Currently submitted to: Journal of Medical Internet Research
Date Submitted: Sep 28, 2026
Open Peer Review Period: Sep 30, 2026 - Nov 25, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The reply is ready. Is the site? False operational closure in AI-assisted clinical trial workflows
ABSTRACT
Generative artificial intelligence (AI) can faithfully reproduce a status that no longer reflects the operational state of a clinical trial. A reply confirming that a site is "ready" can be accurate about its source and wrong about the world. We define false operational closure as the adoption of a completion or readiness status when the evidence required to support it is absent or no longer current. Unlike model error or automation bias, it is defined at the level of the workflow and can be identified without inferring why anyone accepted the status. The key distinction is between verifying a document and verifying the state it represents. The failure mode is not new, but we hypothesize that generative AI may increase its frequency by making closure-ready messages instant and fluent while the evidence behind them still takes hours or days, and while that evidence remains scattered across systems the tool cannot see. We propose evidence-based closure for statuses that bear on critical-to-quality factors and outline observational and experimental tests of the construct.
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