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

Juan C. Quiroz*, Liliana Laranjo, Ahmet Baki Kocaballi, Agustina Briatore, Shlomo Berkovsky, Dana Rezazadegan, Enrico Coiera

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
127 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)2906-2914
Number of pages9
JournalHealth Informatics Journal
Issue number4
Early online date29 Aug 2020
Publication statusPublished - Dec 2020

Bibliographical note

Copyright the Author(s) 2020. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • Automatic summarization
  • clinical conversations
  • digital scribe
  • GP consultation
  • natural language processing
  • Pareto principle


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