Abstract

There is growing demand for emergency-based eyecare services where the majority of those attending do not require urgent ophthalmic management. The Royal College of Ophthalmologists have recommended upskilling and supporting of allied health professionals to support eyecare delivery, where machine learning algorithms could help. A mixed methods study was conducted to evaluate the usability of an artificial intelligence (AI) powered online triage platform for ophthalmology. The interface, usability, safety and acceptability were investigated using a Think Aloud interview and usability questionnaires. Twenty participants who actively examine patients in ophthalmic triage within a tertiary eye centre or primary care setting completed the interview and questionnaires. 90% or more of participants found the platform easy to use, reflected their triage process and were able to interpret the triage outcome, 85% found it safe to use and 95% felt the processing time was fast. A quarter of clinicians reported that they have experienced some uncertainty when triaging in their career and were unsure of using AI, after this study 95% of clinicians were willing to use the platform in their clinical workflow. This study showed the platform interface was acceptable and usable for clinicians actively working in ophthalmic emergency triage.

Details

Title
Usability of an artificially intelligence-powered triage platform for adult ophthalmic emergencies: a mixed methods study
Author
Jindal, Anish 1 ; Sumodhee, Dayyanah 2 ; Brandao-de-Resende, Camilo 3 ; Melo, Mariane 4 ; Neo, Yan Ning 2 ; Lee, Elsa 2 ; Day, Alexander C. 5 

 Moorfields Eye Hospital NHS Foundation Trust, London, UK (GRID:grid.436474.6) (ISNI:0000 0000 9168 0080); University College London, Department of Brain Sciences, Institute of Ophthalmology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201) 
 Moorfields Eye Hospital NHS Foundation Trust, London, UK (GRID:grid.436474.6) (ISNI:0000 0000 9168 0080) 
 University College London, Department of Brain Sciences, Institute of Ophthalmology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201); Moorfields Eye Hospital, NIHR Moorfields Clinical Research Facility, London, UK (GRID:grid.439257.e) (ISNI:0000 0000 8726 5837) 
 Moorfields Eye Hospital, NIHR Moorfields Clinical Research Facility, London, UK (GRID:grid.439257.e) (ISNI:0000 0000 8726 5837) 
 Moorfields Eye Hospital NHS Foundation Trust, London, UK (GRID:grid.436474.6) (ISNI:0000 0000 9168 0080); University College London, Department of Brain Sciences, Institute of Ophthalmology, London, UK (GRID:grid.83440.3b) (ISNI:0000 0001 2190 1201); Moorfields Eye Hospital, NIHR Moorfields Clinical Research Facility, London, UK (GRID:grid.439257.e) (ISNI:0000 0000 8726 5837) 
Pages
22490
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2903150244
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.