@inproceedings{landes-etal-2023-hospital,
    title = "Hospital Discharge Summarization Data Provenance",
    author = "Landes, Paul  and
      Chaise, Aaron  and
      Patel, Kunal  and
      Huang, Sean  and
      Di Eugenio, Barbara",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.41",
    doi = "10.18653/v1/2023.bionlp-1.41",
    pages = "439--448",
    abstract = "Summarization of medical notes has been studied for decades with hospital discharge summaries garnering recent interest in the research community. While methods for summarizing these notes have been the focus, there has been little work in understanding the feasibility of this task. We believe this effort is warranted given the notes{'} length and complexity, and that they are often riddled with poorly formatted structured data and redundancy in copy and pasted text. In this work, we investigate the feasibility of the summarization task by finding the origin, or data provenance, of the discharge summary{'}s source text. As a motivation to understanding the data challenges of the summarization task, we present DSProv, a new dataset of 51 hospital admissions annotated by clinical informatics physicians. The dataset is analyzed for semantics and the extent of copied text from human authored electronic health record (EHR) notes. We also present a novel unsupervised method of matching notes used in discharge summaries, and release our annotation dataset1 and source code to the community.",
}

Summarization of medical notes has been studied for decades with hospital discharge summaries garnering recent interest in the research community. While methods for summarizing these notes have been the focus, there has been little work in understanding the feasibility of this task. We believe this effort is warranted given the notes{'} length and complexity, and that they are often riddled with poorly formatted structured data and redundancy in copy and pasted text. In this work, we investigate the feasibility of the summarization task by finding the origin, or data provenance, of the discharge summary{'}s source text. As a motivation to understanding the data challenges of the summarization task, we present DSProv, a new dataset of 51 hospital admissions annotated by clinical informatics physicians. The dataset is analyzed for semantics and the extent of copied text from human authored electronic health record (EHR) notes. We also present a novel unsupervised method of matching notes used in discharge summaries, and release our annotation dataset1 and source code to the community.
L es, Paul Chaise, Aaron Patel, Kunal Huang, Sean Di Eugenio, Barbara
Hospital Discharge Summarization Data Provenance