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A framework for digital health equity
Open accessAbstract
We present a comprehensive Framework for Digital Health Equity, detailing key digital determinants of health (DDoH), to support the work of digital health tool creators in industry, health systems operations, and academia. The rapid digitization of healthcare may widen health disparities if solutions are not developed with these determinants in mind. Our framework builds on the leading health disparities framework, incorporating a digital environment domain. We examine DDoHs at the individual, interpersonal, community, and societal levels, discuss the importance of a root cause, multi-level approach, and offer a pragmatic case study that applies our framework.
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“Two recently published digital health frameworks for medical students outlined 27 artificial intelligence-specific competencies 47 and 40 digital health topics, 48 respectively.”
From The Digital Health Competencies in Medical Education Framework (2025) · cites this paper as [47]
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“We recognize, however, that social determinants of health are important factors influencing care delivery, symptom management, and supportive care needs and also play an important role in the adoption and access to digital health solutions.41”
From Implementation of a remote symptom monitoring pathway in oncology care: analysis of real-world experience across 33 cancer centres in France and Belgium (2024) · cites this paper as [41]
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“Based on the Digital Health Equity Framework (Richardson et al., 2022), digital determinants of health (DDoHs) and social determinants of health (SDoHs) are intertwined factors that influence an individual’s ability and opportunity to engage with digital health tools.”
From Sociodemographics and Digital Health Literacy in Using Wearables for Health Promotion and Disease Prevention: Cross-Sectional Nationwide Survey in Germany (2025) · cites this paper as Richardson et al., 2022
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“Because this study examined U.S.-based physicians using major commercial EHR platforms, the findings should be interpreted within this context40.”
From When better data meets better design: How EHR data usability and system usability shape physicians' cognitive load (2026) · cites this paper as [40]
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“Nevertheless, these 2 frameworks involved experts from a single country in their development, which may limit their wider use.”
From The Digital Health Competencies in Medical Education Framework (2025) · cites this paper as [47]
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Metadata
- Authors
- Richardson, S., Lawrence, K., Schoenthaler, A. M., & Mann, D.
- Affiliations
- New York University (US)
- Corresponding author
- Richardson, Safiya
- Venue
- npj Digital Medicine
- Published
- 2022-08-18
- Volume
- 5
- Issue
- 1
- Pages
- 119-119
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC9387425
- OpenAlex
- W4292202284
- Cited by
- 607
- References
- 42
- Citations by year
- 2026: 193 · 2025: 183 · 2024: 149 · 2023: 71 · 2022: 9
- FWCI
- 14.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Agency for Healthcare Research and Quality (R01 HS026522)
- National Heart, Lung, and Blood Institute (K23HL145114)
- Topics
- Social Sciences › Health › Health disparities and outcomes
- Social Sciences › Health › Public Health and Social Inequalities
- Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
- Keywords
- digital health equity, digital determinants of health, health disparities, digital health tools, multilevel approach, root cause analysis, digital environment, health equity frameworks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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