Use of an artificial intelligence-generated vascular severity score improved plus disease diagnosis in retinopathy of prematurity.

OBJECTIVE: To evaluate whether providing clinicians with an artificial intelligence-based vascular severity score (AI-VSS) improves consistency in diagnosis of plus disease in retinopathy of prematurity (ROP). DESIGN: This is a multi-reader diagnostic accuracy imaging study. PARTICIPANTS: Eleven ROP experts (4 pediatric ophthalmologists, 7 retina specialists), 9 of which had been in practice for 10 or more years. METHODS: Retcam (Natus Medical Incorporated) fundus images were obtained from premature infants during routine ROP screening as part of the Imaging and Informatics in ROP study between January 2012 and July 2020. From all available exams, a subset of 150 eye exams from 110 infants were selected for grading. An AI-VSS was assigned to each set of images using the i-ROP DL system. The clinicians were asked to diagnose plus disease for each exam and assign an estimated VSS (range 1-9) at baseline, and then again one month later with AI-VSS assistance. A reference standard diagnosis (RSD) was assigned to each eye exam from the i-ROP study based on 3 masked expert labels and the ophthalmoscopic diagnosis. MAIN OUTCOME MEASURE: Mean linearly weighted kappa for plus disease diagnosis compared to the RSD. Area under the receiver operating characteristic and precision-recall curves (AUROC, AUPR) for 1-9 labels compared to RSD for plus disease. RESULTS: Expert agreement improved significantly from substantial (κ: 0.69 [0.59, 0.75]) to near perfect (κ: 0.81 [0.71, 0.86]) when AI-VSS was integrated. Additionally, there was a significant improvement in plus disease discrimination as measured by mean [95% confidence interval] AUROC (0.94 [0.92, 0.96] to 0.98 [0.96, 0.99], difference: 0.04 [0.01, 0.06]) and AUPR (0.86 [0.81, 0.90] to 0.95 [0.91, 0.97], difference: 0.09 [0.03, 0.14]). CONCLUSIONS: Providing ROP clinicians with an AI-based measurement of vascular severity in ROP was associated with both improved plus disease diagnosis and improved continuous severity labeling, as compared to a reference standard diagnosis for plus disease. If implemented in practice, AI-VSS could reduce inter-observer variability and standardize treatment for infants with ROP.

Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence.

PURPOSE: Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate whether an artificial intelligence (AI) algorithm for assessing ROP severity in babies can be used to evaluate changes in disease epidemiology in babies from South India over a 5-year period. DESIGN: Retrospective cohort study. PARTICIPANTS: Babies (3093) screened for ROP at neonatal care units (NCUs) across the Aravind Eye Care System (AECS) in South India. METHODS: Images and clinical data were collected as part of routine tele-ROP screening at the AECS in India over 2 time periods: August 2015 to October 2017 and March 2019 to December 2020. All babies in the original cohort were matched 1:3 by birthweight (BW) and gestational age (GA) with babies in the later cohort. We compared the proportion of eyes with moderate (type 2) or treatment-requiring (TR) ROP, and an AI-derived ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exam for all babies in a district, VSS), in the 2 time periods. MAIN OUTCOME MEASURES: Differences in the proportions of type 2 or worse and TR-ROP cases, and VSS between time periods. RESULTS: Among BW and GA matched babies, the proportion [95% confidence interval {CI}] of babies with type 2 or worse and TR-ROP decreased from 60.9% [53.8%-67.7%] to 17.1% [14.0%-20.5%] (P < 0.001) and 16.8% [11.9%-22.7%] to 5.1% [3.4%-7.3%] (P < 0.001), over the 2 time periods. Similarly, the median [interquartile range] VSS in the population decreased from 2.9 [1.2] to 2.4 [1.8] (P < 0.001). CONCLUSIONS: In South India, over a 5-year period, the proportion of babies developing moderate to severe ROP has dropped significantly for babies at similar demographic risk, strongly suggesting improvements in primary prevention of ROP. These results suggest that AI-based assessment of ROP severity may be a useful epidemiologic tool to evaluate temporal changes in ROP epidemiology. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

