Barriers and Facilitators to Teledermatology and Tele-Eye Care in Department of Veterans Affairs Provider Settings: Qualitative Content Analysis.

BACKGROUND: Veterans Affairs health care systems have been early adopters of asynchronous telemedicine to provide access to timely and high-quality specialty care services in primary care settings for veterans living in rural areas. Scant research has examined how to expand primary care team members' engagement in telespecialty care. OBJECTIVE: This qualitative study aimed to explore implementation process barriers and facilitators to using asynchronous telespecialty care (teledermatology and tele-eye care services). METHODS: In total, 30 participants including primary care providers, nurses, telehealth clinical technicians, medical and program support assistants, and administrators from 2 community-based outpatient clinics were interviewed. Semistructured interviews were conducted using an interview guide, digitally recorded, and transcribed. Interview transcripts were analyzed using a qualitative content analysis summative approach. Two coders reviewed transcripts independently. Discrepancies were resolved by consensus discussion. RESULTS: In total, 3 themes were identified from participants' experiences: positive perception of telespecialty care, concerns and challenges of implementation, and suggestions for service refinement. Participants voiced that the telemedicine visits saved commute and waiting times and provided veterans in rural areas more access to timely medical care. The mentioned concerns were technical challenges and equipment failure, staffing shortages to cover both in-person and telehealth visit needs, overbooked schedules leading to delayed referrals, the need for a more standardized operation protocol, and more hands-on training with formative feedback among supporting staff. Participants also faced challenges with appointment cancellations and struggled to find ways to efficiently manage both telehealth and in-person visits to streamline patient flow. Nonetheless, most participants feel motivated and confident in implementing telespecialty care going forward. CONCLUSIONS: This study provided important insights into the positive perceptions and ongoing challenges in telespecialty care implementation. Feedback from primary care teams is needed to improve telespecialty care service delivery for rural veterans.

Geographic variation in diabetic retinopathy screening within the Veterans Health Administration.

AIMS: Diabetic retinopathy (DR) remains the leading cause of vision impairment among working-age adults in the United States. The Veterans Health Administration (VA) supplemented its DR screening efforts with teleretinal imaging in 2006. Despite its scale and longevity, no national data on the VA's screening program exists since 1998. Our objective was to determine the influence of geography on diabetic retinopathy screening adherence. METHODS: Setting: VA national electronic medical records. STUDY POPULATION: A national cohort of 940,654 veterans with diabetes (defined as two or more diabetes ICD-9 codes (250.xx)) without a history of DR. EXPOSURES: 125 VA Medical Center catchment areas, demographics, comorbidity burden, mean HbA1c levels, medication use and adherence, as well as utilization and access metrics. MAIN OUTCOME MEASURE: Screening for diabetic retinopathy within the VA medical system within a 2-year period. RESULTS: Within a 2-year time frame 74 % of veterans without a history of DR received retinal screenings within the VA system. After adjustment for age, gender, race-ethnic group, service-connected disability, marital status, and the van Walraven Elixhauser comorbidity score, the prevalence of DR screening varied by VA catchment area with values ranging from 27 % to 86 %. These differences persisted after further adjusting for mean HbA1c level, medication use and adherence as well as utilization and access metrics. CONCLUSIONS: The wide variability in DR screening across 125 VA catchment areas indicates the presence of unmeasured determinants of DR screening. These results are relevant to clinical decision making in DR screening resource allocation.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study.

BACKGROUND: Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions. METHODS: We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes). FINDINGS: Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m2, haemoglobin <11·0 g/dL, platelets <150·0 × 103/µL, ACR ≥300 mg/g, and WBC <4·0 × 103/µL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5·3-19·9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300·0 mg/g and haemoglobin <11·0 g/dL by 7·3-13·2%. INTERPRETATION: We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications. FUNDING: Google.

Telemedicine-based approach to caring for patients with inherited retinal diseases: patient satisfaction and diagnostic testing completion rates.

