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Diffusion Of Innovations Theory, Principles, And Practice
Abstract
Aspects of the research and practice paradigm known as the diffusion of innovations are applicable to the complex context of health care, for both explanatory and interventionist purposes. This article answers the question, "What is diffusion?" by identifying the parameters of diffusion processes: what they are, how they operate, and why worthy innovations in health care do not spread more rapidly. We clarify how the diffusion of innovations is related to processes of dissemination and implementation, sustainability, improvement activity, and scale-up, and we suggest the diffusion principles that can be readily used in the design of interventions.
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“This rapid adoption across the delivery system is likely driven both by an expectation that generative AI will address burnout among clinicians and increase patient volume and social, institutional, and mimetic processes.33,34”
From Uptake of Generative AI Integrated With Electronic Health Records in US Hospitals (2025) · cites this paper as [33]
In the citing paper
“From the perspective of innovation diffusion81, the use of digital health interventions for NCD management in primary health care of LMICs could be classified as “late majority” or even “laggards” in some regions”
From Digital health interventions for non-communicable disease management in primary health care in low-and middle-income countries (2023) · cites this paper as [81]
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Metadata
- Authors
- Dearing, J. W., & Cox, J. G.
- Affiliations
- Michigan State University (US)
- ORCID
- Dearing, James W.
- Venue
- Health Affairs
- Publisher
- Health Affairs Publishing, LLC
- Published
- 2018-02
- Volume
- 37
- Issue
- 2
- Pages
- 183-190
- ISSN
- 0278-2715 (ISSN-L)
- 1544-5208
- OpenAlex
- W2787575783
- Semantic Scholar
- c51cca517bacd0aeb780046843be80d73cde6311
- Cited by
- 757
- References
- 54
- Citations by year
- 2026: 95 · 2025: 133 · 2024: 133 · 2023: 125 · 2022: 114 · 2021: 85 · 2020: 40 · 2019: 23 · 2018: 6
- FWCI
- 58.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Health Affairs (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Healthcare Policy and Management
- Keywords
- diffusion of innovations, healthcare innovation, diffusion, dissemination, implementation, sustainability, scale-up, intervention design
- MeSH terms
- Delivery of Health Care, Diffusion of Innovation, Health Plan Implementation, Humans, Organizational Innovation, Information Dissemination
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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