What is the difference between spread and scaling up?

Spread broadly refers to an improvement being taken up by additional sites or populations, while scaling up usually denotes a deliberate, planned effort to extend a tested practice widely across a system so that many more people benefit; the terms overlap and are often used together.

Why do successful improvements often fail to spread?

Because spread depends on the fit of the innovation, the adopters and their networks, and the organizational and system context, and because complexity and local differences make the process unpredictable, an improvement that works in one setting may not transfer to another without adaptation.

Spread and Scaling of Improvements

Spread and scaling concern how a proven improvement moves beyond the site where it was developed to reach additional teams, organizations, regions, and populations. Successful local change does not propagate automatically, so spread and scale-up are studied as active, often unpredictable processes shaped by the innovation, its adopters, and the contexts it enters.

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Definition

Spread is the process by which an improvement is taken up by additional sites or populations beyond its origin, and scaling (scale-up) is the deliberate effort to extend a tested practice broadly so that more people benefit, typically requiring changes across organizational and system levels.

Scope

This topic covers the diffusion of innovations in health-care organizations, the distinction between spread and scale-up, the multi-level nature of change, and frameworks that theorize the scale-up, spread, and sustainability of practices and technologies. It is a conceptual and methodological reference, not operational or clinical guidance.

Core questions

Why do improvements that work in one place often fail to spread to others?
What distinguishes passive diffusion from active scale-up?
Which features of an innovation, adopters, and context drive spread?
Why do many promising innovations face nonadoption or abandonment?

Key concepts

Diffusion versus active scale-up
Spread versus sustainability
Adopter and innovation attributes
Inner and outer context
Nonadoption and abandonment
Complexity and emergence
Multi-level change

Key theories

Diffusion of innovations in service organizations: Greenhalgh and colleagues' systematic review explains spread in health-care organizations through interacting factors: attributes of the innovation, characteristics of adopters, communication and influence networks, the inner and outer context, and the implementation process.
NASSS framework: The Nonadoption, Abandonment, and challenges to Scale-up, Spread, and Sustainability framework identifies multiple interacting domains (the condition, technology, value proposition, adopters, organization, wider system, and their evolution over time) that determine whether innovations, especially technologies, scale and persist.

Mechanisms

Spread is theorized as more than copying an intervention. Diffusion models attribute uptake to the innovation's relative advantage and adaptability, the adopters and their networks, and the organizational and system context. Scale-up adds deliberate effort across levels, from front-line teams to whole systems. Frameworks such as NASSS emphasize that complexity in any domain, and interactions among domains over time, make spread and scale-up unpredictable, helping explain why innovations are sometimes not adopted or are abandoned despite early success. Scoping reviews document how spread, sustaining, and scaling are entangled and rarely follow a linear path.

Clinical relevance

Spreading effective quality and safety improvements is how local gains become system-wide benefit, but spread is difficult and frequently incomplete. This topic describes how spread and scale-up are conceptualized and studied at the organizational and system level and offers no individualized clinical or treatment recommendations.

Evidence & guidelines

The topic relies on a foundational systematic review of diffusion, a scoping review of spread, sustaining, and scaling, and influential theoretical frameworks rather than a clinical guideline. The literature stresses heterogeneity and context-dependence, cautioning against expecting interventions to spread uniformly across settings.

History

Health-care spread research built on Everett Rogers's general diffusion-of-innovations theory, which Greenhalgh and colleagues adapted to service organizations in 2004. Early frameworks for system-wide quality change, such as Ferlie and Shortell's multi-level model, set the stage, and later work like the NASSS framework (2017) and scoping reviews of spread, scaling, and sustainability reframed these as complex, nonlinear processes.

Debates

Can improvements be replicated faithfully across settings?: There is tension between scaling a standardized intervention with high fidelity and adapting it to local contexts; scoping reviews argue that spread is unpredictable and context-bound, so rigid replication and unmodified scale-up frequently fail.

Key figures

Trisha Greenhalgh
Jean-Louis Denis
Ewan Ferlie
Stephen Shortell
Everett Rogers

Seminal works

greenhalgh-2004
ferlie-shortell-2001
greenhalgh-2017-nasss
cote-boileau-2019

Frequently asked questions

What is the difference between spread and scaling up?: Spread broadly refers to an improvement being taken up by additional sites or populations, while scaling up usually denotes a deliberate, planned effort to extend a tested practice widely across a system so that many more people benefit; the terms overlap and are often used together.
Why do successful improvements often fail to spread?: Because spread depends on the fit of the innovation, the adopters and their networks, and the organizational and system context, and because complexity and local differences make the process unpredictable, an improvement that works in one setting may not transfer to another without adaptation.