BACKGROUND: During the COVID-19 pandemic, the Emory Ophthalmic Genetics Service (EOGS) adopted a hybrid telehealth-based care model, with patients undergoing a tailored panel of ancillary tests in addition to a video telehealth encounter with the EOGS physician. This study evaluates patient satisfaction with this model and effectiveness of these encounters in producing a clinical and genetic diagnosis. MATERIALS AND METHODS: A trained interviewer administered a 14-question validated patient satisfaction survey to eligible subjects between October 2020 and April 2021. A mean "favorability index" for patient satisfaction was calculated (maximum score = 5). Rates of ancillary testing, completion of genetic testing, and diagnostic accuracy were also assessed, and compared to results from a control group of EOGS patients that underwent in-person visits. RESULTS: Twenty-one of 33 eligible patients completed the survey. Nine (42.9%) were female, with mean (± SD) age 51.3 ± 13.6 years. The control group was comprised of 49 subjects, predominantly female (71.4%), with mean age 51.5 ± 15.2 years. The mean (range) favorability index was 4.3 (3.1-5.0). Rates of ancillary testing were lower for the telemedicine group vs. the control group: 38.1% vs. 85.7% (p < .001) for electrophysiology; 42.9% vs. 71.4% (p = .03) for perimetry; and 81.0% vs. 95.9% (p = .04) for fundus imaging. Two (11.1%) and 1 (2.8%) (p = .21) subjects in the telehealth and control groups, respectively, did not complete recommended genetic testing. The clinical diagnosis was compatible with the genetic diagnosis in all subjects. CONCLUSIONS: Our results suggest high patient satisfaction and diagnostic accuracy with a hybrid telemedicine-based approach for IRD care, despite lower rates of ancillary testing and no in-person examination.

Stakeholder perceptions affecting the implementation of teleophthalmology.

PURPOSE: Teleophthalmology has become the subject of heightened interest and scrutiny in the wake of the SARS-CoV-2 global pandemic. A streamlined implementation framework becomes increasingly important as demand grows. This study identified obstacles to teleophthalmology implementation through summative content analysis of key stakeholders' perceptions. DESIGN: Summative content analysis of transcribed interviews with key stakeholders (including patients, technicians, ophthalmic readers, staff, nurses, and administrators at two teleophthalmology clinic sites). METHODS: Keyword Were counted and compared to examine underlying meaning. Two analysts coded text independently using MAXQDA for summative qualitative content analysis to derive themes and hierarchical relationships as a basis for future refinement of TECS implementation. xMind ZEN was used to map conceptual relationships and overarching themes that emerged to identify perceived facilitators and barriers to implementation RESULTS: Analysis revealed two themes common to perceptions: (1) benefits of care, and (2) ease of implementation. Perceived benefits included efficiency, accessibility, and earlier intervention in disease course. The quality and quantity of training was heavily weighted in its influence on stakeholders' commitment to and confidence in the program, as were transparent organizational structure, clear bidirectional communication, and the availability of support staff. CONCLUSION: Using a determinant framework of implementation science, this report highlighted potential hindrances to teleophthalmology implementation and offered solutions in order to increase access to screening, improve the quality of care provided, and facilitate sustainability of the innovation.

Detection of signs of disease in external photographs of the eyes via deep learning.

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

Teleophthalmology and Inequities in Diabetic Eye Disease at Safety Net Hospitals.

Introduction: Teleophthalmology has emerged as a convenient and cost-effective intervention to increase access to screening for diabetic retinopathy (DR), a disease that disproportionately affects socially disadvantaged communities. However, a few studies have directly compared the detection of eye disease by teleophthalmology between socially and geographically diverse communities. This study compared the rates and severity of diabetic eye disease, as detected by teleophthalmology, between safety net and non-Safety Net Hospitals (non-SNHs). Methods: Retrospective chart review of patients screened for DR at county Safety Net Hospitals (SNHs) and non-SNHs in 150 cities and 30 states. The rates of DR, macular edema, suspected cataract, suspected glaucoma, and suspected age-related macular degeneration were compared. Relative risk and severity of disease in the county SNH population were calculated. Images were graded by the same group of IRIS readers, who used at least one image per eye with a 45° field centered between the optic disc and the macula. Participants with ungradable screening images were excluded. Results: Ninety-four thousand three hundred twenty-nine participants were screened for eye disease from September 1, 2016 to August 31, 2017. Among the screened participants (54% female; mean [SD] age, 58.7 [12.9] years), overall disease detection was 31% in the county SNH population and 23.6% in the non-SNH population. Compared with the non-SNH population, the county SNH population was twice as likely to screen positive for three or more concurrent eye conditions (1.2% vs. 0.7%) and had increased prevalence of DR (20.2% vs. 16.2%), macular edema (4.9% vs. 3.4%), suspected glaucoma (9.1% vs. 4.3%), suspected cataract (9.6% vs. 4.8%), and proliferative DR (2.1% vs. 1.0%). Conclusions: Increased diabetic eye disease prevalence and severity among people seen at SNHs highlights the need for continued resources to screen, treat, and manage disease. Teleophthalmology continues to be an important tool in efforts to mitigate health inequities and address barriers faced by underserved communities.

Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems.

