Challenges of developing a digital scribe to reduce clinical documentation burden.

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

Challenges of developing a digital scribe to reduce clinical documentation burden.

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

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Publisher:: Springer Science and Business Media LLC
Publication Type:: Journal Article
Citation:: NPJ digital medicine, 2019, 2, (1), pp. 114
Issue Date:: 2019-01

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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	Berkovsky, S
dc.contributor.author	Rezazadegan, D
dc.contributor.author	Coiera, E
dc.date.accessioned	2020-05-09T01:41:50Z
dc.date.available	2019-10-29
dc.date.available	2020-05-09T01:41:50Z
dc.date.issued	2019-01
dc.identifier.citation	NPJ digital medicine, 2019, 2, (1), pp. 114
dc.identifier.issn	2398-6352
dc.identifier.issn	2398-6352
dc.identifier.uri	http://hdl.handle.net/10453/140597
dc.description.abstract	Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Springer Science and Business Media LLC
dc.relation.ispartof	NPJ digital medicine
dc.relation.isbasedon	10.1038/s41746-019-0190-1
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in NPJ digital medicine. The final authenticated version is available online at: https://dx.doi.org/10.1038/s41746-019-0190-1.	en_US
dc.title	Challenges of developing a digital scribe to reduce clinical documentation burden.
dc.type	Journal Article
utslib.citation.volume	2
utslib.location.activity	England
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	2020-05-09T01:41:48Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	2
utslib.start-page	114
utslib.citation.issue	1

Abstract:

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.

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http://hdl.handle.net/10453/140597