Alizadeh, Javad Mohammad, Wu, Huanmei · Nov 2, 2026
Alizadeh and Wu benchmark THReD for preparing clinical records for AI
Markdown · JSON · Original source
Javad Mohammad Alizadeh and Huanmei Wu report a of THReD, a web-based system that uses a chat agent to turn electronic health record (EHR) extracts into AI-ready datasets. The agent’s proposed steps run only after a user confirms them. The authors test whether the agent completes data-preparation tasks and whether its tools produce correct, reproducible results. Across 141 tasks, the deployed agent completed 92.7% on its first answer, but completion fell to 58.0% without a data profile; requests requiring the agent to infer an overall goal were hardest. In separate tool experiments, the authors report high accuracy for inferred patient classes and vital-sign labels, successful mapping of raw variants, and byte-identical replays of saved pipelines under pinned dependencies. They also report that a same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points, while cohort composition shifted by no more than one point. Escape Meridian’s reading: the data profile and preprocessing choices are central to interpreting the benchmark. Task completion fell when the profile was removed, and one rule changed a measured health outcome. The authors tested synthetic tables and records from two chronic-disease cohorts; how well these results carry over to other settings remains open.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 2, 2026 (2026-11-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T09:53:10.448Z. First observed: 2026-11-03T09:04:11.492Z. Retrieved: 2026-11-04T04:46:51.434Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The agent used synthetic tables calibrated to a regional health information exchange; the abstract does not report testing the agent benchmark on real clinical tables. The reported tool experiments cover real records from two chronic-disease cohorts and ; the abstract does not establish how results generalize to other facilities or cohorts. The abstract reports summary results but does not provide the full task definitions or per-episode results; it says these are available on Zenodo.
Open question
How would THReD’s reported performance and the effects of its preprocessing choices hold up on clinical records from other facilities and cohorts?
Source-stated
Alizadeh and Wu describe THReD as a web-based system that turns EHR extracts into AI-ready datasets, with proposed steps requiring user confirmation.
a web-based system that turns electronic health record (EHR) extracts into AI-ready datasets through a chat agent whose proposed steps run only after user confirmation
Source-stated
The agent benchmark included 141 tasks across seven categories, evaluated on synthetic tables calibrated to a regional health information exchange.
The agent benchmark has 141 tasks (100 templates) in seven categories
Source-stated
Across 2,550 episodes, the deployed agent completed 92.7% of tasks on its first answer; without the data profile, completion fell to 58.0%.
Over 2,550 episodes with Claude Haiku 5.5 in four context configurations, the deployed agent completed 92.7% (95% CI 88.1-96.4) of tasks on its first answer
Source-stated
The authors report high accuracy in masked reconstruction for inferred patient classes and vital-sign labels, as well as correct mapping of raw variants.
91-92% of inferred patient classes and 99.7-99.8% of inferred vital-sign labels were correct in masked reconstruction; a dictionary builder combining curated dictionaries with LLM suggestions mapped 99.0% of 2,208 raw variants correctly
Source-stated
The authors report byte-identical replays of saved pipelines and say a same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points.
all 2,176 replays of 128 saved pipelines gave byte-identical files under pinned dependencies; subgroup differences in inference accuracy vanished within facility and lookup key; and the same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points
Escape Meridian interpretation
The authors’ evidence includes real records from two chronic-disease cohorts and synthetic data; the abstract does not establish performance in other settings.
Six experiments on real records of two chronic-disease cohorts and on synthetic data at scale validate the tools
Source and contributor context
Alizadeh, Javad Mohammad, Wu, Huanmei · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Synthetic data
Training or test data generated by software or another model rather than collected from the world.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 29c4f1f2-83f9-4a17-8655-05a0f30d5ebe.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T09:53:10.448Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 9c2cd7d089af97d4c3153a348fc53151a3a53a13d52852e3f55359c6512169fe. Source text hash: 766b5d856b44ffc4052513bdb026dae6037334b86a3b8caf6fc0f0f091560d60.
Revision and source-check history
Version 1 · 2026-11-10T09:53:10.448Z
Alizadeh and Wu benchmark THReD for preparing clinical records for AI
Javad Mohammad Alizadeh and Huanmei Wu report a of THReD, a web-based system that uses a chat agent to turn electronic health record (EHR) extracts into AI-ready datasets. The agent’s proposed steps run only after a user confirms them. The authors test whether the agent completes data-preparation tasks and whether its tools produce correct, reproducible results. Across 141 tasks, the deployed agent completed 92.7% on its first answer, but completion fell to 58.0% without a data profile; requests requiring the agent to infer an overall goal were hardest. In separate tool experiments, the authors report high accuracy for inferred patient classes and vital-sign labels, successful mapping of raw variants, and byte-identical replays of saved pipelines under pinned dependencies. They also report that a same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points, while cohort composition shifted by no more than one point. Escape Meridian’s reading: the data profile and preprocessing choices are central to interpreting the benchmark. Task completion fell when the profile was removed, and one rule changed a measured health outcome. The authors tested synthetic tables and records from two chronic-disease cohorts; how well these results carry over to other settings remains open.
