EyeWorld India September 2020 Issue

EWAP SEPTEMBER 2020 23 SECONDARY FEATURE Level   Contextual factors Considerations for driving adoption Solution form (e.g. device, Technology) - User interface/experience (UI/UX e.g. for patient, provider) - Scientific basis for the solution - Accessibility, material cost, technology platform - Patient acceptance, willingness to use 45 - Review of problem, solution and epidemiology (“market size”) 2 - Affordability, social determinants of health 45 Microsystem (e.g. ward, spe- cialist outpatient service) - Interaction with the existing clinical care pathway (overlay, augment, replace, etc) - Ethics and medicolegal constraints - Provider acceptance (e.g., relevant stakeholders such as clinicians, nurses) 54 - Pilot validation studies (e.g. clinical outcomes/ performance) 40,54 - Guidelines for use, Liability for negative outcomes, Safety net mechanisms 53 Mesosystem (e.g. hospital, “hub and spoke”) - Supporting resources and system - Feedback loops for sustained adoption/ extension - Quality of infrastructure (e.g. electricity, internet, security) 14 - Mechanisms to review/share results about solution performance and optimisation (e.g. M&M, QI projects) 2 Macrosystem (e.g. popula- tion screening program) - Culture, staff availability, and incentives - Policy, financing and regulatory environment - Alignment of human capacity (e.g. operators, support staff) 54 - Health economic assessment (HEA), Health technology assessment (HTA), Pre-market approval (PMA) 53 Table 2. System level approach to address barriers for adoption of a digital health innovation. that new digital models are usually driven not just by the technology, but by clinical needs, demand from patients, manpower availability and various stakeholders’ priorities. Adoption and scale requires addressing concerns in culture, communications, generating capacity, and aligning incentives of stakeholders. Before COVID-19, in “normal” healthcare, most digital models may begin with a small pilot study with low operational impact. Based on the results of such pilots, and traction with clinical demand from patients, such digital models may then slowly be adopted. However, the COVID-19 pandemic has made this “pilot and evaluate” paradigm obsolete. Faster, broader, and bolder attempts at developing and adopting new digital models of ophthalmology care are clearly needed. EWAP References 1. Tuckson RV, et al. Telehealth. N Engl J Med 2017;377(16):1585-1592. 2. Faes L, et al. Home monitoring as a useful extension of modern tele- ophthalmology. Eye (Lond). 2020. 3. Ting DSW, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol. 2019;103(2):167-175. 4. Bellemo V, et al. Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application. Curr Diab Rep. 2019;19(9):72. 5. Yuchen Xie, et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digital Health. 2(5):E240-249. In. 6. Rasti R, et al. Deep learning-based single-shot prediction of differential effects of anti-VEGF treatment in patients with diabetic macular edema. Biomed Opt Express. 2020;11(2):1139-1152. 7. Olivia Li JP, et al. Preparedness among Ophthalmologists: During and Beyond the COVID-19 Pandemic. Ophthalmology. 2020;127(5):569-572. 8. Bibault JE, et al. Healthcare ex Machina: Are conversational agents ready for prime time in oncology? Clin Transl Radiat Oncol. 2019;16:55-59. 9. Laranjo L, et al. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc. 2018;25(9):1248-1258. 10. Ting DSW, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211-2223. 11. Zhongshan Ophthalmic Center (ZOC). ZOC internet hospital: serve the patients with ophthalmic diseases nationwide to help fight against COVID-19. URL: http://english. gzzoc.com/nev/news/202004/ t20200428_166982.html (Accessed 7 Jan 2020). In. 12. Ting DS, et al. Next generation telemedicine platforms to screen and triage. Br J Ophthalmol. 2019. 13. Wong JKW, et al. Tele- ophthalmology amid COVID-19 pandemic-Hong Kong experience. Graefes Arch Clin Exp Ophthalmol. 2020. 14. Straits Times. National eye centre launches telemedicine for glaucoma patients. URL:  https://www.straitstimes. com/singapore/health/national-eye- centre-launches-telemedicine-for- glaucoma-patients (Accessed 10 July 2020). In. 15. Schmid MK, et al. Reliability and diagnostic performance of a novel mobile app for hyperacuity self- monitoring in patients with age-related macular degeneration. Eye (Lond). 2019;33(10):1584-1589. 16. Bellemo V, et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. Lancet Digital Health. 2 019 May 1;1(1):e35–44. In. 17. Dixon-Woods M, et al. Problems and promises of innovation: why healthcare needs to rethink its love/hate relationship with the new. BMJ Qual Saf. 2011;20 Suppl 1:i47-51. 18. Stanberry B. Telemedicine: barriers and opportunities in the 21st century. J Intern Med. 2000;247(6):615-628. Editors’ note: Prof. Wong is affiliated with the Singapore Eye Research institute, Singapore National Eye Centre, Singapore, and Duke-NUS Medical School, National University of Singapore (NUS), Singapore. He is a consultant and member of the advisory board for Allergan, Bayer, Boehringer-Ingelheim, Genentech, Merck, Novartis, Oxurion (formerly ThromboGenics), and Roche, and co-founder of plano and EyRiS.