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Big data in ophthalmology: a systematic review of public databases for ophthalmic research

Eye volume 37, pages 3044–3046 (2023)Cite this article

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Public databases are often used in population health research to generate hypotheses and insights into disparities, diseases, treatments, and outcomes. They can help improve patient care through clinical decision support, strengthen understanding of ophthalmic diseases and treatments, and advance risk adjustment, billing, and reimbursement. However, it can be challenging to find, access, and analyse these large publicly available databases to answer hypothesis-driven research questions.

Previous reviews of big data research in ophthalmology have prioritised determining common research topics and listing commonly used databases over exploring new databases or understudied populations [1,2,3]. In addition, these reviews often lacked a systematic literature search, contributing to potential reporting bias [1,2,3]. Herein, we systematically reviewed the literature for large publicly available health databases available for ophthalmic research.

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Fig. 1: PRISMA flow diagram.

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Acknowledgements

The authors thank Chelsea Misquith of the Brown University Library for her assistance with the search strategy for this review.

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Authors and Affiliations

  1. Program in Biology, Brown University, Providence, RI, USA

    John C. Lin, Sophia Y. Ghauri & Matthew J. Lee

  2. Division of Ophthalmology, Warren Alpert Medical School, Brown University, Providence, RI, USA

    John C. Lin, Sophia Y. Ghauri, Matthew J. Lee & Paul B. Greenberg

  3. Departments of Ophthalmology and Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA

    Ingrid U. Scott

  4. Section of Ophthalmology, Providence VA Medical Center, Providence, RI, USA

    Paul B. Greenberg

Authors
  1. John C. Lin
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  2. Sophia Y. Ghauri
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  3. Matthew J. Lee
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  4. Ingrid U. Scott
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  5. Paul B. Greenberg
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Contributions

JCL: conceptualisation, methodology, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualisation. SYG: formal analysis, investigation, data curation, writing—original draft, visualisation. MJL: formal analysis, investigation, data curation. IUS: conceptualisation, methodology, writing—original draft, writing—review and editing. PBG: conceptualisation, methodology, writing—original draft, writing—review and editing, supervision, project administration.

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Correspondence to Paul B. Greenberg.

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The authors declare no competing interests.

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Lin, J.C., Ghauri, S.Y., Lee, M.J. et al. Big data in ophthalmology: a systematic review of public databases for ophthalmic research. Eye 37, 3044–3046 (2023). https://doi.org/10.1038/s41433-023-02446-7

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  • DOI: https://doi.org/10.1038/s41433-023-02446-7

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