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

Adam Rodman; Ahmed Alaa; Anu Ramachandran; Christopher J. Nash; David Bamman; Kathy T. LeSaint; Liam G. McCoy; Melanie Molina; Namrata Garg; Nikita Mehandru

arxiv: 2505.22919 · v3 · submitted 2025-05-28 · 💻 cs.CL

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

Nikita Mehandru , Niloufar Golchini , Namrata Garg , Kathy T. LeSaint , Christopher J. Nash , Anu Ramachandran , Travis Zack , Liam G. McCoy

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Adam Rodman David Bamman Melanie Molina Ahmed Alaa

This is my paper

Pith reviewed 2026-05-19 12:03 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM clinical reasoningemergency department workflowbenchmark datasetScript Concordance Testdiagnostic belief updatingreal patient casesevidence accumulation

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The pith

ER-Reason tests whether LLMs update diagnostic beliefs correctly in direction and magnitude as real clinical evidence accumulates across emergency workflows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper seeks to move beyond stylized medical exam questions by building a benchmark that follows actual patient cases from triage through final diagnosis. It collects de-identified notes from thousands of emergency visits and creates stepwise questions that mirror how doctors adjust their thinking with each new piece of information. These questions are scored against annotations from emergency physicians to measure if models shift their diagnostic probabilities in the right way and by the right amount. A sympathetic reader would care because prior tests have shown large gaps between model scores on fake cases and results in live clinical use. If the new approach works, it could expose exactly where reasoning breaks down in realistic, accumulating-evidence settings rather than just final-answer accuracy.

Core claim

ER-Reason comprises 25,174 de-identified clinical notes from 3,437 patients and supports evaluation across triage intake, treatment selection, disposition planning, and final diagnosis. Evaluation extends beyond diagnostic accuracy to stepwise Script Concordance Test-style questions grounded in real patient cases that assess whether LLMs update their diagnostic beliefs in the correct direction and magnitude as clinical evidence accumulates, scored against 2,555 emergency physician annotations. The paper shows that these tasks provide a more nuanced view of how LLM reasoning fails on real patient cases than existing benchmarks allow.

What carries the argument

Stepwise Script Concordance Test-style questions that check whether models update diagnostic beliefs in the correct direction and magnitude as evidence from real de-identified clinical notes accumulates across the full emergency workflow.

If this is right

LLM evaluation can now span the full sequence of interdependent decisions rather than isolated final diagnoses.
Differences in how reasoning and non-reasoning models handle accumulating evidence become measurable at each workflow stage.
Specific failure points, such as incorrect belief updates after new test results, can be isolated for targeted improvement.
Benchmarks can move closer to matching the conditions of prospective real-world studies where information arrives incrementally.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same stepwise belief-update format could be applied to other time-sensitive medical domains such as intensive care or surgery.
Training loops for clinical models might incorporate similar scored belief-update exercises drawn from real notes to reduce the benchmark-to-real-world gap.
Regulators or hospital systems could require models to pass thresholds on such accumulating-evidence tests before deployment in emergency settings.
Longitudinal tracking of the same cases could test whether better stepwise scores predict improved patient outcomes in follow-up data.

Load-bearing premise

The de-identified clinical notes fully capture the interdependent tasks and evidence accumulation of real emergency workflows, and the physician annotations accurately represent the correct direction and magnitude of diagnostic belief updates.

What would settle it

A direct comparison in which LLMs show the same pattern of failures and successes on ER-Reason as they do on existing stylized vignette benchmarks, or in which model scores on the stepwise questions fail to track actual physician performance on the same cases.

read the original abstract

Existing benchmarks for evaluating the clinical reasoning capabilities of large language models (LLMs) often lack a clear definition of "clinical reasoning" as a construct, fail to capture the full breadth of interdependent tasks within a clinical workflow, and rely on stylized vignettes rather than real-world clinical documentation. As a result, recent studies have found significant discrepancies between LLM performance on stylized benchmarks derived from medical licensing exams and their performance in real-world prospective studies. To address these limitations, we introduce ER-Reason, a benchmark designed to evaluate LLM reasoning as clinical evidence accumulates across decision-making tasks spanning the full workflow of emergency medicine. ER-Reason comprises 25,174 de-identified clinical notes from 3,437 patients, supporting evaluation across all stages of the emergency department workflow: triage intake, treatment selection, disposition planning, and final diagnosis. Crucially, evaluation in ER-Reason extends beyond diagnostic accuracy to include stepwise Script Concordance Test (SCT)-style questions grounded in real patient cases, which assess whether LLMs update their diagnostic beliefs in the correct direction and magnitude as clinical evidence accumulates, scored against 2,555 emergency physician annotations. We evaluate reasoning and non-reasoning LLMs on ER-Reason, and show that our tasks provide a more nuanced view of how LLM reasoning fails on real patient cases than existing benchmarks allow.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ER-Reason adds real ER notes and stepwise SCT scoring to LLM tests, but the abstract leaves the physician annotations unvalidated.

