Can an LLM Choose the Right Flow Panel?
In a 100-case study, an EHR-integrated LLM matched staff performance for extracting prior diagnoses but performed worse at direct panel selection. The distinction is central to clinical implementation.
By Juan Manuel Ojeda · CFCM
PRIMARY EVIDENCE · PEER-REVIEWED · CORRECTION
Primary evidence
Rojansky R, Keyes T, Oak J. Automating clinical history extraction for flow cytometry panel selection using an EHR-integrated large language model. Cytometry Part B. Published 10 July 2026. DOI: 10.1002/cyto.b.70051.
What it actually demonstrates
This study evaluated an EHR-integrated LLM across 100 cases to automate clinical-history extraction and support panel selection against a standardised institutional decision-tree workflow. It is more precise to describe this as LLM-assisted clinical-context extraction and panel-selection support, rather than an unconstrained LLM independently designing a flow panel.
Evidence boundary
Editorial correction: CFCM will use the narrower description above when interpreting this study. The distinction matters because deterministic institutional rules remain part of the workflow.
Primary record →Primary evidence — Rojansky, Keyes and Oak evaluated ChatEHR in 100 cases against a hematopathologist-reviewed reference. Clinical-history classification accuracy was 78% for both ChatEHR and clinical laboratory scientists; direct panel-selection accuracy was 47% versus 78%.
What it actually demonstrates — Average processing time was 20.3 seconds with ChatEHR versus 41 seconds manually. Faster extraction did not establish reliable execution of institutional panel-selection rules. The reported annual time saving is an estimate, not a measured annual outcome.
Evidence boundary — This single-institution evaluation concerns clinical context and selection among established panels. It does not validate fluorochrome assignment, de novo panel design, generalisation to other hospitals, or autonomous clinical use.
CFCM view
The important question is not whether the technology or marker is interesting. It is whether it changes a validated clinical workflow in a measurable, reproducible and explainable way.
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