Original source
cs.AI · Article
Source details
Original source date: Nov 2, 2026 (2026-11-02).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-10T09:53:10.448Z. First observed: 2026-11-03T09:04:11.492Z. Retrieved: 2026-11-04T04:46:51.434Z.
Limits
The agent used synthetic tables calibrated to a regional health information exchange; the abstract does not report testing the agent benchmark on real clinical tables. The reported tool experiments cover real records from two chronic-disease cohorts and ; the abstract does not establish how results generalize to other facilities or cohorts. The abstract reports summary results but does not provide the full task definitions or per-episode results; it says these are available on Zenodo.
Open question
How would THReD’s reported performance and the effects of its preprocessing choices hold up on clinical records from other facilities and cohorts?
Source-stated
Alizadeh and Wu describe THReD as a web-based system that turns EHR extracts into AI-ready datasets, with proposed steps requiring user confirmation.
a web-based system that turns electronic health record (EHR) extracts into AI-ready datasets through a chat agent whose proposed steps run only after user confirmation
Source-stated
The agent benchmark included 141 tasks across seven categories, evaluated on synthetic tables calibrated to a regional health information exchange.
The agent benchmark has 141 tasks (100 templates) in seven categories
Source-stated
Across 2,550 episodes, the deployed agent completed 92.7% of tasks on its first answer; without the data profile, completion fell to 58.0%.
Over 2,550 episodes with Claude Haiku 5.5 in four context configurations, the deployed agent completed 92.7% (95% CI 88.1-96.4) of tasks on its first answer
Source-stated
The authors report high accuracy in masked reconstruction for inferred patient classes and vital-sign labels, as well as correct mapping of raw variants.
91-92% of inferred patient classes and 99.7-99.8% of inferred vital-sign labels were correct in masked reconstruction; a dictionary builder combining curated dictionaries with LLM suggestions mapped 99.0% of 2,208 raw variants correctly
Source-stated
The authors report byte-identical replays of saved pipelines and say a same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points.
all 2,176 replays of 128 saved pipelines gave byte-identical files under pinned dependencies; subgroup differences in inference accuracy vanished within facility and lookup key; and the same-day consolidation rule changed 90-day emergency revisit rates by up to 3.2 points
Escape Meridian interpretation
The authors’ evidence includes real records from two chronic-disease cohorts and synthetic data; the abstract does not establish performance in other settings.
Six experiments on real records of two chronic-disease cohorts and on synthetic data at scale validate the tools
Source and contributor context
Alizadeh, Javad Mohammad, Wu, Huanmei · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Benchmark
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Synthetic data
Training or test data generated by software or another model rather than collected from the world.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 29c4f1f2-83f9-4a17-8655-05a0f30d5ebe.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-10T09:53:10.448Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 9c2cd7d089af97d4c3153a348fc53151a3a53a13d52852e3f55359c6512169fe. Source text hash: 766b5d856b44ffc4052513bdb026dae6037334b86a3b8caf6fc0f0f091560d60.
uncertain · 2026-11-10T09:53:10.448Z · anthropic/claude-opus-5.5
Publication: 29c4f1f2-83f9-4a17-8655-05a0f30d5ebe
attribution — pass: The draft correctly names Javad Mohammad Alizadeh and Huanmei Wu as the authors. It frames the results as their report, and the Escape Meridian reading is clearly labeled as interpretation.
Authors:Javad Mohammad Alizadeh, Huanmei Wu
summary — uncertain: First, the draft paraphrases "goal-level requests" as requests requiring the agent "to infer an overall goal." The source does not define the term, and in such requests the goal is likely stated while the steps are left to the agent. Keep the exact term and add a tooltip. Second, central terms are unexplained for general readers: data profile, masked reconstruction, pinned dependencies, same-day consolidation rule and raw variants. Data profile carries the EM reading. Third, the draft omits notable reported results: abstention on every advice request, recovery from every injected fault, and never acting on planted prompt-injection text. Add one sentence to restore them.
abstained on every advice request, recovered from every injected fault, and never proposed an action requested by planted text
date — pass: originalPublished is 2026-11-02, which matches the arXiv v1 submission date. The feed date of Nov 3 is later and is correctly not used.
[Submitted on 2 Nov 2026]
support — pass: The figures match the abstract: 141 tasks, 2,550 episodes, 92.7%, 58.0%, the reconstruction accuracies, 99.0% of 2,208 variants, byte-identical replays, and the 3.2-point and one-point shifts. The generalization caveat and the limitation about synthetic agent-benchmark tables are supported. The question about other facilities follows from the evidence without presuming an answer. Minor: "successful mapping of raw variants" omits the 99.0% figure, but the draft does not claim perfection.
Without the data profile, completion fell to 58.0%; goal-level requests were hardest (80.6%).
media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
A defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceTraining or test data generated by software or another model rather than collected from the world.
Definition referenceA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA defined set of tests used to compare systems. Results depend on the tasks and measures selected.
Definition referenceTraining or test data generated by software or another model rather than collected from the world.
Definition reference