IMPORTANCE: The development of artificial intelligence (AI) and other machine diagnostic systems, also known as software as a medical device, and its recent introduction into clinical practice requires a deeply rooted foundation in bioethics for consideration by regulatory agencies and other stakeholders around the globe. OBJECTIVES: To initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders. EVIDENCE REVIEW: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterward in the working group. FINDINGS: Artificial intelligence has the potential to improve health care access and patient outcome fundamentally while decreasing disparities, lowering cost, and enhancing the care team. Nevertheless, substantial concerns exist. Bioethicists, AI algorithm experts, as well as the Food and Drug Administration and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, and payors (i.e., stakeholders) working together in collaborative communities to resolve the fundamental ethical issues of nonmaleficence, autonomy, and equity are essential to attain this potential. Resolution impacts all levels of the design, validation, and implementation of AI in medicine. Design, validation, and implementation of AI warrant meticulous attention. CONCLUSIONS AND RELEVANCE: The development of a bioethically sound foundation may be possible if it is based in the fundamental ethical principles of nonmaleficence, autonomy, and equity for considerations for the design, validation, and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine before consideration by regulatory agencies. Important improvements in accessibility and quality of health care, decrease in health disparities, and lower cost thereby can be achieved. These considerations should be discussed with all stakeholders and expanded on as a useful initiation of this dialogue.

Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.

PURPOSE: To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee. DESIGN: Validation study of an AI-based ROP vascular severity score. PARTICIPANTS: A total of 34 ROP experts from the ICROP3 committee. METHODS: Two separate datasets of 30 fundus photographs each for stage (0-5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1-9) and stage (1-3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader's diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage. MAIN OUTCOME MEASURES: Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe. RESULTS: The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts. CONCLUSIONS: The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member's labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies.

Artificial intelligence at the national eye institute.

PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology. RECENT FINDINGS: Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce. SUMMARY: The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas.

Evaluation of a Deep Learning-Derived Quantitative Retinopathy of Prematurity Severity Scale.

PURPOSE: To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale. DESIGN: Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity. PARTICIPANTS: Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9. METHODS: A quantitative vascular severity score (1-9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement. MAIN OUTCOME MEASURES: Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (<3 clock hours, 3-6 clock hours, and >6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale. RESULTS: For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale. CONCLUSIONS: A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.

International Classification of Retinopathy of Prematurity, Third Edition.

PURPOSE: The International Classification of Retinopathy of Prematurity is a consensus statement that creates a standard nomenclature for classification of retinopathy of prematurity (ROP). It was initially published in 1984, expanded in 1987, and revisited in 2005. This article presents a third revision, the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), which is now required because of challenges such as: (1) concerns about subjectivity in critical elements of disease classification; (2) innovations in ophthalmic imaging; (3) novel pharmacologic therapies (e.g., anti-vascular endothelial growth factor agents) with unique regression and reactivation features after treatment compared with ablative therapies; and (4) recognition that patterns of ROP in some regions of the world do not fit neatly into the current classification system. DESIGN: Review of evidence-based literature, along with expert consensus opinion. PARTICIPANTS: International ROP expert committee assembled in March 2019 representing 17 countries and comprising 14 pediatric ophthalmologists and 20 retinal specialists, as well as 12 women and 22 men. METHODS: The committee was initially divided into 3 subcommittees-acute phase, regression or reactivation, and imaging-each of which used iterative videoconferences and an online message board to identify key challenges and approaches. Subsequently, the entire committee used iterative videoconferences, 2 in-person multiday meetings, and an online message board to develop consensus on classification. MAIN OUTCOME MEASURES: Consensus statement. RESULTS: The ICROP3 retains current definitions such as zone (location of disease), stage (appearance of disease at the avascular-vascular junction), and circumferential extent of disease. Major updates in the ICROP3 include refined classification metrics (e.g., posterior zone II, notch, subcategorization of stage 5, and recognition that a continuous spectrum of vascular abnormality exists from normal to plus disease). Updates also include the definition of aggressive ROP to replace aggressive-posterior ROP because of increasing recognition that aggressive disease may occur in larger preterm infants and beyond the posterior retina, particularly in regions of the world with limited resources. ROP regression and reactivation are described in detail, with additional description of long-term sequelae. CONCLUSIONS: These principles may improve the quality and standardization of ROP care worldwide and may provide a foundation to improve research and clinical care.

