Case Report Forms (CRFs) are structured instruments widely used in clinical research to systematically collect patient information according to predefined protocols. In practice, CRFs are often manually completed by clinicians based on patients’ clinical reports, a process that is time-consuming and prone to inconsistencies. Despite their central role in medical studies, automatic population of CRFs from clinical narratives remains largely underexplored in the Natural Language Processing community, partly due to the scarcity of publicly available datasets. In this paper, we present the CRF Filling Shared Task, organized at the CL4Health Workshop at LREC 2026, which aims to advance research on automatic extraction of structured clinical information from unstructured patient notes. The task consists of assigning the correct value to a set of predefined CRF items given a clinical note. The target dataset is derived from a real-world CRF for dyspnea assessment, comprising 134 medical items with predefined value sets. The task is provided in two languages, Italian and English. We describe the dataset, the task formulation, and the evaluation framework, and discuss the participating systems and their results. By introducing this shared task, we aim to stimulate research on clinically applicable NLP systems for structured data extraction in healthcare.

Understanding procedural skills from visual data is a key challenge in medical AI, especially for tasks that require reasoning over temporal sequences. We report on FBK-NLP’s participation at the ClinSkill QA 2026 shared task, which requires models to arrange shuffled key frames into a coherent sequence of clinical actions and provide explanations for the resulting order. We conduct a systematic study of prompting and reasoning strategies using an open and easily deployable vision-language model (VLM). The central finding of our study is that incorporating keypoint-based representations of people’s body parts substantially improves temporal reasoning behind frame ordering. Furthermore, we show that model performance is highly sensitive to prompt design and to seemingly minor factors such as filename ordering and the inclusion of domain information.