read the letter

The main point is that this paper introduces a benchmark using thousands of real de-identified emergency department notes instead of the usual stylized cases, and it scores LLMs on whether they update diagnostic beliefs correctly as evidence comes in. The scale covers 25,174 notes from 3,437 patients across the full workflow from triage intake through treatment, disposition, and final diagnosis. The use of 2,555 emergency physician annotations for Script Concordance Test style questions is the part that tries to measure reasoning rather than just end-point accuracy. That setup directly targets the gap the abstract notes between exam-style benchmark scores and actual clinical performance, and it is new relative to the prior work referenced. The idea of tracking belief updates in direction and magnitude on real cases is a reasonable way to make evaluation more granular than simple accuracy metrics. The paper does well by grounding the tasks in actual patient documentation and by including both reasoning and non-reasoning models in the evaluation plan. This gives a clearer picture of where models break down in interdependent clinical tasks. The soft spot is the complete lack of information on how the annotations were produced. The abstract mentions the number of physician scores but supplies no protocol for case presentation, no inter-rater reliability numbers, and no check against objective outcomes or other standards. Without those details it is impossible to know whether the scoring truly reflects correct clinical judgment or simply averages of opinion. Since only the abstract is available, the methods and any results on the LLMs themselves cannot be examined either. This paper is aimed at researchers who build or evaluate medical LLMs and who are already aware of the limitations of vignette-based tests. A reader focused on clinical reasoning benchmarks would get value from the workflow coverage and the real-note approach even before seeing full results. It deserves peer review because the core idea addresses a documented problem with current evaluation standards and the dataset scale is large enough to be worth checking. Referees can ask for the missing validation steps on the annotations and for the actual LLM performance numbers.

Referee Report

1 major / 1 minor

Summary. The paper introduces ER-Reason, a benchmark dataset of 25,174 de-identified clinical notes from 3,437 patients designed to evaluate LLM clinical reasoning across the full emergency department workflow (triage intake, treatment selection, disposition planning, and final diagnosis). It extends beyond diagnostic accuracy by using stepwise Script Concordance Test (SCT)-style questions grounded in real patient cases to assess whether LLMs update diagnostic beliefs in the correct direction and magnitude, with scores derived from 2,555 emergency physician annotations. The central claim is that this approach yields a more nuanced view of LLM reasoning failures on real cases than existing stylized benchmarks.

Significance. If the physician annotations prove reliable and the de-identified notes faithfully capture evidence accumulation in real ED workflows, ER-Reason could provide a valuable, ecologically valid benchmark that better explains discrepancies between LLM performance on licensing-exam-style tests and prospective clinical studies, thereby supporting more targeted improvements in LLM reasoning for high-stakes medical decision-making.

major comments (1)

[Abstract] Abstract, description of ER-Reason evaluation: the central claim that the benchmark assesses correct direction and magnitude of diagnostic belief updates rests on the validity of the 2,555 emergency physician annotations, yet the abstract supplies no protocol for case presentation, elicitation of belief updates, inter-rater reliability statistics, or grounding against objective clinical standards or outcomes.

minor comments (1)

[Abstract] The abstract refers to evaluation of 'reasoning and non-reasoning LLMs' without naming the specific models or providing any quantitative results, which would help readers assess the claimed nuanced view of failures.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the single major comment below and indicate where revisions will be made.

read point-by-point responses

Referee: [Abstract] Abstract, description of ER-Reason evaluation: the central claim that the benchmark assesses correct direction and magnitude of diagnostic belief updates rests on the validity of the 2,555 emergency physician annotations, yet the abstract supplies no protocol for case presentation, elicitation of belief updates, inter-rater reliability statistics, or grounding against objective clinical standards or outcomes.

Authors: We agree that the abstract, constrained by length, does not describe the annotation protocol, elicitation method, inter-rater reliability, or grounding against outcomes. The full manuscript contains a Methods section that details physician case presentation, the stepwise SCT question format for belief updates, inter-rater agreement statistics, and linkage to available clinical outcomes. To address the concern directly in the abstract, we will add a concise sentence summarizing the annotation process and reliability. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark grounded in external notes and independent annotations

full rationale

The paper presents ER-Reason as a new benchmark dataset drawn from 25,174 de-identified real patient notes across 3,437 cases, with SCT-style questions scored against 2,555 separate emergency physician annotations. No equations, fitted parameters, self-citations, or derivation steps appear in the abstract that would reduce any claim or score to the paper's own inputs by construction. The central evaluation of LLM belief updates is defined externally via physician judgments on real workflows rather than through self-referential definitions or renamed known results, leaving the framework self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of real clinical notes as proxies for workflow evidence and on physician annotations as ground truth for correct belief updates; no free parameters or invented entities are introduced.

axioms (1)

domain assumption Physician annotations on diagnostic belief updates serve as accurate ground truth for scoring LLM reasoning.
Scoring of SCT-style questions relies directly on the 2,555 emergency physician annotations as the reference standard.

pith-pipeline@v0.9.0 · 5784 in / 1224 out tokens · 63324 ms · 2026-05-19T12:03:06.456498+00:00 · methodology

discussion (0)

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