Title: A mixed methods validation of technology enhanced macula services
Funder: Department of Health and Social Care

Abstract: Background Ophthalmology's lead as NHS England's busiest outpatient specialty is growing, with 9 million 2018/19 appointments (8.2%). The increasing prevalence of exudative age-related macular degeneration (exAMD) has fed this growth. People with exAMD visit clinic every 4-16 weeks for intravitreal injections (IVI). If IVIs are too infrequent patients irreversibly lose vision. Already, observed IVI intervals are often longer than intended, due to inadequate ophthalmologist availability. Consequently, in 2018 the Royal College of Ophthalmologists requested 22% more consultant ophthalmologists. A pre-existent artificial intelligence (AI) package could remove this need through its potential to independently direct treatment. However, the absence of data informing NHS adoption of this AI perpetuates its pre-clinical status and deprives patients from its benefits. Aim To validate pre-existent AI for exAMD treatment planning and establish how it can be adopted to improve NHS service capacity and quality. Objectives Synthesise qualitative evidence for AI adoption in all healthcare settings and inform the design of primary qualitative research targeting specified healthcare niches including exAMD services. Inform the placement of AI within the exAMD clinical pathway and disseminate barriers and facilitators to its adoption. Validate a pre-existent AI package's treatment planning performance relative to real-world exAMD care. Establish the consequences to patient care of using alternative strategies to address the demand-capacity mismatch in exAMD services; telemedicine and clinician diversification.Methods Qualitative evidence synthesis of research examining the adoption of automated healthcare decision aids from clinician, patient and carer perspectives. Semi-structured interviews with patients, carers, hospital ophthalmic professionals, community optometrists and NHS commissioners and managers will be conducted to explore perceptions of how AI should fit into the exAMD clinical pathway and the barriers and facilitators to its adoption. Imaging and clinical data archived from 384 exAMD patient visits will be identified in the electronic medical record. A single AI package will analyse imaging to recommend a treatment interval for each visit. Medical retina consultants will independently review imaging and clinical data to recommend a gold standard IVI interval for the same visits. The disagreement of both the real-world and hypothetical AI IVI intervals with these gold standards will be calculated, then AI performance will be tested against the real-world for non-inferiority. Consultants will undertake blinded telemedical review of their own archived face-to-face consultations, creating paired decisions for comparison. Seniority and profession of clinicians making real-world clinic decisions will be analysed using Bland-Altman plots to define any impact on patient treatment.Impact Personal: I will develop the ability to deliver patient benefit from AI advancements, through elevating my career trajectory towards translational clinical data science. Patient: Hundreds of thousands of people depending on injections to keep their sight will be able to access timely and convenient treatment sooner. Society: Outside of exAMD services, the public will benefit, as previously committed human and financial NHS resources are freed for other unmet healthcare needs and a route to AI adoption is set to lead innovation in other NHS services.

Lay abstract: Age related macular degeneration (AMD) affects older people and is the most common cause of sight loss in the UK. Sight loss can occur over days when blood vessels at the back of the eye leak, known as exudative AMD (exAMD). Courses of painless eye injections help people with exAMD keep their sight for years longer, but rising needs for treatment and a shortage of eye doctors in the NHS means that people may not always get the injections they need. This project aims to see if existing technology can be used safely in macula clinics to prevent sight threatening treatment delays. When planning this research I spoke to exAMD patients and those close to them. I learnt about the their concerns about delayed appointments and slow congested clinics. One solution to this is stopping people having to wait for doctor consultations before each injection, as nurses give most injections. New software already known to make safe referral decisions for exAMD could make this possible, by supporting doctors making treatment decisions. We could think of these safe and efficient clinics as Technology Enhanced Macula Services (TEMS). In TEMS, known exAMD patients would get clinical photos in hospital or high-street opticians, from which the software would judge their need for injections. As a result, patients would not need to see eye doctors so often and when they did, there would be more time to explore each individual's needs. Patient groups with whom I discussed the research project liked the idea, but they agreed safety must be proven before people can use it. We also need to know more about how patients, clinicians and managers see this software fitting into NHS macular services. First, I will bring together research on views about technology assisted healthcare similar to TEMS. I will explain this evidence to exAMD patients and their carers, to see what they think about it and how it fits with their ideas about TEMS. The public have already helped plan this study through meetings arranged by the Macula Society, Vision Support Newcastle and the NIHR. They taught me that offering to contact people from routine clinic visits at home will make it easier for them to contribute their opinions. I will also collect views on TEMS from different NHS professionals in exAMD services. Throughout the project, an exAMD service user and their carer have agreed to help me pull these ideas together to see what place software should take on the patient journey and how best to make TEMS a reality. To prove TEMS' safety I will find past clinic notes, where people had eye photos and injections. The software will review these photos and say what injection plans it would have made. Consultants who lead exAMD care, will also give their opinion to define a gold standard of care. By comparing what really happened in our clinic with what the software would have done, TEMS can be tested against current care. Testing current care against consultant opinion will also check our exAMD clinics are coping with demands on the service. If TEMS are proven to be safe, real patient care can start to benefit, guided by the ideas of the patients and professionals that this study reports. If the software isn't ready yet, I will tell industry what needs to change. Whatever the outcome, I am planning an event for AMD patients and their carers in the North-East to let them know. This work will allow patient benefit from TEMS to become a reality, not just a theory, and it will pave the way for technology enhancement across NHS services.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: EYE