105° field of view non-contact handheld swept-source optical coherence tomography.

We demonstrate a handheld swept-source optical coherence tomography (OCT) system with a 400 kHz vertical-cavity surface-emitting laser (VCSEL) light source, a non-contact approach, and an unprecedented single shot 105° field of view (FOV). We also implemented a spiral scanning pattern allowing real-time visualization with improved scanning efficiency. To the best of our knowledge, this is the widest FOV achieved in a portable non-contact OCT retinal imaging system to date. Improvements to the FOV may aid the evaluation of retinal diseases such as retinopathy of prematurity, where important vitreoretinal changes often occur in the peripheral retina.

Description and management of retinopathy of prematurity reactivation after intravitreal antivascular endothelial growth factor therapy.

PURPOSE OF REVIEW: To review the literature regarding reactivation of retinopathy of prematurity (ROP) after treatment with antivascular endothelial growth factor (anti-VEGF) agents. RECENT FINDINGS: Reactivation can occur after anti-VEGF or laser. Risk factors for reactivation include patient and disease-related factors. Various studies are evaluating the use of different anti-VEGF agents and reactivation rates. However, the definition of reactivation varies between studies. SUMMARY: The literature has varied definitions of reactivation, which is often used interchangeably with recurrence. It is important to recognize features of reactivation of ROP to appropriately manage patients and conduct clinical trials. The International Classification of ROP 3rd edition has established a consensus guideline regarding terminology describing reactivation.

Ranibizumab versus laser therapy for the treatment of very low birthweight infants with retinopathy of prematurity (RAINBOW): an open-label randomised controlled trial.

BACKGROUND: Despite increasing worldwide use of anti-vascular endothelial growth factor agents for treatment of retinopathy of prematurity (ROP), there are few data on their ocular efficacy, the appropriate drug and dose, the need for retreatment, and the possibility of long-term systemic effects. We evaluated the efficacy and safety of intravitreal ranibizumab compared with laser therapy in treatment of ROP. METHODS: This randomised, open-label, superiority multicentre, three-arm, parallel group trial was done in 87 neonatal and ophthalmic centres in 26 countries. We screened infants with birthweight less than 1500 g who met criteria for treatment for retinopathy, and randomised patients equally (1:1:1) to receive a single bilateral intravitreal dose of ranibizumab 0·2 mg or ranibizumab 0·1 mg, or laser therapy. Individuals were stratified by disease zone and geographical region using computer interactive response technology. The primary outcome was survival with no active retinopathy, no unfavourable structural outcomes, or need for a different treatment modality at or before 24 weeks (two-sided α=0·05 for superiority of ranibizumab 0·2 mg against laser therapy). Analysis was by intention to treat. This trial is registered with ClinicalTrials.gov, NCT02375971. INTERPRETATION: Between Dec 31, 2015, and June 29, 2017, 225 participants (ranibizumab 0·2 mg n=74, ranibizumab 0·1 mg n=77, laser therapy n=74) were randomly assigned. Seven were withdrawn before treatment (n=1, n=1, n=5, respectively) and 17 did not complete follow-up to 24 weeks, including four deaths in each group. 214 infants were assessed for the primary outcome (n=70, n=76, n=68, respectively). Treatment success occurred in 56 (80%) of 70 infants receiving ranibizumab 0·2 mg compared with 57 (75%) of 76 infants receiving ranibizumab 0·1 mg and 45 (66%) of 68 infants after laser therapy. Using a hierarchical testing strategy, compared with laser therapy the odds ratio (OR) of treatment success following ranibizumab 0·2 mg was 2·19 (95% Cl 0·99-4·82, p=0·051), and following ranibizumab 0·1 mg was 1·57 (95% Cl 0·76-3·26); for ranibizumab 0·2 mg compared with 0·1 mg the OR was 1·35 (95% Cl 0·61-2·98). One infant had an unfavourable structural outcome following ranibizumab 0·2 mg, compared with five following ranibizumab 0·1 mg and seven after laser therapy. Death, serious and non-serious systemic adverse events, and ocular adverse events were evenly distributed between the three groups. FINDINGS: In the treatment of ROP, ranibizumab 0·2 mg might be superior to laser therapy, with fewer unfavourable ocular outcomes than laser therapy and with an acceptable 24-week safety profile. FUNDING: Novartis.

