Tele-Neuro-Ophthalmology Utilization, Availability, and Attitudes: Update 1 Year Into the COVID-19 Public Health Emergency

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department

PURPOSE

The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system on nonmydriatic fundus photographs from the FOTO-ED studies to determine if the deep learning system could have improved the detection of papilledema had it been available to ED providers as a real-time diagnostic aid.

DESIGN

Retrospective secondary analysis of a cohort of patients included in the FOTO-ED studies.

METHODS

The testing data set included 1608 photographs obtained from 828 patients in the FOTO-ED studies. Photographs were reclassified according to the optic disc classification system used by the deep learning system ("normal optic discs," "papilledema," and "other optic disc abnormalities"). The system's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 1-vs-rest strategy, with reference to expert neuro-ophthalmologists.

RESULTS

The BONSAI deep learning system successfully distinguished normal from abnormal optic discs (AUC 0.92 [95% confidence interval {CI} 0.90-0.93]; sensitivity 75.6% [73.7%-77.5%] and specificity 89.6% [86.3%-92.8%]), and papilledema from normal and others (AUC 0.97 [0.95-0.99]; sensitivity 84.0% [75.0%-92.6%] and specificity 98.9% [98.5%-99.4%]). Six patients with missed papilledema in 1 eye were correctly identified by the deep learning system as having papilledema in the other eye.

CONCLUSIONS

The BONSAI deep learning system was able to reliably identify papilledema and normal optic discs on nonmydriatic photographs obtained in the FOTO-ED studies. Our deep learning system has excellent potential as a diagnostic aid in EDs and non-ophthalmology clinics equipped with nonmydriatic fundus cameras. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Evolution of Disparities in Outpatient Ophthalmic Care at a Tertiary Care Center in California at the Beginning of and One Year into the COVID-19 Public Health Emergency

PURPOSE

To compare disparities in outpatient ophthalmic care during early and later periods of the COVID-19 public health emergency.

METHODS

This cross-sectional study compared non-peri-operative outpatient ophthalmology visits by unique patients at an adult ophthalmology practice affiliated with a tertiary-care academic medical center in the Western US during three time periods: pre-COVID (3/15/19-4/15/19), early-COVID (3/15/20-4/15/20), and late-COVID (3/15/21-4/15/21). Differences in participant demographics, barriers to care, visit modality (telehealth, in person), and subspeciality of care were studied using unadjusted and adjusted models.

RESULTS

There were 3095, 1172 and 3338 unique patient-visits during pre-COVID, early-COVID and late-COVID (overall age 59.5 ± 20.5 years, 57% female, 41.8% White, 25.9% Asian, 16.1% Hispanic). There were disparities in patient age (55.4 ± 21.8 vs. 60.2 ± 19.9 years), race (21.9% vs. 26.9% Asian), ethnicity (18.3% Hispanic vs. 15.2% Hispanic), and insurance (35.9% vs. 45.1% Medicare) as well as changes in modality (14.2% vs. 0% telehealth) and subspecialty (61.6% vs. 70.1% internal exam specialty) in early-COVID vs. pre-COVID (p < .05 for all). In late-COVID, only insurance (42.7% vs. 45.1% Medicare) and modality of care (1.8% vs. 0% telehealth) persisted as differences compared to pre-COVID.

CONCLUSIONS

There were disparities in patients receiving outpatient ophthalmology care during early-COVID that returned close to pre-COVID baseline one year later. These results suggest that there has not been a lasting positive or negative disruptive effect of the COVID-19 pandemic on disparities in outpatient ophthalmic care.

Healthcare utilization impacts of an eConsult program for headache at an academic medical center

INTRODUCTION

Interprofessional consultations ("eConsults") can reduce healthcare utilization. However, the impact of eConsults on healthcare utilization remains poorly characterized among patients with headache.

METHODS

We performed a retrospective, 1:1 matched cohort study comparing patients evaluated for headache via eConsult request or in-person referral at the Mount Sinai Health System in New York. Groups were matched on clinical and demographic characteristics. Our primary outcome was one or more outpatient headache-related encounters in 6 months following referral date. Secondary outcomes included one or more all-cause outpatient neurology and headache-related emergency department (ED) encounters during the same period. We used univariable and multivariable logistic regression to model associations between independent variables and outcomes.

RESULTS

We identified 74 patients with headache eConsults who were matched to 74 patients with in-person referrals. Patients in the eConsult group were less likely to achieve the primary outcome (29.7% vs 62.2%, P < 0.0001) or have an all-cause outpatient neurology encounter (33.8% vs 79.7%, P < 0.0001) than patients in the comparison group. Both groups did not significantly differ by headache-related ED encounters. In multivariable analyses, patients in the eConsult group had significantly lower odds of having one or more headache-related or all-cause neurology encounters than patients in the comparison group (odds ratio (OR) 0.3, 95% confidence interval (CI) 0.1-0.6; OR 0.1, 95% CI 0.1-0.3, respectively).

DISCUSSION

In comparison to in-person referrals, eConsult requests for headache were associated with reduced likelihood of outpatient neurology encounters in the short-term but not with differential use of headache-related ED encounters. Larger-scale, prospective studies should validate our findings and assess patient outcomes.

Diagnosis of Optic Disc Oedema: Fundus Features, Ocular Imaging Findings, and Artificial Intelligence

Optic disc swelling is a manifestation of a broad range of processes affecting the optic nerve head and/or the anterior segment of the optic nerve. Accurately diagnosing optic disc oedema, grading its severity, and recognising its cause, is crucial in order to treat patients in a timely manner and limit vision loss. Some ocular fundus features, in light of a patient's history and visual symptoms, may suggest a specific mechanism or aetiology of the visible disc oedema, but current criteria can at most enable an educated guess as to the most likely cause. In many cases only the clinical evolution and ancillary testing can inform the exact diagnosis. The development of ocular fundus imaging, including colour fundus photography, fluorescein angiography, optical coherence tomography, and multimodal imaging, has provided assistance in quantifying swelling, distinguishing true optic disc oedema from pseudo-optic disc oedema, and differentiating among the numerous causes of acute optic disc oedema. However, the diagnosis of disc oedema is often delayed or not made in busy emergency departments and outpatient neurology clinics. Indeed, most non-eye care providers are not able to accurately perform ocular fundus examination, increasing the risk of diagnostic errors in acute neurological settings. The implementation of non-mydriatic fundus photography and artificial intelligence technology in the diagnostic process addresses these important gaps in clinical practice.

Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?

PURPOSE OF THE REVIEW

Neuro-ophthalmologists rapidly adopted telehealth during the COVID-19 pandemic to minimize disruption to patient care. This article reviews recent research on tele-neuro-ophthalmology adoption, current limitations, and potential use beyond the pandemic. The review considers how digital transformation, including machine learning and augmented reality, may be applied to future iterations of tele-neuro-ophthalmology.

RECENT FINDINGS

Telehealth utilization has been sustained among neuro-ophthalmologists throughout the pandemic. Adoption of tele-neuro-ophthalmology may provide solutions to subspecialty workforce shortage, patient access, physician wellness, and trainee educational needs within the field of neuro-ophthalmology. Digital transformation technologies have the potential to augment tele-neuro-ophthalmology care delivery by providing automated workflow solutions, home-based visual testing and therapies, and trainee education via simulators. Tele-neuro-ophthalmology use has and will continue beyond the COVID-19 pandemic. Digital transformation technologies, when applied to telehealth, will drive and revolutionize the next phase of tele-neuro-ophthalmology adoption and use in the years to come.