A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports

Henning Schäfer; Cynthia S. Schmidt; Johannes Wutzkowsky; Kamil Lorek; Lea Reinartz; Johannes Rückert; Christian Temme; Britta Böckmann; Peter A. Horn; Christoph M. Friedrich

arXiv · 2504.20220

A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports

Henning Schäfer·Cynthia S. Schmidt·Johannes Wutzkowsky·Kamil Lorek·Lea Reinartz·Johannes Rückert·Christian Temme·Britta Böckmann·Peter A. Horn·Christoph M. Friedrich

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Abstract

Despite the growing adoption of electronic health records, many processes still rely on paper documents, reflecting the heterogeneous real-world conditions in which healthcare is delivered. The manual transcription process is time-consuming and prone to errors when transferring paper-based data to digital formats. To streamline this workflow, this study presents an open-source pipeline that extracts and categorizes checkbox data from scanned documents. Demonstrated on transfusion reaction reports, the design supports adaptation to other checkbox-rich document types. The proposed method integrates checkbox detection, multilingual optical character recognition (OCR) and multilingual vision-language models (VLMs). The pipeline achieves high precision and recall compared against annually compiled gold-standards from 2017 to 2024. The result is a reduction in administrative workload and accurate regulatory reporting. The open-source availability of this pipeline encourages self-hosted parsing of checkbox forms.

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Henning Schäfer, Cynthia S. Schmidt, Johannes Wutzkowsky, Kamil Lorek, Lea Reinartz, Johannes Rückert, Christian Temme, Britta Böckmann, Peter A. Horn, Christoph M. Friedrich. 2025-04-28. A Multimodal Pipeline for Clinical Data Extraction: Applying Vision-Language Models to Scans of Transfusion Reaction Reports. https://arxiv.org/abs/2504.20220

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