Big Data-Based Epidemiology of Uveitis and Related Intraocular Inflammation

Large administrative health databases, nationwide surveys, and the widespread adoption of electronic medical records have led to an increasing availability of health-related data on ocular inflammatory disease, allowing us to elucidate the real-world epidemiology of uveitis and examine patient and systems-level risk factors for the incidence of specific etiologies of uveitis and its complications. Despite the many advantages to using big databases, there arc also limitations that clinicians must be aware of when making conclusions and extrapolating to the general population, such as the lack of standardization of nomenclature and coding. As the availability of even more robust datasets increases, clinicians and scientists should be prepared to leverage these tools to improve our understanding of disease pathophysiology and our ability to manage patients with ocular inflammatory disease.

Automated summary

The availability of more health-related data has allowed for a better understanding of the epidemiology and risk factors of ocular inflammatory diseases, providing valuable information for treatment. While there are advantages to using large databases, clinicians should also be aware of limitations such as the lack of standardization in terminology and coding. As more robust datasets become available, clinicians and scientists can leverage these tools to enhance their understanding of disease pathophysiology and improve patient management.