In 2016, Implementation Science published the IHI Scale-up Framework, providing guidance on how to scale an improvement idea or initiative from local small-scale testing to a defined region or country. The framework proposes a deliberate sequence of four improvement phases, from set up through to scale up, together with the mechanisms that are required to facilitate adoption of interventions and the underlying support systems required for success.
From testing the Scale-up Framework, we have learned that more guidance is needed on how the content — or the elements of the change intervention — needs to evolve to support scale up. We propose a modification of the Scale-up Framework to describe how the content requirement evolves for each phase.
This theory of content evolution is based on the rationale that the more we can provide clear evidence of how an intervention can be implemented or adapted — and demonstrate improvement and the value of that content in a range of increasingly varied settings — the more likely it is that scale up will occur.
Over the four phases of the IHI Scale-up framework, we suggest the content associated with the intervention needs to evolve as follows:
1. Set Up:
The purpose of the set-up phase is to understand the current state, identify the key processes, systems, and infrastructure currently in place, and understand how they produce health and health care outcomes. From this assessment, areas of potential improvement can be identified and prioritized, and an initial theory describing the core elements of an improvement initiative can be developed. At IHI, we typically use driver diagrams and change packages to describe these core elements. We need to provide an initial prediction for the likely impact of the changes if successfully implemented, although our degree of belief in these changes at this stage may be relatively low. In addition, we need to provide some guidance on how to measure implementation and impact of the core elements of the intervention.
For example, IHI’s work on improving maternal and neonatal mortality in Ethiopia included an extensive set-up phase. Site visits, baseline data collection, and review of best practices were conducted to understand the current state of the country’s health system and develop an initial, multi-pronged theory of change. Although clinical protocols existed for reduction of maternal and neonatal mortality, they had not been widely disseminated and locally tested in conjunction with a number of other supporting changes such as improving health-seeking behavior, access to care, use of quality improvement techniques, and improved supply chain management. The initial change package was then adapted to local settings during the Build the Scalable Unit phase, incorporating and adapting the changes based on learning from small-scale tests at the woreda (district facility) level.
2. Build the scalable unit:
As part of building the “scalable unit” (the smallest administrative unit that will be replicated during scale up), we test an initial theory that includes a preliminary change package comprising of existing proven or promising implementation ideas as well as new change ideas developed in the Set-up phase. This phase will involve working with one or a small number of settings within a well understood local context. An important activity in this phase is to build the degree of belief that these early change ideas will result in improvement. Recommended next steps from this work can include further testing to build greater belief in the change ideas, or to proceed to the next phase (Test Scale up) which tests the emerging change package in a broader range of settings.
3. Test Scale up:
The purpose of this phase is to adapt or validate a change package that has been shown to be effective in a limited number of settings in additional settings or contexts. The change package may have been developed during set up and building the scalable unit or may have been developed elsewhere independently and adopted for testing scale up. In testing scale up, the aim is to build confidence that application of the implementation content will result in improvement even across varied contexts, and what infrastructure and will is required to support implementation.
Next steps from this phase may include recommendations to continue testing to build a greater degree of belief in changes applied to different settings, to undertake more work to develop the readiness of settings to adopt and adapt the changes, or to conclude that the change ideas are robust enough to move to implementation at full scale.
For example, building off a successful pilot in 2012 on reducing unnecessary C-sections in one healthcare facility in Brazil, a refined set of changes was scaled and tested with 26 hospitals during 18 months of Parto Adequado. While building a scalable unit, a new set of practices with a low degree of belief was tested. Learning from this improvement project was then incorporated into a change package, and tested in both private and public hospitals. Successful completion of this phase led to the conclusion that these changes could be scaled to a national level initiative and applied in 150 hospitals with varying contexts.
4. Go to full scale and sustain:
The purpose of this phase is to replicate — at a large scale and with great fidelity — the results seen in the earlier phases of scale up. While there will need to be some ongoing adaptation of the changes package to all contexts within a defined population, region or country, the implementation benefits from the experience gathered from testing of the changes in the Test Scale-up phase, and from the development of standard work for implementation strategies and tactics that are generally applicable to the broader system. The implementers will have a high degree of belief that application of the change package will result in improvement in a variety of settings. Next steps from this stage may be recommendations to continue testing scale up, if results from the interventions are not being demonstrated at expected levels, or to continue to scale-up and sustain the improvement to go to full-scale.
Understanding how the content evolves is crucial to effectively scaling up. Additionally, scale-up activities need to consider how to test and implement the content in local settings. Indeed, the scale-up process may go back and forth between the phases as promising approaches require refinement or more substantial change in order to achieve the overall aims.
Gareth Parry, MSc, PhD, is a Senior Scientist at the Institute for Healthcare Improvement. This post was co-authored by Pierre Barker, Chief Global Partnerships and Programs Officer, and Amrita Dasgupta, Senior Research Associate.
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