Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice

Navarro, DF; Kocaballi, AB; Dras, M; Berkovsky, S

Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice

Navarro, DF Kocaballi, AB Dras, M Berkovsky, S

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Computer-Human Interaction

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Field	Value	Language
dc.contributor.author	Navarro, DF
dc.contributor.author	Kocaballi, AB
dc.contributor.author	Dras, M
dc.contributor.author	Berkovsky, S
dc.date.accessioned	2023-01-27T05:39:49Z
dc.date.available	2023-01-27T05:39:49Z
dc.identifier.citation	ACM Transactions on Computer-Human Interaction
dc.identifier.issn	1073-0516
dc.identifier.issn	1557-7325
dc.identifier.uri	http://hdl.handle.net/10453/165542
dc.description.abstract	<jats:p>General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.</jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM Transactions on Computer-Human Interaction
dc.relation.isbasedon	10.1145/3569893
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	"© ACM 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Computer-Human Interaction • Online publication date: 27 Oct 2022 http://doi.org/10.1145/3569893"
dc.subject	0806 Information Systems, 1702 Cognitive Sciences
dc.subject.classification	Human Factors
dc.title	Collaboration, not Confrontation: Understanding General Practitioners’ Attitudes Towards Natural Language and Text Automation in Clinical Practice
dc.type	Journal Article
utslib.for	0806 Information Systems
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	open_access	*
dc.date.updated	2023-01-27T05:39:47Z
pubs.publication-status	Published online

Abstract:

General Practitioners are among the primary users and curators of textual electronic health records, highlighting the need for technologies supporting record access and administration. Recent advancements in natural language processing facilitate the development of clinical systems, automating some time-consuming record-keeping tasks. However, it remains unclear what automation tasks would benefit clinicians most, what features such automation should exhibit, and how clinicians will interact with the automation. We conducted semi-structured interviews with General Practitioners uncovering their views and attitudes toward text automation. The main emerging theme was doctor-AI collaboration, addressing a reciprocal clinician-technology relationship that does not threaten to substitute clinicians, but rather establishes a constructive synergistic relationship. Other themes included: (i) desired features for clinical text automation; (ii) concerns around clinical text automation; and (iii) the consultation of the future. Our findings will inform the design of future natural language processing systems, to be implemented in general practice.

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http://hdl.handle.net/10453/165542