EyeWorld Korea 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 표 2. 디지털 보건 혁신 채택에 지장을 주는 장애물을 다루기 위한 시스템 수준의 접근 공동체는 디지털 서비스 활용의 고충점과 잠재적인 장애물들을 명확하게 다루고, 이를 채택하기 위한 분명한 계획을 채택하고 디자인해야 할 필요가 있다. 새로운 디지털 모델은 보통 단순히 기술에 의해 구동되는 것이 아닌, 임상적 필요성, 환자들의 요구, 인력, 그리고 다양한 이해 당사자들의 우선순위에 의해 구동된다고 강조함이 중요하다. 채택과 그 규모를 위해서는 문화, 의사소통, 생산 능력 및 이해당사자들의 인센티브 조정을 해결해야 한다. COVID-19 가 발병하기 전이라면, “정상적” 의료에서 대부분의 디지털 모델은 운영적인 영향을 적게 받으면서 시험 연구로 시작할 수 있다. 그러한 소규모 연구 결과와 환자의 임상적 요구와 관심을 바탕으로, 천천히 디지털 모델을 채택할 수 있었다. 그러나, COVID-19 사태가 이러한 “시험연구 후 평가” 패러다임을 구식으로 만들었다. 안과진료의 새로운 디지털 모델의 개발과 채택을 위한 더 빠르며, 폭 넓고, 과감한 시도가 분명 필요하다. 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. 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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.