Title: Tele-opHthalmology-enablEd and ARtificial Intelligence-ready referral pathway for coMmunity optomEtry referralS of retinal disease: the HERMES study - a Cluster Randomised Superiority Trial with a linked Observational Diagnostic Accuracy Study
Funder: Department of Health and Social Care

Abstract: One in ten of all patients referred to hospitals in the UK are for problems involving the eyes. Many of the most urgent referrals are for problems in the retina, a light-sensitive layer at the back of the eye which allows us to see. We can now detect and diagnose these diseases earlier than ever before thanks to a technology called Optical Coherence Tomography (OCT). This light-based technology is safe, comfortable and quick._x000D_ _x000D_ Excitingly, OCT is increasingly being installed in high street optometry practices, and is no longer limited to hospital eye clinics. This could transform the way we detect patients with retinal diseases, but only if these scans are accurately interpreted enabling early diagnosis and correct decisions over who should be referred to the hospital eye service, and how urgently. Currently however the introduction of this new technology on the high street is not always matched by the availability of those skilled at interpreting these scans. This is leading to a large number of patients being referred inappropriately, which is increasing the pressure on hospital eye services and delaying access to care for patients that do need treatment. Additionally unnecessary referral causes distress and inconvenience._x000D_ _x000D_ This study aims to use new technologies to improve this referral process, moving hospital-level expertise to the high street without the specialist needing to leave the hospital, and helping hospitals and high street optometrists to work together to refer the ‘right patient at the right time’. _x000D_ _x000D_ The first technology we will evaluate is ‘tele-ophthalmology’ in which OCT scans taken by high street optometrists are reviewed by hospital specialists remotely. In this part of the study high street optometrists with OCT will be divided into two groups: half of the practices, selected by chance, will continue to refer patients using the existing paper-based system, with the other half installing a leading tele-ophthalmology platform (‘Big Picture’) to allow instant transfer of scans to the eye hospitals for review and advice within 24 hours. We will involve 500 patients overall in this part of the study. _x000D_ _x000D_ We will then check whether the new referral system can safely lead to fewer unnecessary visits to the eye hospitals and whether it improves the time it takes for referred patients to be seen or treated. We will also assess the cost of the new system and ask what patients and health practitioners think about it -confidence in its safety and data privacy, and effect on patient experience._x000D_ _x000D_ The second technology we will evaluate is interpretation of retinal scans by ‘Artificial Intelligence’ (AI). In a recent study from the UK, members of our team and a leading technology company worked together to develop an AI algorithm that can diagnose retinal diseases and provide referral advice to the same standard as leading UK experts. This exciting development could enable expert-level care to be digitally embedded into this referral pathway but first we need further evidence of how this AI technology would perform in the real world. _x000D_ _x000D_ In this part of our study, we will use AI (‘the Moorfields-DeepMind algorithm’) on all the OCT scans collected from our participating high street optometrists and we will assess how accurate it is in providing the correct advice for referral. A key part of this study is to collect opinions of patients and practitioners on the potential role of Artificial Intelligence for eye referrals.

Lay abstract: Research Question: Can we improve the care of patients with sight-threatening retinal disease by using new technologies – tele-ophthalmology and artificial intelligence (AI) - to disseminate expert care from the hospital to the high-street? _x000D_ Specifically we will ask:_x000D_ 1. Is timely and appropriate review for patients with retinal disease improved by embedding tele-ophthalmology technologies in high-street optometrist practices._x000D_ 2. Can this remote review be safely performed by an AI Decision Support System (AI DSS) previously validated in a hospital setting (Moorfields-DeepMind algorithm)._x000D_ 3. What are the barriers and enablers to the adoption of these digital technologies._x000D_ _x000D_ Background: Eye disease accounts for 10% of all outpatient activity in the United Kingdom (UK). Many of the most urgent referrals are for problems in the retina, with retinal disease (including macular degeneration) being the leading cause of blindness in the UK. Effective treatment is available for many retinal diseases but outcome is dependent on early triage, diagnosis and access. _x000D_ _x000D_ Early diagnosis of retinal disease depends on the imaging technology called Optical Coherence Tomography (OCT). OCT is increasingly available in high street optometrist practices but is not accompanied by hospital-level expertise in interpreting these scans. This is leading to a large number of patients being referred inappropriately, increasing the pressure on hospital eye services and delaying access to care for patients that do need treatment. _x000D_ _x000D_ Primary Objectives: To compare the proportion of referrals classified as unnecessary between standard care and tele-ophthalmology referral pathway (objective 1) and to assess the diagnostic accuracy of AI DSS in the context of this referral pathway (objective 2). Enablers and barriers to adoption of these digital technologies will be assessed through a Human Computer Interaction (HCI) analysis. A full economic analysis of the digital referral pathways will be performed._x000D_ _x000D_ Methods: Addressing objective 1, an interventional superiority cluster randomised trial (RCT) will be performed comparing standard practice for referral of suspicious retinal disease with tele-ophthalmology (Big Picture platform) between community optometry and HES. Addressing objective 2 a prospective observational study will be conducted integrating the data of the RCT to assess the diagnostic accuracy of an advanced AI DSS (the DeepMind algorithm) for the automated diagnosis and referral of retinal disease. A within trial and model based economic evaluation will estimate the efficiency of alternative referral models for retinal disease. A HCI analysis using qualitative methods will assess feasibility of implementation of both digital technologies. _x000D_ _x000D_ Timelines: Months 0 to 6; Development phase, Months 7 to 30; Patient recruitment and data collection, Months 30 to 36; Data analysis, Economic and HCI analysis_x000D_ _x000D_ Anticipated impact and dissemination: This research project is expected to optimise the interface between high street optometry and hospital-based eye clinics by comparing the effectiveness and efficiency of 2 advanced digital technologies (cloud-based tele-ophthalmology and Artificial Intelligence DSS) with the standard non-digital referral pathway. Dissemination will take place through scientific publications and presentations, the ophthalmic charities and NHS commissioning bodies.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: EYE