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

Quiroz, JC; Laranjo, L; Kocaballi, AB; Briatore, A; Berkovsky, S; Rezazadegan, D; Coiera, E

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

Quiroz, JC Laranjo, L Kocaballi, AB Briatore, A Berkovsky, S Rezazadegan, D Coiera, E

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Publisher:: SAGE Publications
Publication Type:: Journal Article
Citation:: Health Informatics Journal, pp. 146045822095171-146045822095171

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Field	Value	Language
dc.contributor.author	Quiroz, JC
dc.contributor.author	Laranjo, L
dc.contributor.author	Kocaballi, AB
dc.contributor.author	Briatore, A
dc.contributor.author	Berkovsky, S
dc.contributor.author	Rezazadegan, D
dc.contributor.author	Coiera, E
dc.date.accessioned	2020-08-30T03:28:04Z
dc.date.available	2020-08-30T03:28:04Z
dc.identifier.citation	Health Informatics Journal, pp. 146045822095171-146045822095171
dc.identifier.issn	1460-4582
dc.identifier.issn	1741-2811
dc.identifier.uri	http://hdl.handle.net/10453/142420
dc.description.abstract	<jats:p> 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. </jats:p>
dc.language	en
dc.publisher	SAGE Publications
dc.relation.ispartof	Health Informatics Journal
dc.relation.isbasedon	10.1177/1460458220951719
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	Health Informatics Journal, pp. 146045822095171-146045822095171. Copyright © SAGE Publications. DOI: https://dx.doi.org/10.1177/1460458220951719	en_US
dc.subject	0806 Information Systems, 0807 Library and Information Studies
dc.subject.classification	Medical Informatics
dc.title	Identifying relevant information in medical conversations to summarize a clinician-patient encounter
dc.type	Journal Article
utslib.for	0806 Information Systems
utslib.for	0807 Library and Information Studies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-08-30T03:27:58Z
pubs.publication-status	Published online

Abstract:

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.

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http://hdl.handle.net/10453/142420