Grounded Adjudication of Variations across Extracted TimeLines (GAVEL) is a new LLM‑based protocol that compares two clinical timelines against their source case report, identifying discrepancy types, issuing verdicts, and citing relevant report passages for each difference. In a study of 126 reports, GAVEL evaluated 2,738 findings from GPT‑5.6sol and DeepSeek V3.2, ranked six LLM extractors and two human annotators, and guided a merging process that improved timeline accuracy—reducing discrepancies from 7.63 to 0.85 per report and yielding a 77.0% preference rate for merged timelines. The approach demonstrates that report‑based comparison can refine extracted timelines without assuming any single timeline as ground truth.
arXiv:2608.22062v1 Announce Type: new Abstract: Longitudinal clinical event-relation verification determines whether a patient record supports a specified relation among two or more clinical events....
CliniCIRCA is a modular large‑language‑model framework that reconstructs longitudinal mental‑health patient journeys from raw electronic health record narratives. It temporally classifies clinical events in unstructured discharge summaries without explicit timestamps, producing 15,891 tagged events from 52 summaries and correcting 629 errors to create verified gold‑standard timelines. The framework then generates temporally grounded summaries, compressing each source by 1.52×, and scales to produce 1,000 silver‑standard timelines for training, showing that instruction tuning improves event extraction, temporal tagging, and summarization across models.
arXiv:2606. 05463v1 Announce Type: new Abstract: Patient safety event triage, determining whether a clinical event is reportable under jurisdiction-specific policy, is a high-stakes task typically performed manually by patient safety experts.
arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is...
arXiv:2608.28974v1 Announce Type: new Abstract: Clinically relevant oncology information is distributed across heterogeneous, longitudinal documentation, creating substantial abstraction burden and r...
The study evaluates large language models (LLMs) on unprocessed electronic medical record data for clinical registry abstraction, focusing on the American College of Cardiology National Cardiovascular Data Registry. In a pilot at one academic center, the LLM identified candidate data sources for each registry question, which abstractors used to define question‑specific document sets. In a subsequent validation at a second center, the LLM answered 157 registry questions with an overall mean accuracy of 91.5%, but accuracy dropped from 96% for simple medication or event flag questions to 62% for event timing questions, reflecting increasing ambiguity and required clinical reasoning.
Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select one of these records before model comparison begins...
arXiv:2608. 12779v1 Announce Type: cross Abstract: Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment.
arXiv:2609.16267v1 Announce Type: new Abstract: Many operational cases are documented more than once, at different workflow stages and for different purposes, yet model evaluations normally select on...
Understanding the temporal progression of symptoms in clinical narratives is critical for disease monitoring, safety surveillance, and causality assessment. Clinical narratives, however, rarely provide explicit temporal anchors.
The paper introduces VERGE, a verification-enhanced refinement workflow that extracts six red‑flag symptoms and family‑history risk status for early‑onset colorectal cancer from free‑text clinical notes. VERGE uses retrieval‑augmented generation followed by a bounded verification‑refinement cycle that checks textual grounding and clinical validity, correcting claims until resolved or escalating to human review. In evaluation on 4,033 clinician‑labeled note‑finding pairs, VERGE improved precision from 0.764 to 0.849 and MCC from 0.681 to 0.730 compared to a single‑agent baseline, while requiring human review for only 1.5 % of claims.
The paper introduces Clinical Intent Extraction (CIE), a task that transforms fragmented clinical action annotations into complete structured records called Clinical Intent Representation (CIR). CIR decomposes each action into verb, type, coded target, timing, condition, request‑intent (aligned to HL7 FHIR) and modality, adding dimensions absent in prior datasets. By re‑expressing five heterogeneous corpora into CIR, the authors create CIRCA, a benchmark of 10,011 harmonized intents with human‑validated subsets, crosswalks, and a deterministic FHIR R4 mapper, and demonstrate that existing models perform poorly on the full task, highlighting the need for targeted development.