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AuthorWalter, Steffendc.contributor.author
AuthorGruss, Saschadc.contributor.author
AuthorFrisch, Stephandc.contributor.author
AuthorLiter, Josephdc.contributor.author
AuthorJerg-Bretzke, Luciadc.contributor.author
AuthorZujalovic, Benediktdc.contributor.author
AuthorBarth, Eberharddc.contributor.author
Date of accession2021-10-20T13:34:29Zdc.date.accessioned
Available in OPARU since2021-10-20T13:34:29Zdc.date.available
Date of first publication2020-12-21dc.date.issued
AbstractBackground: Over the last 12 years, the fundamentals of automated pain recognition using artificial intelligence (AI) algorithms have been investigated and optimized. The main target groups are patients with limited communicative abilities. To date, the extent to which anesthetists and nurses in intensive care units would benefit from an automated pain recognition system has not been investigated. Methods: N = 102 clinical employees were interviewed. To this end, they were shown a video in which the visionary technology of automated pain recognition, its basis and goals are outlined. Subsequently, questions were asked about: (1) the potential benefit of an automated pain recognition in clinical context, (2) preferences with regard to the modality used (physiological, paralinguistic, video-based, multimodal), (3) the maximum willingness to invest, (4) preferences concerning the required pain recognition rate and finally (5) willingness to use automated pain recognition. Results: The respondents expect the greatest benefit from an automated pain recognition system to be “to avoid over- or undersupply of analgesics in patients with limited communicative abilities,” a total of 50% of respondents indicated that they would use automated pain recognition technology, 32.4% replied with “perhaps” and 17.4% would not use it. Conclusion: Automated pain recognition is, in principle, accepted by anesthetists and nursing staff as a possible new method, with expected benefits for patients with limited communicative skills. However, studies on automated pain recognition in a clinical environment and proof of its acceptance and practicability are absolutely necessary before such systems can be implemented.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
Keywordautomated pain recognitiondc.subject
Keywordacceptancedc.subject
Keywordbenefitdc.subject
Keywordmultimodaldc.subject
Dewey Decimal GroupDDC 610 / Medicine & healthdc.subject.ddc
MeSHPaindc.subject.mesh
MeSHPain managementdc.subject.mesh
MeSHArtificial intelligencedc.subject.mesh
Title“What About Automated Pain Recognition for Routine Clinical Use?” A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptancedc.title
Resource typeWissenschaftlicher Artikeldc.type
SWORD Date2021-01-04T05:35:54Zdc.date.updated
VersionpublishedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-39183dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-39259-4dc.identifier.urn
GNDAlgesimetriedc.subject.gnd
GNDKünstliche Intelligenzdc.subject.gnd
InstitutionUKU. Klinik für Psychosomatische Medizin und Psychotherapieuulm.affiliationSpecific
InstitutionUKU. Klinik für Anästhesiologieuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeCollectionuulm.typeDCMI
CategoryPublikationenuulm.category
DOI of original publication10.3389/fmed.2020.566278dc.relation1.doi
Source - Title of sourceFrontiers in Medicinesource.title
Source - Place of publicationFrontiers Mediasource.publisher
Source - Volume7source.volume
Source - Year2020source.year
Source - Article number566278source.articleNumber
Source - eISSN2296-858Xsource.identifier.eissn
Open AccessGreen Published, Other Golduulm.OA
WoS000604293100001uulm.identifier.wos
Bibliographyuulmuulm.bibliographie
Is Supplemented Byhttps://www.frontiersin.org/articles/10.3389/fmed.2020.566278/full#supplementary-materialdc.relation.isSupplementedBy


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