A framework for scaling up health interventions: lessons from large-scale improvement initiatives in Africa

Six existing frameworks for sequential scale-up from the literature

The literature on achieving results at scale describes various approaches, taking
into account factors at the smallest scale, including details of the intervention
itself; factors at the largest scale, including the larger socio-political and economic
context; and myriad factors in between, including variables related to the implementing
health systems, communities, and practitioners 5]–11]. This approach accommodates multi-level interventions that address the complexities
of the environment and interacting systems. In addition, several articles point to
lessons from outside of health care, including from social movements and complex adaptive
systems 12]–14]. We review six existing frameworks that advocate a sequential approach—a particular
ordering of phases for successful scale-up and spread—and provide practical guidance
for how to work with organizations, health systems, and communities to implement and
scale up best practices.

The three core categories we sought to understand better—the journey from an idea
to full-scale implementation, environmental factors that foster adoption, and infrastructure
required to support scale-up—are reflected to varying degrees in the six frameworks
for achieving results at scale that we studied (Table 1). All six frameworks include phased activities used to move new interventions or
pockets of best practices to full scale 15]–23]. To move through the phases, methods used include theory testing in different settings
at a small scale and deep inquiry to understand the context and planning before moving
forward. All frameworks promoted the use of data to reflect and improve the future
design of the work and acknowledge factors that impact spread (e.g., intervention
characteristics and the will for change). Some highlight the importance of looking
ahead to build needed infrastructure to support full-scale implementation and advocate
testing resource requirements during smaller tests of implementation. Others rely
more on pre-planning, predictions of resource needs, and feedback after implementation.

Table 1. Review of frameworks for scaling up health interventions

Non-sequential scale-up approaches

A number of non-sequential approaches to taking an intervention to full scale have also been described 24]. These include policy changes and executive mandates (e.g., the banning of traditional
birth attendants to increase women’s attendance in facilities), campaigns that saturate
coverage of specific geographies over a short period of time (e.g., vaccination drives,
deworming), more complex “campaign” approaches that disseminate evidence-based bundles
and a “playbook” of implementation strategies (e.g., IHI’s 100,000 Lives Campaign),
and a rapid mobilization approach used when spread is required in emergency situations
(e.g., vaccination against a new virus like H1N1 or cholera). These approaches all
have an important role in implementing health interventions and should be considered
in the final phase of the sequence of activities described in our framework.

Evolution of models and frameworks that use quality improvement as the “engine” for
change

At the heart of the quality improvement (QI) method is rapid-cycle testing using the
Shewhart Plan-Do-Study-Act (PDSA) cycle 25], which ensures that ideas for change are tested and adapted for local context. The
notion of using PDSA to “ramp up” the implementation process into broader and more
diverse environments, as proposed by Associates for Process Improvement (Fig. 1), was a breakthrough in understanding how to apply QI to the scale-up process. This
concept provides the essential requirement for an adaptive design that can accommodate
different contexts that are encountered as the intervention is scaled up.

Fig. 1. Rapid-cycle improvement. Integral to the Model for Improvement, an improvement approach
developed by Associates in Process Improvement, rapid-cycle improvement is a disciplined
way to iteratively test changes in a process at a larger and larger scale (Langley
GJ, Nolan KM, Nolan TW, Norman CL, Provost LP. The improvement guide: a practical approach to enhancing organizational performance. San Francisco: Jossey-Bass Publishers; 2009). Based on a theory about what change
will lead to improvement, a change is first tested at a very small scale, e.g., with
one clinician and one patient, using the Plan-Do-Study-Act method. Based on the results
of each cycle, further tests are planned or the change may be abandoned

The QI approach is central to the IHI “Breakthrough Series,” or BTS, model 26]—a structured learning system that brings a number of teams from different settings
together to accelerate the implementation of improved processes within and across
participating organizations. Quality Improvement methods were also a core element
of IHI’s 100,000 Lives Campaign, which used a rapid national dissemination (campaign)
approach to reach thousands of hospitals across the USA 27].

Efforts to understand the determinants of spread resulted in IHI’s early Framework
for Spread 28], which was designed to help organizations, primarily hospitals and health systems,
expand the impact of their work from pilots or small-scale interventions to larger
areas within their organizations or communities (Fig. 2). The Framework for Spread brought attention to the determinants of spreading good
practice (i.e., social science, organizational structure, and network properties)
29]–33]. The framework also emphasized the role of leadership behavior as a key determinant
of success in spreading evidence-based interventions.

