Protocol for improving equity in quantitative big data cleaning: lessons from longitudinal analysis of electronic health records from underrepresented and marginalized communities
The phenomenological approach to filtering big data presents an opportunity to better advocate for marginalized communities even when using large datasets that require automated rules for data filtering. Our method may empower researchers who are partnering with communities to embrace large datasets without compromising their commitment to community benefit and respect.
Authors
- Alison E. Fohner (ORCID: https://orcid.org/0000-0003-4231-3331)
- Bert B. Boyer (ORCID: https://orcid.org/0000-0001-5511-4329)
- Scarlett E. Hopkins (ORCID: https://orcid.org/0000-0001-5641-7751)
- Zeruiah V. Buchanan (ORCID: https://orcid.org/0000-0002-4147-6633)
Institutions
- University of Alaska Fairbanks (US)
- Johns Hopkins University (US)
- Oregon Health & Science University (US)
- University of Washington (US)
- Native Health (US)
- Robert Wood Johnson Foundation (US)
Publication Details
- Journal
- International Journal of Epidemiology
- Published
- 2025-02-16
- DOI
- https://doi.org/10.1093/ije/dyaf013
- Primary Topic
- Healthcare Systems and Reforms
- Type
- article
- Field-Weighted Citation Impact
- 0.00