PurposeCataract is a major cause of age-related eye diseases in the United States, and cataract extraction is the most commonly performed surgery on Medicare beneficiaries. Analyzing the pattern in delivery of cataract care at the national level can highlight areas of disparities. We evaluated geographic disparities seen in cataract surgery delivery to Medicare beneficiaries in the United States.SettingCataract extractions across the United States in 2012.DesignCross-sectional study examining distance to provider and observed versus expected number of cataract extractions.MethodsCataract extraction current procedural terminology codes were used to sum the total observed number of cataract extractions per cataract surgeon. Epidemiology data on expected number of cataract surgeries in one year by decade of life were extrapolated via a Gaussian Process model. A linear regression model was used to compare differences in delivery of care between US economic regions.Results2.2 million patients underwent cataract surgery in the Medicare dataset in 2012. The average distance to the nearest provider was 9.846 miles (standard deviation: 11.410 miles). This distance was statistically significant (p < 2.0 x 10−22) in the New England (5.935 mi), Mideast (6.356 mi), Great Lakes (8.733 mi), Far West (9.038 mi), Southeast (9.793 mi), Southwest (12.711 mi), Plains (16.047 mi), and Rocky Mountain (17.934 mi) regions. The total number of expected cataract surgeries greater than 100 miles to the nearest cataract surgeon was 1,901, where Montana, South Dakota, and Texas each had over 200 of these expected distances.ConclusionsA large discrepancy exists in cataract delivery to the Medicare population based on geographic factors. Patients who live in rural areas travel farther on average to see ophthalmologists, resulting in a lower observed than expected rate of cataract surgery. Our results have implications in future allocation of resources and ophthalmologists.

PurposeWith increasing volumes of electronic health record data, algorithm-driven extraction may aid manual extraction. Visual acuity often is extracted manually in vision research. The total visual acuity extraction algorithm (TOVA) is presented and validated for automated extraction of visual acuity from free text, unstructured clinical notes.MethodsConsecutive inpatient ophthalmology notes over an 8-year period from the University of Washington healthcare system in Seattle, WA were used for validation of TOVA. The total visual acuity extraction algorithm applied natural language processing to recognize Snellen visual acuity in free text notes and assign laterality. The best corrected measurement was determined for each eye and converted to logMAR. The algorithm was validated against manual extraction of a subset of notes.ResultsA total of 6266 clinical records were obtained giving 12,452 data points. In a subset of 644 validated notes, comparison of manually extracted data versus TOVA output showed 95% concordance. Interrater reliability testing gave κ statistics of 0.94 (95% confidence interval [CI], 0.89–0.99), 0.96 (95% CI, 0.94–0.98), 0.95 (95% CI, 0.92–0.98), and 0.94 (95% CI, 0.90–0.98) for acuity numerators, denominators, adjustments, and signs, respectively. Pearson correlation coefficient was 0.983. Linear regression showed an R2 of 0.966 (P < 0.0001).ConclusionsThe total visual acuity extraction algorithm is a novel tool for extraction of visual acuity from free text, unstructured clinical notes and provides an open source method of data extraction.Translational RelevanceAutomated visual acuity extraction through natural language processing can be a valuable tool for data extraction from free text ophthalmology notes.

Background Self-reported questions on blindness and vision problems are collected in many national surveys. Recently released surveillance estimates on the prevalence of vision loss used self-reported data to predict variation in the prevalence of objectively measured acuity loss among population groups for whom examination data are not available. However, the validity of self-reported measures to predict prevalence and disparities in visual acuity has not been established. Objective This study aimed to estimate the diagnostic accuracy of self-reported vision loss measures compared to best-corrected visual acuity (BCVA), inform the design and selection of questions for future data collection, and identify the concordance between self-reported vision and measured acuity at the population level to support ongoing surveillance efforts. Methods We calculated accuracy and correlation between self-reported visual function versus BCVA at the individual and population level among patients from the University of Washington ophthalmology or optometry clinics with a prior eye examination, randomly oversampled for visual acuity loss or diagnosed eye diseases. Self-reported visual function was collected via telephone survey. BCVA was determined based on retrospective chart review. Diagnostic accuracy of questions at the person level was measured based on the area under the receiver operator curve (AUC), whereas population-level accuracy was determined based on correlation. Results The survey question, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” had the highest accuracy for identifying patients with blindness (BCVA ≤20/200; AUC=0.797). The highest accuracy for detecting any vision loss (BCVA <20/40) was achieved by responses of “fair,” “poor,” or “very poor” to the question, “At the present time, would you say your eyesight, with glasses or contact lenses if you wear them, is excellent, good, fair, poor, or very poor” (AUC=0.716). At the population level, the relative relationship between prevalence based on survey questions and BCVA remained stable for most demographic groups, with the only exceptions being groups with small sample sizes, and these differences were generally not significant. Conclusions Although survey questions are not considered to be sufficiently accurate to be used as a diagnostic test at the individual level, we did find relatively high levels of accuracy for some questions. At the population level, we found that the relative prevalence of the 2 most accurate survey questions were highly correlated with the prevalence of measured visual acuity loss among nearly all demographic groups. The results of this study suggest that self-reported vision questions fielded in national surveys are likely to yield an accurate and stable signal of vision loss across different population groups, although the actual measure of prevalence from these questions is not directly analogous to that of BCVA.

PurposeThe aim of this study was to compare physician preferences regarding the commercially available spectral-domain (SD) optical coherence tomography angiography (OCTA) and swept-source (SS) OCTA prototype device.DesignComparative analysis of diagnostic instruments was performed.Patients and methodsSubjects at the University of Washington Eye Institute and Harborview Medical Center were prospectively recruited and imaged with the Zeiss SD OCTA (HD-5000, Angioplex) and Zeiss SS OCTA (Plex Elite, Everest) devices on the same day. The study included 10 eyes from 10 subjects diagnosed with a retinal/choroidal disease. Deidentified images were compiled into a survey and sent to retina specialists in various countries. The survey presented masked SD and SS images of each eye for each retinal sublayer side by side. Respondents were asked about their image preference and impact on clinical management. A priori and post hoc preferences for SD vs SS were collected.ResultsFifty-four retina specialists responded to the survey. Median years in practice was 3.00 (interquartile range [IQR] 1.50–17.00). At baseline, 23 (48%) physicians owned an OCTA machine. The majority of physician responses showed a preference for the SS over SD OCTA, independent of the retinal pathology shown (n=454 overall responses, 74%). Nevertheless, the majority indicated that both SD and SS would be equally valuable in informing clinical decisions (n=374 overall responses, 61%).ConclusionThese findings indicate that the majority of retina specialists surveyed prefer SS over SD OCTA based on image quality, regardless of the retinal pathology shown. Regarding the clinical utility of each modality, the majority of physicians perceive SD and SS as equally effective.

ImportanceDiagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown.ObjectiveTo estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review.Design, Setting, and ParticipantsThis cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington–affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss.ExposuresPatients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review.Main Outcome and MeasuresAccuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans.ResultsAmong 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70).Conclusion and RelevanceIn this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.