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. 2018 Aug;125(8):1143-1148.
doi: 10.1016/j.ophtha.2017.12.001. Epub 2018 Jan 17.

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

Michael F Chiang  1 , Alfred Sommer  2 , William L Rich  3 , Flora Lum  4 , David W Parke 2nd  3
Affiliations

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

Michael F Chiang et al. Ophthalmology. 2018 Aug.

Abstract

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.

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