Endophthalmitis after Cataract Surgery in the United States: A Report from the Intelligent Research in Sight Registry, 2013-2017.

PURPOSE: To determine recent incidence and visual outcomes for acute-onset endophthalmitis after cataract surgery performed in the United States. DESIGN: Retrospective cohort study. PARTICIPANTS: United States cataract surgery patients, 2013-2017 (5 401 686 patients). METHODS: Cases of acute-onset postoperative endophthalmitis occurring within 30 days after cataract surgery were identified using diagnosis codes in the American Academy of Ophthalmology IRIS (Intelligent Research in Sight) Registry database, drawn from electronic health records in ophthalmology practices across the nation. Annual and aggregate 5-year incidences were determined for all cataract surgeries and specifically for standalone procedures versus those combined with other ophthalmic surgeries. Patient characteristics were compared. Mean and median visual acuity was determined at 1 month preoperative as well as 1 week, 1 month, and 3 months postoperative among patients with and without endophthalmitis. MAIN OUTCOME MEASURES: Incidence of acute-onset postoperative endophthalmitis after cataract surgery. RESULTS: A total of 8 542 838 eyes underwent cataract surgery, 3629 of which developed acute-onset endophthalmitis (0.04%; 95% confidence interval, 0.04%-0.04%). Endophthalmitis incidence was highest among patients aged 0 to 17 years (0.37% over 5 years), followed by patients aged 18 to 44 years (0.18% over 5 years; P < 0.0001). Endophthalmitis occurred 4 times more often after combined cases (cataract with other ophthalmic procedures) than after standalone cataract surgeries (0.20% vs. 0.04% of cases), and occurred in 0.35% of patients receiving anterior vitrectomy. Mean 3-month postoperative visual acuity was 20/100 (median, 20/50) among endophthalmitis patients, versus a mean of approximately 20/40 (median, 20/30) among patients without endophthalmitis. However, 4% of endophthalmitis patients still achieved 20/20 or better visual acuity, and 44% achieved 20/40 or better visual acuity at 3 months. CONCLUSIONS: Acute-onset endophthalmitis occurred in 0.04% of 8 542 838 cataract surgeries performed in the United States between 2013 and 2017. Risk factors may include younger age, cataract surgery combined with other ophthalmic surgeries, and anterior vitrectomy. Visual acuity outcomes vary; however, patients can recover excellent vision after surgery. Big data from clinical registries like the IRIS Registry has great potential for evaluating rare conditions such as endophthalmitis, including developing benchmarks, longer-term time trend investigation, and comprehensive analysis of risk factors and prophylaxis.

Aggressive Posterior Retinopathy of Prematurity: Clinical and Quantitative Imaging Features in a Large North American Cohort.

PURPOSE: Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score. DESIGN: Retrospective analysis. PARTICIPANTS: The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease. METHODS: A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9. MAIN OUTCOME MEASURES: Birth weight, gestational age, postmenstrual age, and vascular severity score. RESULTS: Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P < 0.01) and reached peak severity at an earlier postmenstrual age (34.7 weeks vs. 36.9 weeks; P < 0.001) compared with infants with TR without AP-ROP. The mean vascular severity score was greatest in TR with AP-ROP infants compared with TR without AP-ROP infants (8.79 vs. 7.19; P < 0.001). Analyzing the severity score over time, the rate of progression was fastest in infants with AP-ROP (P < 0.002 at 30-32 weeks). CONCLUSIONS: Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.

Artificial intelligence for retinopathy of prematurity.

