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Identifying relevant information in medical conversations to summarize a clinician-patient encounter.

Juan Carlos QuirozLiliana LaranjoAhmet Baki KocaballiAgustina BriatoreShlomo BerkovskyDana RezazadeganEnrico Coiera
Published in: Health informatics journal (2020)
To inform the development of automated summarization of clinical conversations, this study sought to estimate the proportion of doctor-patient communication in general practice (GP) consultations used for generating a consultation summary. Two researchers with a medical degree read the transcripts of 44 GP consultations and highlighted the phrases to be used for generating a summary of the consultation. For all consultations, less than 20% of all words in the transcripts were needed for inclusion in the summary. On average, 9.1% of all words in the transcripts, 26.6% of all medical terms, and 27.3% of all speaker turns were highlighted. The results indicate that communication content used for generating a consultation summary makes up a small portion of GP consultations, and automated summarization solutions-such as digital scribes-must focus on identifying the 20% relevant information for automatically generating consultation summaries.
Keyphrases
  • general practice
  • palliative care
  • primary care
  • healthcare
  • case report
  • machine learning
  • deep learning
  • health information
  • advance care planning