@inproceedings{ding-etal-2020-methods,
    title = "Methods for Extracting Information from Messages from Primary Care Providers to Specialists",
    author = "Ding, Xiyu  and
      Barnett, Michael  and
      Mehrotra, Ateev  and
      Miller, Timothy",
    editor = "Bhatia, Parminder  and
      Lin, Steven  and
      Gangadharaiah, Rashmi  and
      Wallace, Byron  and
      Shafran, Izhak  and
      Shivade, Chaitanya  and
      Du, Nan  and
      Diab, Mona",
    booktitle = "Proceedings of the First Workshop on Natural Language Processing for Medical Conversations",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.nlpmc-1.1/",
    doi = "10.18653/v1/2020.nlpmc-1.1",
    pages = "1--6",
    abstract = "Electronic consult (eConsult) systems allow specialists more flexibility to respond to referrals more efficiently, thereby increasing access in under-resourced healthcare settings like safety net systems. Understanding the usage patterns of eConsult system is an important part of improving specialist efficiency. In this work, we develop and apply classifiers to a dataset of eConsult questions from primary care providers to specialists, classifying the messages for how they were triaged by the specialist office, and the underlying type of clinical question posed by the primary care provider. We show that pre-trained transformer models are strong baselines, with improving performance from domain-specific training and shared representations."
}