arXiv:2606. 09489v1 Announce Type: new Abstract: Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines.
By Giorgio Leonardi, Stefania Montani, Manuel Striani, Alessandro Canessa, Delfina Ferrandi
arXiv:2608.30022v1 Announce Type: new
Abstract: Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing ap...
By Ashvin Gupta, Denys Prociuk, Alessandra Russo, Brendan C. Delaney
arXiv:2607. 06802v1 Announce Type: cross Abstract: Open physiological corpora are heterogeneous: they use different sensors, labels, sampling rates, recording settings, and clinical endpoints.
By Dovy Paukstys
The paper introduces MEGA-CDP, a benchmark designed to evaluate medical large language models (LLMs) on their ability to generate clinical decision pathways (CDPs) that adhere to clinical practice guidelines. MEGA-CDP is built from 2,274 English and Chinese guidelines, producing 42,353 clinical cases with explicit reference CDPs, and supports both single-turn and multi-turn interactions. Experiments on 16 LLMs reveal that reliable guideline adherence remains difficult, underscoring the need for CDP-focused evaluation and the potential of MEGA-CDP to advance medical LLM performance.
By Nuo Chen, Xinyang Jiang, Zilong Wang, Zhifei Zhang, Xiaoye Qu, Jiajun Deng, Yulan Guo, Cairong Zhao
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.
By Keqi Han, Ryan Young, Annabel Strauss, Lindsey Hughes, Katharine M. Nesbitt, Nicole Schueler, Che Ngufor, Carl Yang, Yuan Xue, Zhijun Yin
The paper introduces ALLMTS, an automated medical triage system that uses large language models (LLMs) to execute commercially maintained telehealth guidelines rather than directly triage callers. By selecting the appropriate guideline and evaluating its triage questions, ALLMTS deterministically derives dispositions, achieving 0.95 accuracy on 566 synthetic scenarios and 0.98 on a 50-scenario benchmark, matching expert nurse performance. The system dramatically reduces over‑triage rates (to ≤0.02) while keeping under‑triage low, demonstrating the effectiveness of coupling LLMs with executable clinical guidelines.
By Micha{\l} Remigiusz Janiszewski, Artur Stopa, {\L}ukasz Lenkiewicz, Wojciech Achtelik, Bartosz Gonczarek