PURPOSE OF REVIEW: In this article, we review the current state of artificial intelligence applications in retinopathy of prematurity (ROP) and provide insight on challenges as well as strategies for bringing these algorithms to the bedside. RECENT FINDINGS: In the past few years, there has been a dramatic shift from machine learning approaches based on feature extraction to 'deep' convolutional neural networks for artificial intelligence applications. Several artificial intelligence for ROP approaches have demonstrated adequate proof-of-concept performance in research studies. The next steps are to determine whether these algorithms are robust to variable clinical and technical parameters in practice. Integration of artificial intelligence into ROP screening and treatment is limited by generalizability of the algorithms to maintain performance on unseen data and integration of artificial intelligence technology into new or existing clinical workflows. SUMMARY: Real-world implementation of artificial intelligence for ROP diagnosis will require massive efforts targeted at developing standards for data acquisition, true external validation, and demonstration of feasibility. We must now focus on ethical, technical, clinical, regulatory, and financial considerations to bring this technology to the infant bedside to realize the promise offered by this technology to reduce preventable blindness from ROP.

Automated and Computer-Assisted Detection, Classification, and Diagnosis of Diabetic Retinopathy.

Background: The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction: Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion: Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion: There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes.

Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy, Third Edition.

Contributors The following document and appendices represent the third edition of the Practice Guidelines for Ocular Telehealth-Diabetic Retinopathy. These guidelines were developed by the Diabetic Retinopathy Telehealth Practice Guidelines Working Group. This working group consisted of a large number of subject matter experts in clinical applications for telehealth in ophthalmology. The editorial committee consisted of Mark B. Horton, OD, MD, who served as working group chair and Christopher J. Brady, MD, MHS, and Jerry Cavallerano, OD, PhD, who served as cochairs. The writing committees were separated into seven different categories. They are as follows: 1.Clinical/operational: Jerry Cavallerano, OD, PhD (Chair), Gail Barker, PhD, MBA, Christopher J. Brady, MD, MHS, Yao Liu, MD, MS, Siddarth Rathi, MD, MBA, Veeral Sheth, MD, MBA, Paolo Silva, MD, and Ingrid Zimmer-Galler, MD. 2.Equipment: Veeral Sheth, MD (Chair), Mark B. Horton, OD, MD, Siddarth Rathi, MD, MBA, Paolo Silva, MD, and Kristen Stebbins, MSPH. 3.Quality assurance: Mark B. Horton, OD, MD (Chair), Seema Garg, MD, PhD, Yao Liu, MD, MS, and Ingrid Zimmer-Galler, MD. 4.Glaucoma: Yao Liu, MD, MS (Chair) and Siddarth Rathi, MD, MBA. 5.Retinopathy of prematurity: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD. 6.Age-related macular degeneration: Christopher J. Brady, MD, MHS (Chair) and Ingrid Zimmer-Galler, MD. 7.Autonomous and computer assisted detection, classification and diagnosis of diabetic retinopathy: Michael Abramoff, MD, PhD (Chair), Michael F. Chiang, MD, and Paolo Silva, MD.

Changes in Electronic Health Record Use Time and Documentation over the Course of a Decade.

PURPOSE: With the current wide adoption of electronic health records (EHRs) by ophthalmologists, there are widespread concerns about the amount of time spent using the EHR. The goal of this study was to examine how the amount of time spent using EHRs as well as related documentation behaviors changed 1 decade after EHR adoption. DESIGN: Single-center cohort study. PARTICIPANTS: Six hundred eighty-five thousand three hundred sixty-one office visits with 70 ophthalmology providers. METHODS: We calculated time spent using the EHR associated with each individual office visit using EHR audit logs and determined chart closure times and progress note length from secondary EHR data. We tracked and modeled how these metrics changed from 2006 to 2016 with linear mixed models. MAIN OUTCOME MEASURES: Minutes spent using the EHR associated with an office visit, chart closure time in hours from the office visit check-in time, and progress note length in characters. RESULTS: Median EHR time per office visit in 2006 was 4.2 minutes (interquartile range [IQR], 3.5 minutes), and increased to 6.4 minutes (IQR, 4.5 minutes) in 2016. Median chart closure time was 2.8 hours (IQR, 21.3 hours) in 2006 and decreased to 2.3 hours (IQR, 18.5 hours) in 2016. In 2006, median note length was 1530 characters (IQR, 1435 characters) and increased to 3838 characters (IQR, 2668.3 characters) in 2016. Linear mixed models found EHR time per office visit was 31.9±0.2% (P < 0.001) greater from 2014 through 2016 than from 2006 through 2010, chart closure time was 6.7±0.3 hours (P < 0.001) shorter from 2014 through 2016 versus 2006 through 2010, and note length was 1807.4±6.5 characters (P < 0.001) longer from 2014 through 2016 versus 2006 through 2010. CONCLUSIONS: After 1 decade of use, providers spend more time using the EHR for an office visit, generate longer notes, and close the chart faster. These changes are likely to represent increased time and documentation pressure for providers. Electronic health record redesign and new documentation regulations may help to address these issues.

