1.CSV

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SCT Reasoning

Clinical knowledge baseline

python Experiments/SCT\ Reasoning/clinical_knowledge.py
# Output: clinical_knowledge_results.csv

SCT evaluation — runs all three conditions (baseline, single oracle, full oracle)

python Experiments/SCT\ Reasoning/sct.py
# Output: sct_results.csv
#         sct_baseline_checkpoint.csv
#         sct_single_oracle_checkpoint.csv
#         sct_full_oracle_checkpoint.csv

Each condition checkpoints independently — if interrupted, re-running will resume from where it left off.

SCT metrics and figures (Tables 2, 3, 4 and Figure 3)

python Experiments/SCT\ Reasoning/sct_eval.py
# Reads: sct_results.csv, gt_clean.csv
# Output: top1_by_timestep.pdf, top1_by_timestep.png

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Citation

If you use ER-Reason in your research, please cite:

@inproceedings{@article{mehandru2025er,
  title={Er-reason: A benchmark dataset for llm-based clinical reasoning in the emergency room},
  author={Mehandru, Nikita and Golchini, Niloufar and Bamman, David and Zack, Travis and Molina, Melanie F and Alaa, Ahmed},
  journal={arXiv preprint arXiv:2505.22919},
  year={2025}
}

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License

The code in this repository is released under the MIT License. The dataset is governed by the PhysioNet Data Use Agreement and may not be redistributed.