Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
1.
Prog Retin Eye Res ; 82: 100900, 2021 05.
Article in English | MEDLINE | ID: covidwho-745955

ABSTRACT

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.


Subject(s)
Artificial Intelligence/trends , Digital Technology/methods , Eye Diseases/diagnosis , Eye Diseases/therapy , Ophthalmology/methods , Telemedicine/methods , COVID-19/epidemiology , Delivery of Health Care , Global Health , Humans , Inventions , SARS-CoV-2/pathogenicity
2.
J Med Internet Res ; 22(7): e19483, 2020 07 30.
Article in English | MEDLINE | ID: covidwho-658765

ABSTRACT

BACKGROUND: Timely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed. OBJECTIVE: We investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States. METHODS: We used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels. RESULTS: Predictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05. CONCLUSIONS: Identifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Population Surveillance/methods , Public Health Informatics/methods , Search Engine/trends , Algorithms , Betacoronavirus , COVID-19 , Humans , Internet , Models, Statistical , Pandemics , SARS-CoV-2 , United States
3.
Am J Ophthalmol ; 216: 237-242, 2020 08.
Article in English | MEDLINE | ID: covidwho-155116

ABSTRACT

PURPOSE: To discuss the effects of the severe acute respiratory syndrome coronavirus 2 betacoronavirus on ambulatory ophthalmology practices, the value proposition of telemedicine, teleophthalmology implementation methodologies, and the accelerated future of telemedicine. DESIGN: Review of the current telehealth landscape including usage, policies, and techniques for ambulatory practice integration. METHODS: We provide author-initiated review of recent trends in telehealth, governmental recommendations for health care delivery during the COVID-19 pandemic, and a PubMed Central query for telemedicine in ophthalmology or teleophthalmology. In addition, the authors' comprehensive experience in telemedicine design and implementation is provided. RESULTS: We provide a summary describing the present state of telehealth, teleophthalmology modeling, care delivery, and the proposed impact of telehealth surges on the future of ophthalmology practice. CONCLUSION: Recent patient and provider interest in telemedicine, the relaxation of regulatory restrictions, increased remote care reimbursement, and ongoing social distancing practices compel many ophthalmologists to consider virtualizing services.


Subject(s)
Betacoronavirus , COVID-19/epidemiology , Coronavirus Infections/epidemiology , Delivery of Health Care/organization & administration , Eye Diseases/therapy , Ophthalmology/organization & administration , Pneumonia, Viral/epidemiology , Telemedicine/organization & administration , User-Computer Interface , Ambulatory Care/organization & administration , COVID-19/prevention & control , Coronavirus Infections/prevention & control , Humans , Intraocular Pressure , New York/epidemiology , Pandemics , Pneumonia, Viral/prevention & control , Practice Guidelines as Topic , SARS-CoV-2 , Visual Acuity
SELECTION OF CITATIONS
SEARCH DETAIL