Gender Representation Among Ophthalmology Fellowship Directors in 2022.

PURPOSE: Women are underrepresented in several medical specialties, including ophthalmology. Reducing disparities is critical in diversifying perspectives and increasing equity within ophthalmology, both of which can ultimately improve care delivery. We examined ophthalmic fellowship programs directors in the United States to investigate gender disparities by subspecialty. DESIGN: Cross-sectional study. METHODS: This was a retrospective cross-sectional study of ophthalmology fellowship program directors in academic medical centers. The primary outcome measure was a descriptive analysis of current fellowship directors in 2022 when stratified by subspecialty and demographic features. RESULTS: Analysis was conducted on 358 fellowship directors in the United States. Twenty-nine percent of directors were women. Female directors had significantly fewer years since residency graduation compared with male peers (17 vs 24; P < .001); however, no differences were observed by program type (P = .896) or location (P = 0.104). Differences in female director representation were observed by subspecialty (P < .001), with the greatest percentage of women in pediatric ophthalmology (54%), other (oncology and pathology) fellowships (50%), and medical retina (40%). The subspecialties with the lowest percentage of female directors were oculoplastic and reconstructive surgery (13%) surgical retina and vitreous (16%). CONCLUSION: There are disparities in female representation in academic leadership positions across ophthalmic subspecialties. Addressing this difference may have critical impacts on career advancement and opportunities available for marginalized groups in medicine.

Application of Patient Sentiment Analysis to Evaluate Glaucoma Care.

PURPOSE: Patients utilize online physician reviews to decide between and rate ophthalmologists. Sentiment analysis allows for better understanding of patient experiences. In this study, Valence Aware Dictionary sEntiment Reasoner (VADER) and word frequency analysis of glaucoma specialist Healthgrades reviews were used to determine factors prioritized by patients. DESIGN: Retrospective cross-sectional analysis. PARTICIPANTS: N/A. METHODS: Written reviews and Star ratings of glaucoma specialists listed under the Physicians Payments Sunshine Acts were obtained, and demographic information was collected. Valence Aware Dictionary sEntiment Reasoner produced Negative, Neutral, Positive, and Compound scores of reviews, and these were stratified by demographic variables. Word frequency review was applied to determine popular words and phrases. MAIN OUTCOME MEASURES: Star ratings, VADER Compound score of written reviews, and highest word frequencies. RESULTS: A total of 203 glaucoma specialists and 3531 written reviews were assessed. Glaucoma specialists had an average of 4.26/5 stars, with a mean of 30 ratings per physician on Healthgrades. Most physicians (86%) had overall Positive written reviews (VADER = 0.74), indicating high patient satisfaction. Specialists who were women or had fewer years of practice had higher Compound and Star scores than their respective male and senior counterparts, with statistical significance observed between junior and senior physician Stars (P < 0.001). Repeated words pertaining to the surgery, staff, wait times, and questions were common overall and among the most positive and most negative reviews. CONCLUSIONS: Glaucoma specialist patients value nonclinical factors, such as appointment setting and nonphysician health-care staff members, in their written reviews. Thus, factors beyond clinical outcomes are influential in the overall patient experience and should be considered to improve health-care delivery. These results can also advise ophthalmologists on factors that patients prioritize when evaluating physicians, which influences the decisions of other patients seeking glaucoma care. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Implementation of deep learning artificial intelligence in vision-threatening disease screenings for an underserved community during COVID-19.

INTRODUCTION: Age-related macular degeneration, diabetic retinopathy, and glaucoma are vision-threatening diseases that are leading causes of vision loss. Many studies have validated deep learning artificial intelligence for image-based diagnosis of vision-threatening diseases. Our study prospectively investigated deep learning artificial intelligence applications in student-run non-mydriatic screenings for an underserved, primarily Hispanic community during COVID-19. METHODS: Five supervised student-run community screenings were held in West New York, New Jersey. Participants underwent non-mydriatic 45-degree retinal imaging by medical students. Images were uploaded to a cloud-based deep learning artificial intelligence for vision-threatening disease referral. An on-site tele-ophthalmology grader and remote clinical ophthalmologist graded images, with adjudication by a senior ophthalmologist to establish the gold standard diagnosis, which was used to assess the performance of deep learning artificial intelligence. RESULTS: A total of 385 eyes from 195 screening participants were included (mean age 52.43 ± 14.5 years, 40.0% female). A total of 48 participants were referred for at least one vision-threatening disease. Deep learning artificial intelligence marked 150/385 (38.9%) eyes as ungradable, compared to 10/385 (2.6%) ungradable as per the human gold standard (p < 0.001). Deep learning artificial intelligence had 63.2% sensitivity, 94.5% specificity, 32.0% positive predictive value, and 98.4% negative predictive value in vision-threatening disease referrals. Deep learning artificial intelligence successfully referred all 4 eyes with multiple vision-threatening diseases. Deep learning artificial intelligence graded images (35.6 ± 13.3 s) faster than the tele-ophthalmology grader (129 ± 41.0) and clinical ophthalmologist (68 ± 21.9, p < 0.001). DISCUSSION: Deep learning artificial intelligence can increase the efficiency and accessibility of vision-threatening disease screenings, particularly in underserved communities. Deep learning artificial intelligence should be adaptable to different environments. Consideration should be given to how deep learning artificial intelligence can best be utilized in a real-world application, whether in computer-aided or autonomous diagnosis.