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• W4288432615 abstract "<h3>Introduction</h3> Novel teleophthalmology technologies have the potential to reduce unnecessary and inaccurate referrals between community optometry practices and hospital eye services and as a result improve patients’ access to appropriate and timely eye care. However, little is known about the acceptability and facilitators and barriers to the implementations of these technologies in real life. <h3>Methods and analysis</h3> A theoretically informed, qualitative study will explore patients’ and healthcare professionals’ perspectives on teleophthalmology and Artificial Intelligence Decision Support System models of care. A combination of situated observations in community optometry practices and hospital eye services, semistructured qualitative interviews with patients and healthcare professionals and self-audiorecordings of healthcare professionals will be conducted. Participants will be purposively selected from 4 to 5 hospital eye services and 6–8 affiliated community optometry practices. The aim will be to recruit 30–36 patients and 30 healthcare professionals from hospital eye services and community optometry practices. All interviews will be audiorecorded, with participants’ permission, and transcribed verbatim. Data from interviews, observations and self-audiorecordings will be analysed thematically and will be informed by normalisation process theory and an inductive approach. <h3>Ethics and dissemination</h3> Ethical approval has been received from London-Bromley research ethics committee. Findings will be reported through academic journals and conferences in ophthalmology, health services research, management studies and human-computer interaction." @default.
• W4288432615 created "2022-07-29" @default.
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• W4288432615 date "2022-07-28" @default.
• W4288432615 modified "2023-10-18" @default.
• W4288432615 title "Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging" @default.
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• W4288432615 doi "https://doi.org/10.3174/ajnr.a7589" @default.
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