Exploring the Potential of LLMs for Patient Safety Incident Reporting in Finland: Interview Insights and a Proof-of-Concept Study
: Annevirta, Jusa; Saarenpää, Ilkka
: Mantas, John; Hasman, Arie; Gallos, Parisis; Zoulias, Emmanouil; Karitis, Konstantinos
: International Conference of Informatics, Management and Technology in Healthcare
Publisher: IOS Press BV
: 2025
: Studies in Health Technology and Informatics
: Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics
: Studies in Health Technology and Informatics
: 328
: 41
: 45
: 978-1-64368-600-4
: 0926-9630
: 1879-8365
DOI: https://doi.org/10.3233/SHTI250669
: https://ebooks.iospress.nl/doi/10.3233/SHTI250669
: https://research.utu.fi/converis/portal/detail/Publication/499593301
This paper explores the potential of Large Language Models (LLMs) to improve patient safety incident (PSI) reporting in Finland. Through semi-structured interviews with doctors and authorities, key requirements and perspectives on AI integration were gathered. A Proof-of-Concept (PoC) study evaluated the feasibility of using a commercial LLM (GPT-4o) to generate structured PSI reports from unstructured clinical text from patient records. Interview results highlighted the need for integrated and automated reporting systems, with AI seen as a tool to reduce documenting burden and improve data analysis. The PoC demonstrated the technological capability of the LLM to generate coherent and relevant reports but also revealed challenges in completeness and distinguishing incident causality. The findings suggest promising avenues for leveraging LLMs in PSI reporting, warranting further research and development for national implementation.
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The work was funded by the Finnish Ministry of Social Affairs and Health (STM) as a part of an initiative led by the Finnish Centre for Client and Patient Safety on improving national patient safety reporting in Finland.