OBJECTIVE: With rising global prevalence of diabetic retinopathy (DR), automated DR screening is needed for primary care settings. Two automated artificial intelligence (AI)-based DR screening algorithms have U.S. Food and Drug Administration (FDA) approval. Several others are under consideration while in clinical use in other countries, but their real-world performance has not been evaluated systematically. We compared the performance of seven automated AI-based DR screening algorithms (including one FDA-approved algorithm) against human graders when analyzing real-world retinal imaging data. RESEARCH DESIGN AND METHODS: This was a multicenter, noninterventional device validation study evaluating a total of 311,604 retinal images from 23,724 veterans who presented for teleretinal DR screening at the Veterans Affairs (VA) Puget Sound Health Care System (HCS) or Atlanta VA HCS from 2006 to 2018. Five companies provided seven algorithms, including one with FDA approval, that independently analyzed all scans, regardless of image quality. The sensitivity/specificity of each algorithm when classifying images as referable DR or not were compared with original VA teleretinal grades and a regraded arbitrated data set. Value per encounter was estimated. RESULTS: Although high negative predictive values (82.72-93.69%) were observed, sensitivities varied widely (50.98-85.90%). Most algorithms performed no better than humans against the arbitrated data set, but two achieved higher sensitivities, and one yielded comparable sensitivity (80.47%, P = 0.441) and specificity (81.28%, P = 0.195). Notably, one had lower sensitivity (74.42%) for proliferative DR (P = 9.77 × 10-4) than the VA teleretinal graders. Value per encounter varied at $15.14-$18.06 for ophthalmologists and $7.74-$9.24 for optometrists. CONCLUSIONS: The DR screening algorithms showed significant performance differences. These results argue for rigorous testing of all such algorithms on real-world data before clinical implementation.

Enhancing Risk Assessment in Patients with Diabetic Retinopathy by Combining Measures of Retinal Function and Structure.

Purpose: To determine whether combining measures of retinal structure and function predicts need for intervention for diabetic retinopathy (DR) better than either modality alone. Methods: The study sample consisted of 279 diabetic patients who participated in an earlier cross-sectional study. Patients were excluded if they were previously treated for macular edema or proliferative DR or if they had other retinopathies. Medical records were reviewed for ocular interventions including vitrectomy, intravitreal injection, and laser treatment. Need for intervention was analyzed using Kaplan-Meier analyses and Cox proportional hazards. Baseline electroretinograms and fundus photographs were obtained. Two definitions of structural positive findings were as follows: 1. Early Treatment of Diabetic Retinopathy Study diabetic retinopathy severity scale (ETDRS-DR) severity ≥ level 53 (ETDRS-DR+) and 2. ETDRS-DR+ or clinically significant macular edema (VTDR+). A positive function finding corresponded to a RETeval DR Score >23.5 (RETeval+). Results: For patients with VTDR+ the incidence of intervention was 19%, 31%, and 53% after 1, 2, and 3 years of follow-up. In these patients, intervention incidence increased to 34%, 54%, and 74% the subsequent 1, 2, and 3 years if function was above criterion (RETeval+), whereas RETeval- results reduced the risk to 3%, 4%, and 29%, respectively, reducing risk to similar levels seen for patients with VTDR- results at baseline. Conclusions: Prediction of subsequent intervention was best when combining structural and functional information. Translational Relevance: This study demonstrates that clinical management of diabetic retinopathy is improved by adding electroretinography to fundus photographic information in assessing the risk of the need for intervention.

Novel Detection and Restorative Levodopa Treatment for Preclinical Diabetic Retinopathy.

Diabetic retinopathy (DR) is diagnosed clinically by directly viewing retinal vascular changes during ophthalmoscopy or through fundus photographs. However, electroretinography (ERG) studies in humans and rodents have revealed that retinal dysfunction is demonstrable prior to the development of visible vascular defects. Specifically, delays in dark-adapted ERG oscillatory potential (OP) implicit times in response to dim-flash stimuli (<-1.8 log cd · s/m2) occur prior to clinically recognized DR. Animal studies suggest that retinal dopamine deficiency underlies these early functional deficits. In this study, we randomized individuals with diabetes, without clinically detectable retinopathy, to treatment with either low- or high-dose Sinemet (levodopa plus carbidopa) for 2 weeks and compared their ERG findings with those of control subjects (no diabetes). We assessed dim-flash-stimulated OP delays using a novel handheld ERG system (RETeval) at baseline and 2 and 4 weeks. RETeval recordings identified significant OP implicit time delays in individuals with diabetes without retinopathy compared with age-matched control subjects (P < 0.001). After 2 weeks of Sinemet treatment, OP implicit times were restored to control values, and these improvements persisted even after a 2-week washout. We conclude that detection of dim-flash OP delays could provide early detection of DR and that Sinemet treatment may reverse retinal dysfunction.

