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Disruptive innovations for designing and diffusing evidence-based interventions
Open accessAbstract
Evidence-based therapeutic and preventive intervention programs (EBIs) have been growing exponentially. Yet EBIs have not been broadly adopted in the United States. In order for our EBI science to significantly reduce disease burden, we need to critically reexamine our scientific conventions and norms. Innovation may be spurred by reexamining the traditional biomedical model for validating, implementing, and diffusing EBI products and science. The model of disruptive innovations suggests that we reengineer EBIs on the basis of their most robust features in order to serve more people in less time and at lower cost. A disruptive innovation provides a simpler and less expensive alternative that meets the essential needs for the majority of consumers and is more accessible, scalable, replicable, and sustainable. Examples of disruptive innovations from other fields include minute clinics embedded in retail chain drug stores, $2 generic eyeglasses, automated teller machines, and telemedicine. Four new research approaches will be required to support disruptive innovations in EBI science: synthesize common elements across EBIs; experiment with new delivery formats (e.g., consumer controlled, self-directed, brief, paraprofessional, coaching, and technology and media strategies); adopt market strategies to promote and diffuse EBI science, knowledge, and products; and adopt continuous quality improvement as a research paradigm for systematically improving EBIs, based on ongoing monitoring data and feedback. EBI science can have more impact if it can better leverage what we know from existing EBIs in order to inspire, engage, inform, and support families and children to adopt and sustain healthy daily routines and lifestyles.
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“Modularity is also an ideal fit with current trends toward disruptive innovations in EBPIs, as it is patient-centered, responsive to idiographic variables that influence treatment engagement and response, and maintains a tight link to the evidence base while allowing therapists to select components that match their expertise and are feasible within their practice settings (Rotheram-Borus et al., 2”
From Acceptance and Commitment Therapy modules: Differential impact on treatment processes and outcomes (2016) · cites this paper as Rotheram-Borus et al., 2012
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“This science-practice gap may reflect a failure of the dominant intervention research paradigm to adequately address factors that influence the implementation of EBPI in usual care (Fairburn & Wilson, 2013; Kazdin & Blase, 2011; Rotheram-Borus, Swendeman, & Chorpita, 2012).”
From Acceptance and Commitment Therapy modules: Differential impact on treatment processes and outcomes (2016) · cites this paper as Rotheram-Borus, Swendeman, & Chorpita, 2012
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“Evidence-based practices not only require careful examination of the efficacy of an intervention but also the ability of the evidence-based practice to be translated and disseminated for real-world use [2].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [2]
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“It has been argued elsewhere that evidence-based practices often become their own separate silos of evidence, rendering it very difficult to mix and match modules across evidence-based practices [2].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [2]
In the citing paper
“The disruptive innovation process emphasizes the focus on providing simpler and less expensive alternatives to current practices that meet the essential need for the majority of users in order to make them more easily accessible, scalable, and replicable [2].”
From Agile science: creating useful products for behavior change in the real world (2016) · cites this paper as [2]
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Metadata
- Authors
- Rotheram-Borus, M. J., Swendeman, D., & Chorpita, B. F.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- American Psychologist
- Published
- 2012
- Volume
- 67
- Issue
- 6
- Pages
- 463-476
- ISSN
- 0003-066X (ISSN-L)
- 1935-990X
- PMC
- PMC3951837
- OpenAlex
- W2161464172
- Semantic Scholar
- ca50e2e0f43c9261a4f32ef583a1989c7c90cc7e
- Cited by
- 210
- References
- 126
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 6 · 2022: 4 · 2021: 7 · 2020: 15 · 2019: 20 · 2018: 21 · 2017: 18 · 2016: 23 · 2015: 33 · 2014: 31 · 2013: 21 · 2012: 5
- FWCI
- 51.11
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- American Psychologist (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (P30MH058107)
- Topics
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Social Sciences › Public Administration › Evaluation and Performance Assessment
- Health Sciences › Health Professions › General Health Professions › Health Sciences Research and Education
- Keywords
- disruptive innovation, evidence-based interventions, diffusion of innovations, implementation science, biomedical model, intervention scalability, continuous quality improvement, market strategy, self-guided intervention, coaching
- MeSH terms
- Delivery of Health Care, Diffusion of Innovation, Humans, Mental Health Services, Preventive Health Services, Evidence-Based Medicine
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.
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