Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department
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arXiv
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| Format: | Preprint |
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2026
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| author | Kaczmarek, Gabriela Anna Ferrazzi, Pietro Porta, Lorenzo Rubini, Vicky Magnini, Bernardo |
| author_facet | Kaczmarek, Gabriela Anna Ferrazzi, Pietro Porta, Lorenzo Rubini, Vicky Magnini, Bernardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_23062 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department Kaczmarek, Gabriela Anna Ferrazzi, Pietro Porta, Lorenzo Rubini, Vicky Magnini, Bernardo Computation and Language 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. |
| title | Toward Automatic Filling of Case Report Forms: A Case Study on Data from an Italian Emergency Department |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2602.23062 |