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dc.contributor.authorManning, F
dc.contributor.authorMahmoud, A
dc.contributor.authorMeertens, R
dc.date.accessioned2023-09-22T15:18:55Z
dc.date.issued2023-09-19
dc.date.updated2023-09-22T14:50:46Z
dc.description.abstractIntroduction: Research into patient and public views on predictive software and its use in healthcare is relatively new. This study aimed to understand older adults' acceptability of an opportunistic bone density assessment for osteoporosis diagnosis (IBEX BH), views on its integration into healthcare, and views on predictive software and AI in healthcare. Methods: Focus groups were conducted with participants aged over 50 years, based in South West England. Data were analysed using thematic analysis. Analysis was informed by the theoretical framework of acceptability. Results: Two focus groups were undertaken with a total of 14 participants. Overall, the participants were generally positive about the IBEX BH software, and predictive software's in general stating ‘it sounds like a brilliant idea’. Although participants did not understand the intricacies of the software, they did not feel they needed to. Concerns about IBEX BH focussed more on the clinical indications of the software (e.g. more scans or medications), with participants expressing less trust in results if they indicated medication. Questions were also raised about how and who would receive the results of this software. Individual choice was evident in these discussions, however most indicated the preferences for spoken communication ‘But I would expect that these results would be given by a human to another human.’ Conclusions: Focus group participants were generally accepting of the use of predictive software in healthcare. Implications for practice: Thought and care needs to be taken when integrating predictive software into practice. Focusses on empowering patients, providing information on processes and results are keyen_GB
dc.description.sponsorshipTranslational Research Exchange @ Exeter (TREE)en_GB
dc.format.extent1046-1053
dc.identifier.citationVol. 29, No. 6, pp. 1046-1053en_GB
dc.identifier.doihttps://doi.org/10.1016/j.radi.2023.08.011
dc.identifier.grantnumber121526en_GB
dc.identifier.urihttp://hdl.handle.net/10871/134050
dc.identifierORCID: 0000-0002-9768-1695 (Manning, F)
dc.identifierORCID: 0000-0002-2120-8877 (Meertens, R)
dc.language.isoenen_GB
dc.publisherElsevier / The College of Radiographersen_GB
dc.rights© 2023 The Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).en_GB
dc.subjectOsteoporosisen_GB
dc.subjectScreeningen_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectPatient involvementen_GB
dc.subjectQualitativeen_GB
dc.subjectPredictive softwareen_GB
dc.titleUnderstanding patient views and acceptability of predictive software in osteoporosis identificationen_GB
dc.typeArticleen_GB
dc.date.available2023-09-22T15:18:55Z
dc.identifier.issn1078-8174
dc.descriptionThis is the final version. Available on open access from Elsevier via the DOI in this record. en_GB
dc.identifier.eissn1532-2831
dc.identifier.journalRadiographyen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2023-08-28
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2023-09-19
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-09-22T15:16:08Z
refterms.versionFCDVoR
refterms.dateFOA2023-09-22T15:19:36Z
refterms.panelAen_GB
refterms.dateFirstOnline2023-09-19


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© 2023 The Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open
access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's licence is described as © 2023 The Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).