Data-Driven Scheduling for Improving Patient Efficiency in Ophthalmology Clinics.

PURPOSE: To improve clinic efficiency through development of an ophthalmology scheduling template developed using simulation models and electronic health record (EHR) data. DESIGN: We created a computer simulation model of 1 pediatric ophthalmologist's clinic using EHR timestamp data, which was used to develop a scheduling template based on appointment length (short, medium, or long). We assessed its impact on clinic efficiency after implementation in the practices of 5 different pediatric ophthalmologists. PARTICIPANTS: We observed and timed patient appointments in person (n = 120) and collected EHR timestamps for 2 years of appointments (n = 650). We calculated efficiency measures for 172 clinic sessions before implementation vs. 119 clinic sessions after implementation. METHODS: We validated clinic workflow timings calculated from EHR timestamps and the simulation models based on them with observed timings. From simulation tests, we developed a new scheduling template and evaluated it with efficiency metrics before vs. after implementation. MAIN OUTCOME MEASURES: Measurements of clinical efficiency (mean clinic volume, patient wait time, examination time, and clinic length). RESULTS: Mean physician examination time calculated from EHR timestamps was 13.8±8.2 minutes and was not statistically different from mean physician examination time from in-person observation (13.3±7.3 minutes; P = 0.7), suggesting that EHR timestamps are accurate. Mean patient wait time for the simulation model (31.2±10.9 minutes) was not statistically different from the observed mean patient wait times (32.6±25.3 minutes; P = 0.9), suggesting that simulation models are accurate. After implementation of the new scheduling template, all 5 pediatric ophthalmologists showed statistically significant improvements in clinic volume (mean increase of 1-3 patients/session; P ≤ 0.05 for 2 providers; P ≤ 0.008 for 3 providers), whereas 4 of 5 had improvements in mean patient wait time (average improvements of 3-4 minutes/patient; statistically significant for 2 providers, P ≤ 0.008). All of the ophthalmologists' examination times remained the same before and after implementation. CONCLUSIONS: Simulation models based on big data from EHRs can test clinic changes before real-life implementation. A scheduling template using predicted appointment length improves clinic efficiency and may generalize to other clinics. Electronic health records have potential to become tools for supporting clinic operations improvement.

The 2016 American Academy of Ophthalmology IRIS® Registry (Intelligent Research in Sight) Database: Characteristics and Methods.

PURPOSE: To describe the characteristics of the patient population included in the 2016 IRIS® Registry (Intelligent Research in Sight) database for analytic aims. DESIGN: Description of a clinical data registry. PARTICIPANTS: The 2016 IRIS Registry database consists of 17 363 018 unique patients from 7200 United States-based ophthalmologists in the United States. METHODS: Electronic health record (EHR) data were extracted from the participating practices and placed into a clinical database. The approach can be used across dozens of EHR systems. MAIN OUTCOME MEASURES: Demographic characteristics. RESULTS: The 2016 IRIS Registry database includes data about patient demographics, top-coded disease conditions, and visit rates. CONCLUSIONS: The IRIS Registry is a unique, large, real-world data set that is available for analytics to provide perspectives and to learn about current ophthalmic care and treatment outcomes. The IRIS Registry can be used to answer questions about practice patterns, use, disease prevalence, clinical outcomes, and the comparative effectiveness of different treatments. Limitations of the data are the same limitations associated with EHR data in terms of documentation errors or missing data and the lack of images. Currently, open access to the database is not available, but there are opportunities for researchers to submit proposals for analyses, for example through a Research to Prevent Blindness and American Academy of Ophthalmology Award for IRIS Registry Research.