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HFEPX · Eval paper review

sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Notebook

Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa, Florian Matthes

Published

Mar 14, 2026

Citations

0

Trust level

Low

Usefulness score

40/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Domain Experts

Signals refreshed

Mar 28, 2026

Open arXiv Find implementation Shortlist (0) RSS feed

Tags

Coding Expert Verification Medicine

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

Background context only.

What to verify

Read the full paper before copying any benchmark, metric, or protocol choices.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
40/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently. However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements. In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook. We participate in all four subtasks of the ArchEHR-QA 2026 shared task and evaluate several approaches designed to run on commodity hardware. All experiments are conducted locally without external APIs or cloud infrastructure. Our results show that such systems can achieve competitive performance on the shared task leaderboards. In particular, our submissions perform above average in two subtasks, and we observe that smaller models can approach the performance of much larger systems when properly configured. These findings suggest that privacy-preserving EHR QA systems running fully locally are feasible with current models and commodity hardware. The source code is available at https://github.com/ibrahimey/ArchEHR-QA-2026.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

partial

Expert Verification

Directly usable for protocol triage.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Rater Population

partial

Domain Experts

Helpful for staffing comparability.

"Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

No metric terms were extracted from the available abstract.

Human feedback details
Uses human feedback
Yes
Feedback types
Expert Verification
Rater population
Domain Experts
Expertise required
Medicine, Coding
Evaluation details
Evaluation modes
None
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently.
  • However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements.
  • In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Contribution summary

  • Clinical question answering over electronic health records (EHRs) can help clinicians and patients access relevant medical information more efficiently.
  • However, many recent approaches rely on large cloud-based models, which are difficult to deploy in clinical environments due to privacy constraints and computational requirements.
  • In this work, we investigate how far grounded EHR question answering can be pushed when restricted to a single notebook.

Researcher checklist

  • Human feedback protocol is explicit

    Detected: Expert Verification

  • Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    No metric terms extracted.