Virtual Ophthalmology: Telemedicine in a COVID-19 Era.

PURPOSE: To discuss the effects of the severe acute respiratory syndrome coronavirus 2 betacoronavirus on ambulatory ophthalmology practices, the value proposition of telemedicine, teleophthalmology implementation methodologies, and the accelerated future of telemedicine. DESIGN: Review of the current telehealth landscape including usage, policies, and techniques for ambulatory practice integration. METHODS: We provide author-initiated review of recent trends in telehealth, governmental recommendations for health care delivery during the COVID-19 pandemic, and a PubMed Central query for telemedicine in ophthalmology or teleophthalmology. In addition, the authors' comprehensive experience in telemedicine design and implementation is provided. RESULTS: We provide a summary describing the present state of telehealth, teleophthalmology modeling, care delivery, and the proposed impact of telehealth surges on the future of ophthalmology practice. CONCLUSION: Recent patient and provider interest in telemedicine, the relaxation of regulatory restrictions, increased remote care reimbursement, and ongoing social distancing practices compel many ophthalmologists to consider virtualizing services.

Assessing the Precision of ICD-10 Codes for Uveitis in 2 Electronic Health Record Systems.

Importance: Electronic health record (EHR) systems based on International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding of disease entities are increasingly being used to generate large data sets for analysis. However, the reproducibility of ICD-10 coding in uveitis has not been assessed across EHR platforms, and imprecision in coding may lead to improper conclusions in big-data analyses. Objective: To compare ICD-10 coding of uveitis using 2 EHR systems. Design, Setting, and Participants: This study compares ICD-10 codes for 27 uveitic diseases generated by the Epic and MDIntelleSys EHR systems to the ICD-10 descriptions associated with the codes. No patient data were assessed in this study. Main Outcomes and Measures: The number of diseases for which ICD-10 coding differed between the 2 systems. Results: Thirteen of 27 uveitic diseases were coded differently by the 2 EHR systems. Coding imprecision was notable in that the Epic system returned 16 ICD-10 codes and the MDIntelleSys returned 12 ICD-10 codes to describe 13 diseases; 4 diseases had multiple codes returned, and 6 codes were used to describe more than 1 disease. For example, MDIntelleSys uses ICD-10 code H30.13 for both birdshot choroiditis and acute retinal necrosis, while Epic uses H30.9 for both birdshot choroiditis and multiple evanescent white dot syndrome; MDIntelleSys uses this code for multifocal choroiditis. Furthermore, the ICD-10 descriptions for certain codes lack specificity, allowing variable interpretation by the coder. Conclusions and Relevance: This study suggests there is substantial disparity in the ICD-10 codes that are generated for specific uveitides by the 2 EHR systems studied. This result implies that analysis of large databases generated from the pooling of EHR data could produce results with substantial bias because of misclassification resulting from conflicting and imprecise coding of uveitides. Therefore, research into outcomes, costs, health care utilization, and epidemiology in uveitis might be improved if a more uniform coding system to describe ocular inflammatory disease is implemented.

Interobserver Agreement Among Uveitis Experts on Uveitic Diagnoses: The Standardization of Uveitis Nomenclature Experience.

PURPOSE: To evaluate the interobserver agreement among uveitis experts on the diagnosis of the specific uveitic disease. DESIGN: Interobserver agreement analysis. METHODS: Five committees, each comprised of 9 individuals and working in parallel, reviewed cases from a preliminary database of 25 uveitic diseases, collected by disease, and voted independently online whether the case was the disease in question or not. The agreement statistic, κ, was calculated for the 36 pairwise comparisons for each disease, and a mean κ was calculated for each disease. After the independent online voting, committee consensus conference calls, using nominal group techniques, reviewed all cases not achieving supermajority agreement (>75%) on the diagnosis in the online voting to attempt to arrive at a supermajority agreement. RESULTS: A total of 5766 cases for the 25 diseases were evaluated. The overall mean κ for the entire project was 0.39, with disease-specific variation ranging from 0.23 to 0.79. After the formalized consensus conference calls to address cases that did not achieve supermajority agreement in the online voting, supermajority agreement overall was reached on approximately 99% of cases, with disease-specific variation ranging from 96% to 100%. CONCLUSIONS: Agreement among uveitis experts on diagnosis is moderate at best but can be improved by discussion among them. These data suggest the need for validated and widely used classification criteria in the field of uveitis.