Fig. 2. IHI Framework for Spread. IHI’s earlier Framework for Spread 28] identifies six areas that have been shown to contribute to successful spread: the
role of organizational or governmental leadership in setting the agenda for change,
aligning incentives, and establishing accountability; the development of better ideas
and practices that demonstrate the relative advantage of such practices over the old
way; the development and use of communications channels and messages; the strengthening
the social system; the use of data to guide spread; and the refinement of the spread
effort as needed, based on feedback from the field and data on the performance of
the system

Our evolving understanding of the social and environmental determinants of going to
full scale—addressed in the new framework—reflects two realities of carrying out this
kind of work: first, the need to account for the factors that are required both to
promote adoption of the changes and to support scale-up; and second, the need to design
a phased plan from the outset with full-scale implementation in mind. Many pilots
cannot progress to scale-up because the specifications of the pilot cannot be replicated
at scale.

A new framework

The Framework for Going to Full Scale (Fig. 3) defines four phases required to get to full scale: (1) Set-up, which prepares the ground for introduction and testing of the intervention that
will be taken to full scale; (2) Develop the Scalable Unit, which is an early test and demonstration phase, (3) Test of Scale-up, which spreads the intervention to a variety of settings that are likely to represent
contexts that will be encountered at full scale; and (4) Go to Full Scale, which unfolds rapidly to enable a larger number of sites to adopt and/or replicate
the intervention. We discuss the importance of designing in sustainability at all
phases. While this sequence reflects a logical progression from conception to full
scale, in reality, the phases may not be linear; rather, they may be more organic
and iterative, with streams of work initiated at different times and progressing at
different rates.

Fig. 3. IHI Framework for Going to Full Scale. The IHI Framework for Going to Full Scale addresses
the phases of going to full scale and the adoption mechanisms and support systems
needed to achieve large-scale programming. The elements of the framework include the
phases of going to full scale (i.e., Set-up, Develop the Scalable Unit, Test of Scale-up, and Go to Full Scale); adoption mechanisms (i.e., leadership engagement, communication methods, leveraging
social networks, and building a culture of urgency and persistence); and support systems
needed to achieve large-scale programming (i.e., a learning system that connects adopters
and experts, a data system to support measurement for improvement, infrastructure
such as IT, equipment, etc.), building capability through training and support, and
building reliable process that support sustainability

Setup

This phase establishes an entry point for the planned intervention into the existing
health system. This phase includes a clear articulation of what needs to be scaled
up and defines the ambition for “full scale.” During this phase, initial test sites,
early adopters, and potential “champions” of the intervention are identified.

Develop the scalable unit

This phase develops the “scalable unit”—the smallest representative facsimile of the
system targeted for full-scale implementation. The purpose of this phase is to intensively
test local ideas for best-practice implementation so that the interaction among all
parts of this representative sub-system can be understood. An important outcome is
the generation of a set of context-sensitive strategies and interventions (change
package) that can be further tested and refined in a broader range of settings. This
change package will drive rapid improvement of performance during the Go to Full Scale phase.

The scalable unit is typically a small administrative unit (e.g., sub-district/district
or clinical ward/division) that includes key infrastructural components and relationship
architecture that are likely to be encountered in the system at full scale. If the
ambition of scale is large (e.g., county, province, health system), a scalable unit
could comprise multiple levels of care and the communities that are served by a large
health system, or a divisional unit of care in a hospital setting or large clinic
system.

Initial testing can be done at a single site if that site represents the scalable
unit; however, if the scalable unit requires inclusion of multiple sub-units (e.g.,
clinics and a hospital in a district), an adaptation of the IHI Breakthrough Series
(BTS) Collaborative model 26] can be used to accelerate learning and build the initial change package. When scaling
to a nation or a region, the scalable unit itself may be very large and complex (e.g.,
large health district, large Accountable Care Organization). In that case, a sub-system
(e.g., sub-district, hospital, referral clinics/communities) can be tested first,
before broadening the work to include all parts of the scalable unit. The IHI “Idealized
Design” process proposed that this early testing phase could itself comprise several
iterative steps required to deliver a change package that would be ready for further
dissemination through the BTS mechanism 34].

Test of scale-up (i.e., testing the set of interventions to be taken to scale)

The underlying theory of change and the change package from a successful early demonstration
need to be tested in a broader range of settings before going to full scale. Also,
during this phase, we test necessary infrastructure (e.g., data systems and supply
chain) required to support full-scale implementation and build the human capacity
and capability (e.g., leadership, managerial, and frontline capacity needed to support
the method being used to scale up). This phase is an important opportunity to build
the belief and will of leaders and frontline staff to support the changes. As with
the Develop the Scalable Unit phase, the BTS model can be an effective mechanism to accelerate the adoption of
new ideas and generate a more mature change package that can be used for full-scale
implementation across a range of contexts.

Go to full scale

This is a rapid deployment phase in which a well-tested set of interventions, supported
by a reliable data feedback system, is adopted by frontline staff on a larger scale.
The focus is on rapid uptake of the intervention through replication. While some adaptation
of the intervention to local environments will always be required, there is less emphasis
on new learning in this phase. Significant will, knowledge, experience, and well-tested
infrastructural support and capacity need to be in place before moving to this phase;
the determinants of adoption as reflected in the IHI Framework for Spread (i.e., intrinsic
properties of the change, the social environment, and the network properties) are
well established.

