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TRUVACE RECORD VERSION record: TRV-2026-2035 version: 1 kind: certified reason: Certified into the record timestamp: 2026-10-20T18:14:32.505025Z status: published lens: trace sector: health headline: Large Language Model-Generated Electronic Consultations: A Blinded Comparative Study of OpenEvidence and Gastroenterologist Electronic Consultations dek: Objective: To evaluate whether a context-specific large language model, OpenEvidence, can generate gastroenterology/hepatology eConsult responses for metabolic dysfunction-associated steatotic liver disease (MASLD), while characterizing the frequency and potential severity of errors. Patients and methods: In this retrospective cohort study at a tertiary academic medical center, 125 specialist electronic consultations (eConsults) for MASLD (from January 2020 to December 2024) were paired with OpenEvidence-generat… gain_title: In blinded ratings of 125 paired MASLD eConsults, responses generated by the OpenEvidence language model scored higher on quality and on appropriate information amount than specialist-written responses. problem_title: Among the 125 OpenEvidence-generated MASLD eConsults, 12 contained minor errors and 1 contained a potentially major error, so clinically relevant mistakes still occurred. trace_subject: OpenEvidence-generated eConsults for MASLD gain_reading: In blinded ratings of 125 paired MASLD eConsults, responses generated by the OpenEvidence language model scored higher on quality and on appropriate information amount than specialist-written responses. gain_evidence: OpenEvidence-generated eConsults received higher overall eConsult Specialist Quality of Response scores than specialist eConsults problem_reading: Among the 125 OpenEvidence-generated MASLD eConsults, 12 contained minor errors and 1 contained a potentially major error, so clinically relevant mistakes still occurred. problem_evidence: 12 contained minor errors and 1 contained a potentially major error quick_read: Researchers at a tertiary academic medical center paired 125 past specialist eConsults on metabolic dysfunction-associated steatotic liver disease with responses produced by OpenEvidence from standardized prompts. Five physician reviewers, blinded to the source, rated all responses with a previously validated instrument. The AI responses were rated higher in perceived quality and informational adequacy, which suggests a possible role in assisting protocol-driven specialist consultations. However, the ratings measure perceived quality rather than patient outcomes, and the presence of one potentially major error shows that oversight remains a open question. limitation: The study was a retrospective, single-center comparison of perceived quality for one condition, and the AI outputs still included errors, one potentially major. tag: Dual reading key_points: The mean difference in eConsult Specialist Quality of Response scores favored OpenEvidence by 6.4 points (95% CI, 6.0-6.9). | Global quality ratings favored OpenEvidence with an odds ratio of 16.1. | OpenEvidence responses were more often judged to give an appropriate amount of information, 70.9% versus 33.9%. | Of 125 OpenEvidence eConsults, 12 had minor errors and 1 had a potentially major error. rundown: The study was a retrospective cohort at a single tertiary academic medical center, using specialist eConsults for MASLD written between January 2020 and December 2024. Each was matched with an OpenEvidence-generated version. Five blinded physician reviewers scored every eConsult. A separate board-certified gastroenterologist reviewed the OpenEvidence outputs to identify errors and assess their potential for harm. The authors conclude that the findings support further evaluation of OpenEvidence-assisted eConsult workflows for protocol-driven conditions, rather than replacing specialists outright. sources: - peer_reviewed | Mayo Clinic Proceedings: Digital Health | https://doi.org/10.1016/j.mcpdig.2026.100404 | 2026-09-18 prev: 0000000000000000000000000000000000000000000000000000000000000000
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