Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care

Kern, Christoph; Fu, Dun Jack; Kortuem, Karsten; Huemer, Josef; Barker, David; Davis, Alison; Balaskas, Konstantinos; Keane, Pearse A.; McKinnon, Tom und Sim, Dawn A. (2020): Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care. In: British Journal of Ophthalmology, Bd. 104, Nr. 3: S. 312-317

Abstract

Background: Hospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The National Health Service (NHS) published a 10-year strategy (NHS Long-Term Plan) to transform services to meet this challenge. In this study, we implemented a cloud-based referral platform to improve communication between optometrists and ophthalmologists. Methods Retrospective cohort study conducted at Moorfields Eye Hospital, Croydon (NHS Foundation Trust, London, UK). Patients classified into the HES referral pathway by contributing optometrists have been included into this study. Main outcome measures was the reduction of unnecessary referrals. Results After reviewing the patient's data in a web-based interface 54 (52%) out of 103 attending patients initially classified into the referral pathway did not need a specialist referral. Fourteen (14%) patients needing urgent treatment were identified. Usability was measured in duration for data input and reviewing which was an average of 9.2 min (median: 5.4;IQR: 3.4-8.7) for optometrists and 3.0 min (median: 3.0;IQR: 1.7-3.9) min for ophthalmologists. A variety of diagnosis was covered by this tool with dry age-related macular degeneration (n=34) being most common. Conclusion After implementation more than half of the HES referrals have been avoided. This platform offers a digital-first solution that enables rapid-access eye care for patients in community optometrists, facilitates communication between healthcare providers and may serve as a foundation for implementation of artificial intelligence.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0007-1161
• Sprache: Englisch