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    <abstract>Nursing staff are increasingly confronted with extensive and detailed patient documentation, requiring much time to read through numerous possible care measures. Combined with rising patient loads, this underscores the need for a clearer and more immediately accessible overview of each patient’s situation. Patient-specific care pathway visualisations offer a promising approach to reduce cognitive load, support faster decision-making, and improve situational awareness. This work investigates two Artificial intelligence (AI)-assisted methods for generating such visualisations: (1) simple image generation based on structured textual prompts, and (2) automated code generation that produces graph-based representations of clinical pathways. Using a dataset of synthetic patient profiles and seven defined care pathways, evaluating multiple state-of-the-art foundation models. The results highlight clear differences between models and approaches, particularly in language sensitivity, structural consistency, and the level of detail achievable. Image-based outputs provided visually rich overviews but frequently introduced subtle logical inconsistencies, while code-based methods produced verifiable and structurally coherent pathways yet varied in their ability to preserve contextual and psychosocial information. Together, these findings indicate that AI-assisted visualisation can effectively support—but not yet fully automate—patient-specific pathway generation, and they point toward hybrid solutions that combine visual accessibility with logical robustness.</abstract>
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%0 Conference Proceedings
%T Patient-Specific Care Pathway Visualisation for Medical Nursing Staff
%A Bauernfeind, Sophie
%A Adlberger, Selina
%A Krauss, Oliver
%A Stöckl, Andreas
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F bauernfeind-etal-2026-patient
%X Nursing staff are increasingly confronted with extensive and detailed patient documentation, requiring much time to read through numerous possible care measures. Combined with rising patient loads, this underscores the need for a clearer and more immediately accessible overview of each patient’s situation. Patient-specific care pathway visualisations offer a promising approach to reduce cognitive load, support faster decision-making, and improve situational awareness. This work investigates two Artificial intelligence (AI)-assisted methods for generating such visualisations: (1) simple image generation based on structured textual prompts, and (2) automated code generation that produces graph-based representations of clinical pathways. Using a dataset of synthetic patient profiles and seven defined care pathways, evaluating multiple state-of-the-art foundation models. The results highlight clear differences between models and approaches, particularly in language sensitivity, structural consistency, and the level of detail achievable. Image-based outputs provided visually rich overviews but frequently introduced subtle logical inconsistencies, while code-based methods produced verifiable and structurally coherent pathways yet varied in their ability to preserve contextual and psychosocial information. Together, these findings indicate that AI-assisted visualisation can effectively support—but not yet fully automate—patient-specific pathway generation, and they point toward hybrid solutions that combine visual accessibility with logical robustness.
%R 10.63317/5gskdj7g5ceg
%U https://aclanthology.org/2026.cl4health-1.11/
%U https://doi.org/10.63317/5gskdj7g5ceg
%P 117-131

Markdown (Informal)

[Patient-Specific Care Pathway Visualisation for Medical Nursing Staff](https://aclanthology.org/2026.cl4health-1.11/) (Bauernfeind et al., CL4Health 2026)

• Patient-Specific Care Pathway Visualisation for Medical Nursing Staff (Bauernfeind et al., CL4Health 2026)

ACL

• Sophie Bauernfeind, Selina Adlberger, Oliver Krauss, and Andreas Stöckl. 2026. Patient-Specific Care Pathway Visualisation for Medical Nursing Staff. In Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026, pages 117–131, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).