@inproceedings{berg-dalianis-2024-using,
    title = "Using {BART} to Automatically Generate Discharge Summaries from {S}wedish Clinical Text",
    author = "Berg, Nils  and
      Dalianis, Hercules",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Thompson, Paul  and
      Ondov, Brian",
    booktitle = "Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.cl4health-1.30",
    pages = "246--252",
    abstract = "Documentation is a regular part of contemporary healthcare practices and one such documentation task is the creation of a discharge summary, which summarizes a care episode. However, to manually write discharge summaries is a time-consuming task, and research has shown that discharge summaries are often lacking quality in various respects. To alleviate this problem, text summarization methods could be applied on text from electronic health records, such as patient notes, to automatically create a discharge summary. Previous research has been conducted on this topic on text in various languages and with various methods, but no such research has been conducted on Swedish text. In this paper, four datasets extracted from a Swedish clinical corpora were used to fine-tune four BART language models to perform the task of summarizing Swedish patient notes into a discharge summary. Out of these models, the best performing model was manually evaluated by a senior, now retired, nurse and clinical coder. The evaluation results show that the best performing model produces discharge summaries of overall low quality. This is possibly due to issues in the data extracted from the Health Bank research infrastructure, which warrants further work on this topic.",
}

Documentation is a regular part of contemporary healthcare practices and one such documentation task is the creation of a discharge summary, which summarizes a care episode. However, to manually write discharge summaries is a time-consuming task, and research has shown that discharge summaries are often lacking quality in various respects. To alleviate this problem, text summarization methods could be applied on text from electronic health records, such as patient notes, to automatically create a discharge summary. Previous research has been conducted on this topic on text in various languages and with various methods, but no such research has been conducted on Swedish text. In this paper, four datasets extracted from a Swedish clinical corpora were used to fine-tune four BART language models to perform the task of summarizing Swedish patient notes into a discharge summary. Out of these models, the best performing model was manually evaluated by a senior, now retired, nurse and clinical coder. The evaluation results show that the best performing model produces discharge summaries of overall low quality. This is possibly due to issues in the data extracted from the Health Bank research infrastructure, which warrants further work on this topic.
Berg, Nils Dalianis, Hercules
Using BART to Automatically Generate Discharge Summaries from Swedish Clinical Text