# Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department

arXiv: https://arxiv.org/abs/2602.23062 · HTML: https://mlsanity.com/paper/2602.23062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 3 of 3)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures](https://mlsanity.com/paper/2410.05235/) (2024)
- [Converting Annotated Clinical Cases into Structured Case Report Forms](https://mlsanity.com/paper/2506.11666/) (2025)

## Built on by (most cited first, 1 of 1)

- [sebis at CRF Filling 2026: A Two-Stage Local LLM Pipeline for Medical CRF Filling](https://mlsanity.com/paper/2606.13082/) (2026)

## Abstract (arXiv)

Case Report Forms (CRFs) collect data about patients and are at the core of well-established practices to conduct research in clinical settings. With the recent progress of language technologies, there is an increasing interest in automatic CRF-filling from clinical notes, mostly based on the use of Large Language Models (LLMs). However, there is a general scarcity of annotated CRF data, both for training and testing LLMs, which limits the progress on this task. As a step in the direction of providing such data, we present a new dataset of clinical notes from an Italian Emergency Department annotated with respect to a pre-defined CRF containing 134 items to be filled. We provide an analysis of the data, define the CRF-filling task and metric for its evaluation, and report on pilot experiments where we use an open-source state-of-the-art LLM to automatically execute the task. Results of the case-study show that (i) CRF-filling from real clinical notes in Italian can be approached in a zero-shot setting; (ii) LLMs' results are affected by biases (e.g., a cautious behaviour favours "unknown" answers), which need to be corrected.