Diagnostic Accuracy of Technology-based Eye Care Services: The Technology-based Eye Care Services Compare Trial Part I.

PURPOSE: Ophthalmologic telemedicine has the ability to provide eye care for patients remotely, and many countries have used screening tele-ophthalmology programs for several years. One such initiative at the Veterans Affairs (VA) Healthcare System is Technology-based Eye Care Services (TECS). The TECS services are located in primary care clinics and provide basic screening eye care, including vision, refraction, and retinal photography. Eye care providers ("readers") review the clinical data and recommend appropriate follow-up. One of the most common referrals from TECS has been for glaucoma, and this study was powered for glaucoma/glaucoma suspect detection. The current study was undertaken to identify aspects of the protocol that could be refined to enhance accuracy. DESIGN: Prospective comparison between the standard TECS protocol versus a face-to-face (FTF) examination on 256 patients, all of whom had no known history of significant ocular disease. PARTICIPANTS: Patients with no known ocular disease who were scheduled for an in-person eye appointment at the Atlanta VA. Patients underwent screening through the TECS protocol and received an FTF examination on the same day (gold standard). The TECS readers were masked to the results of the FTF examination. MAIN OUTCOME MEASURES: Percent agreement, kappa, sensitivity, and specificity were calculated for the TECS readers' interpretations versus the FTF examination. RESULTS: The TECS readers showed substantial agreement for cataract (κ ≥ 0.71) and diabetic retinopathy (κ ≥ 0.61) and moderate to substantial agreement for glaucoma/glaucoma suspect (κ ≥ 0.52) compared with an FTF examination. Age-related macular degeneration (AMD) showed moderate agreement (κ ≥ 0.34). Percent agreement with the TECS protocol was high (84.3%-98.4%) for each of the disease categories. Overall sensitivity and specificity were ≥75% and ≥55%, respectively, for any diagnosis resulting in referral. Inter-reader and intra-reader agreement was substantial for most diagnoses (κ > 0.61) with percent agreements ranging from 66% to 99%. CONCLUSIONS: Our results indicate that the standard TECS protocol is accurate when compared with an FTF examination for the detection of common eye diseases. The inclusion of additional testing such as OCT could further enhance diagnostic capability.

The Impact of OCT on Diagnostic Accuracy of the Technology-Based Eye Care Services Protocol: Part II of the Technology-Based Eye Care Services Compare Trial.

PURPOSE: Ophthalmologic telemedicine programs help to address the growing demand for eye care and lessen healthcare disparities for patients. One example is Technology-Based Eye Care Services (TECS), implemented in the Veteran Affairs Healthcare System in 2015. Accuracy and quality data for TECS both have been reported, and data suggest that although the TECS examination is comparable with an in-person examination, sensitivity for glaucoma and glaucoma suspect detection is less than that for other diseases, such as macular degeneration. Several articles suggest that OCT can improve disease detection for glaucoma. Therefore, this study was undertaken to test the impact of OCT on the accuracy of the TECS protocol. This article reports the data from part II of the TECS Compare trial; results from part I are discussed in a previous article. DESIGN: Prospective comparison between the TECS protocol with OCT versus a face-to-face (FTF) examination for 256 patients. PARTICIPANTS: An eligible patient was defined as a patient with no known ocular disease who desired a routine eye examination. METHODS: Patient underwent the TECS protocol workup and OCT nerve, OCT macula, and FTF examination on the same day. MAIN OUTCOME MEASURES: Percent agreement, κ values, sensitivity, and specificity were calculated for nonexpert readers after OCT interpretation of the TECS protocol using the FTF examination as the clinical gold standard. RESULTS: OCT did not improve the diagnostic accuracy of the TECS protocol when compared with an FTF examination. In most cases, OCT had no impact, and in the case of reader 2, OCT actually reduced the κ value from moderate agreement to agreement equal to chance while lowering the percent agreement by 10%. OCT also did not impact inter- or intrareader variability parameters. CONCLUSIONS: In this study, OCT did not seem to improve the accuracy of glaucoma or retinal disease detection when added to the standard TECS protocol. In one case, OCT worsened the agreement of the reader compared with the FTF. Further study is necessary to confirm these findings, and results may change if the readers are glaucoma or retina specialists instead of nonexpert OCT readers, comprehensive and anterior segment specialists.

