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dc.contributor.authorde Pennington, Nen
dc.contributor.authorMole, Gen
dc.contributor.authorLim, Een
dc.contributor.authorMilne-Ives, Men
dc.contributor.authorNormando, Een
dc.contributor.authorXue, Ken
dc.contributor.authorMeinert, Een
dc.date.accessioned2021-01-21T10:18:52Z
dc.identifier.issn1929-0748en
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.

en
dc.language.isoenen
dc.publisherJMIR Publicationsen
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.titleSafety and acceptability of a natural-language AI assistant to deliver clinical follow-up to cataract surgery patients: Proposal for a pragmatic evaluationen
dc.typeJournal Article
plymouth.journalJMIR Research Protocolsen
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/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2021-01-20en
dc.rights.embargodate9999-12-31en
dc.rights.embargoperiodNot knownen
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en
rioxxterms.typeJournal Article/Reviewen


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