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dc.contributor.authorde Pennington, N
dc.contributor.authorMole, G
dc.contributor.authorLim, E
dc.contributor.authorMilne-Ives, Madison
dc.contributor.authorNormando, E
dc.contributor.authorXue, K
dc.contributor.authorMeinert, Edward
dc.date.accessioned2021-01-21T10:18:52Z
dc.date.issued2021-07-28
dc.identifier.issn1929-0748
dc.identifier.issn1929-0748
dc.identifier.othere27227
dc.identifier.urihttp://hdl.handle.net/10026.1/16811
dc.description.abstract

Background: Due to an ageing population, the demand for many services is exceeding the capacity of the clinical workforce. As a result, staff are facing a crisis of burnout from being pressured to deliver high-volume workloads, driving increasing costs for providers. Artificial intelligence, in the form of conversational agents, presents a possible opportunity to enable efficiencies in the delivery of care. Aims and Objectives: This study aims to evaluate the effectiveness, usability, and acceptability of Dora - an AI-enabled autonomous telemedicine call - for detection of post-operative cataract surgery patients who require further assessment. The study’s objectives are to: 1) establish Dora’s efficacy in comparison to an expert clinician, 2) determine baseline sensitivity and specificity for detection of true complications, 3) evaluate patient acceptability, 4) collect evidence for cost-effectiveness, and 5) capture data to support further development and evaluation. Methods: Based on implementation science, the interdisciplinary study will be a mixed-methods phase one pilot establishing inter-observer reliability of the system, usability, and acceptability. This will be done using using the following scales and frameworks: the system usability scale; assessment of Health Information Technology Interventions in Evidence-Based Medicine Evaluation Framework; the telehealth usability questionnaire (TUQ); the Non-Adoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework. Results: The results will be included in the final evaluation paper, which we aim to publish in 2022. The study will last eighteen months: seven months of evaluation and intervention refinement, nine months of implementation and follow-up, and two months of post-evaluation analysis and write-up. Conclusions: The project’s key contributions will be evidence on artificial intelligence voice conversational agent effectiveness, and associated usability and acceptability.

dc.format.extente27227-e27227
dc.format.mediumElectronic
dc.languageen
dc.language.isoen
dc.publisherJMIR Publications
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectaftercare
dc.subjectartificial intelligence
dc.subjectcataract
dc.subjectcell phone
dc.subjectchatbot
dc.subjectconversational agent
dc.subjectdelivery of health care
dc.subjectdialogue system
dc.subjectexpert systems
dc.subjecthealth communication
dc.subjecthealth services
dc.subjectinternet
dc.subjectmedical informatics
dc.subjectmental health
dc.subjectnatural language processing
dc.subjectpatient acceptance of health care
dc.subjectrelational agent
dc.subjectspeech recognition software
dc.subjecttelemedicine
dc.titleSafety and acceptability of a natural-language AI assistant to deliver clinical follow-up to cataract surgery patients: Proposal for a pragmatic evaluation
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/34319248
plymouth.issue7
plymouth.volume10
plymouth.publication-statusPublished online
plymouth.journalJMIR Research Protocols
dc.identifier.doi10.2196/27227
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/School of Nursing and Midwifery
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeCanada
dcterms.dateAccepted2021-01-20
dc.rights.embargodate2021-8-27
dc.identifier.eissn1929-0748
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.2196/27227
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
rioxxterms.licenseref.startdate2021-07-28
rioxxterms.typeJournal Article/Review


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