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers. DESIGN: Development and validation of an algorithm. PARTICIPANTS: Fundus images from screening programs, studies, and a glaucoma clinic. METHODS: A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic. MAIN OUTCOME MEASURES: The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features. RESULTS: The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929-0.960) in dataset A, 0.855 (95% CI, 0.841-0.870) in dataset B, and 0.881 (95% CI, 0.838-0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. CONCLUSIONS: A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.

Diagnosis of retinal detachments by a tele-ophthalmology screening program.

In 2015, a tele-ophthalmology program was undertaken at the Atlanta Veterans Affairs Medical Center to provide screening eye care for veterans in their primary care clinics. Though this program was developed as a screening tool, the availability of these services in primary care clinics has enabled triage of certain acute eye complaints. These case reports describe two patients who were diagnosed with retinal detachments through this program, although their primary care providers had triaged them as requiring non-urgent referrals to the eye clinic. Although many patients are seen for acute ocular complaints in primary care clinics and emergency departments, providers in such settings may lack the ability to adequately examine eyes and thus triage ocular complaints. These cases demonstrate the ability of tele-ophthalmology to assist in diagnosing urgent ocular conditions in primary care clinics. Though tele-ophthalmology has been accepted in some parts of the world, in the United States of America it remains widely underutilized. These cases highlight the ability of tele-ophthalmology to close the gap in acute eye care coverage that exists in the USA, most prominently in rural regions.

Exporting Diabetic Retinopathy Images from VA VistA Imaging for Research.

The US Department of Veterans Affairs has been acquiring store and forward digital diabetic retinopathy surveillance retinal fundus images for remote reading since 2007. There are 900+ retinal cameras at 756 acquisition sites. These images are manually read remotely at 134 sites. A total of 2.1 million studies have been performed in the teleretinal imaging program. The human workload for reading images is rapidly growing. It would be ideal to develop an automated computer algorithm that detects multiple eye diseases as this would help standardize interpretations and improve efficiency of the image readers. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs have been developed and there are needs for additional image data to validate this work. To further this research, the Atlanta VA Health Care System (VAHCS) has extracted 112,000 DICOM diabetic retinopathy surveillance images (13,000 studies) that can be subsequently used for the validation of automated algorithms. An extensive amount of associated clinical information was added to the DICOM header of each exported image to facilitate correlation of the image with the patient's medical condition. The clinical information was saved as a JSON object and stored in a single Unlimited Text (VR = UT) DICOM data element. This paper describes the methodology used for this project and the results of applying this methodology.

Differences in Tertiary Glaucoma Care in the Veterans Affairs Health Care System.

Importance: Glaucoma is a common cause of visual impairment in the Veterans Affairs (VA) health care system, but to our knowledge, no data exist concerning tertiary glaucoma care (ie, laser and filtering surgery). Objective: To determine whether the rate of tertiary glaucoma care differs among veterans cared for through the 4 different eye care delivery models that are present in the VA: optometry-only clinics, ophthalmology-only clinics, clinics with optometry and ophthalmology functioning as a single integrated clinic with ophthalmology as the lead, and clinics with optometry and ophthalmology functioning as separate clinics. Design, Setting, and Participants: In this retrospective review of the Veterans Health Administration Support Service Center database, 490 926 veterans with a glaucoma-related diagnosis received care from 136 VA medical centers during fiscal year 2016. Demographic and baseline clinical factors, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, and Current Procedural Terminology codes, and the rates of glaucoma surgery procedures were extracted from the database. The organizational structure of each VA eye clinic was obtained. Univariate and multivariate regression analyses were performed for log percent for laser peripheral iridotomy (LPI), laser trabeculoplasty (LTP), and filtering surgery. Main Outcomes and Measures: Rates of LPI, LTP, and filtering surgery. Results: Of the 490 926 veterans with a glaucoma-related diagnosis, 465 842 (94.9%) were male, 309 677 (63.1%) were white, and 203 243 (41.4%) were aged 65 to 74 years. The rate of LPI was 0.30%, 0.28%, 0.67%, and 0.69% in optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). The rate of LTP was 0.31%, 1.06%, 0.93%, and 0.92% in care delivery models that included optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). The rate of filtering surgery was 0.32%, 0.51%, 0.69%, and 0.60% in optometry-only clinics, ophthalmology-only clinics, integrated clinics, and separated clinics, respectively (P < .001). Multivariate regression analyses showed that these differences remained significantly different even after adjusting for potential confounders. Conclusions and Relevance: Disparities exist in the use of tertiary glaucoma services within the VA, and different care delivery models may play a role. Outcomes of glaucoma care for the different models of eye care delivery were not analyzed in this study.