Experience with this approach suggests that the rate of expansion can be exponential
(i.e., not linear) by a multiple of 5 of the number of units involved in the scale-up
(e.g., 1–5–25–125–625, etc.). The actual multiple can vary depending on the complexity
of the intervention and the complexity of the environment. In South Africa, the scale-up
included 3 districts, then 10, and then all 52 for the Develop the Scalable Unit, Test of Scale-up, and Go to Full Scale phases, respectively. In Ghana, the number of units scaled went from 35 sub-districts
to 265 to 289 to 554. (See the case reports below.)

Adoption mechanisms

The environment for change and psychology of change are crucial factors that foster
or hinder the pace and extent of adoption of the intervention. Rapid scale-up will
not occur in an unreceptive environment. At each step of the scale-up process, the
design of the intervention needs to be closely attuned to the social beliefs and health
system practices, taking account of and closely integrated with policies, protocols,
and other health system structures. We outline five areas that impact adoption.

Identifying factors that affect adoption should start in the Set-up phase with understanding the health system’s infrastructure, culture, size, and strength
of its underlying social system 28]. Understanding the psychology of change and whom to target in the different phases
is crucial to success of scale-up; in the Set-up phase, the different segments of the target adopter population (e.g., leaders, caregivers,
populations) and early adopters are identified.

Better ideas

Everett Rogers identifies several key characteristics of the intervention itself 33] that are key determinants of the scalability of the intervention and its rate of
adoption by the broader community. These include the evident superiority of the intervention,
its simplicity, and its alignment with the culture of the new implementers.

Leadership

The crucial role of leadership at all levels for system transformation is well described
35], and the capacity for leading large-scale change needs to be developed as part of
the scale-up process. Leaders can be coached to understand the difference between
simply raising awareness of a new practice and what it takes to lead and ensure its
broad adoption. To get results, IHI has promoted a number of systematic approaches
to engage leaders in their key role of guiding and supporting large-scale change 36], 37].

Communication

The early demonstration phase (Develop the Scalable Unit) is a crucial time for communicating the value of the intervention to both leadership
and the implementers (frontline staff). Providing real-time data is a powerful way
to draw attention and garner support for the next phase of scale-up. Early adopters
and charismatic frontline staff who have successfully implemented the intervention
in this phase become powerful advocates for the intervention to their peers. During
this phase, the “early majority” of Rogers’ Diffusion of Innovations curve 33] are targeted with these communications, while in the Test of Scale-up phase, the audience includes the “late majority,” preparing the ground for more rapid
and extensive scale-up.

Policy

The identification and/or development of regulatory or administrative policies are
an important environmental factor that can either inhibit or expedite the adoption
of specific interventions. Policies that create negative financial or procedural incentives
function as barriers to adoption by making the desired actions more difficult to test
and sustain. Conversely, policies associated with positive incentives can enhance
the motivation to change behavior.

Culture of urgency and persistence

Leaders of initiatives that are intended to achieve results at scale should consider
several key questions when they begin their initial planning, including why others
would want to join the effort and whether there is a glaring gap in performance or
an urgent need 38]. Responses to these questions serve as a barometer for the amount of will and energy
needed to stay the course in bringing interventions to—and achieving results at—full
scale. While the levels of will and energy may fluctuate over the course of an initiative,
the other adoption mechanisms described above can help to enhance adopters’ ability
to sustain their efforts.

Support systems

This phased scale-up approach needs a supporting infrastructure. The following components
of support should be considered in a scale-up design from the outset:

Human capability for scale-up

The expanding QI capability needs of a scale-up initiative should be anticipated early
in the Set-up phase. While frontline staff can be trained in basic QI methods, scale-up will require
team leaders who can use change management approaches to guide and mentor teams at
the front line and improvement specialists who can lead and design QI-based programs
for those who need additional training. The project needs be able to communicate quantitative
results and the underlying stories of success and challenge. Data managers need training
in analytic and reporting capabilities that are best suited to QI methods (e.g., run
charts and statistical process control).

Infrastructure for scale-up

Ideally, scale-up can be achieved primarily through redesign rather than addition
of new resources, such as hiring new staff, but the Develop the Scalable Unit phase may reveal resource constraints that cannot be overcome through system redesign.
Common structural considerations include additional tools (e.g., checklists, data
capture systems), communication systems (e.g., materials and messages, mentoring relationships,
structured programs), and key personnel (e.g., data capturers, quality improvement
mentors) that are specifically assigned to enable better system performance.

Data collection and reporting systems

Having reliable systems that track and provide feedback on the performance of key
processes and outcomes is essential to any scale-up initiative. While some ad hoc
data collection is essential to track new ideas, particularly in the demonstration
and scale-up testing phases, large-scale implementation cannot occur or be sustained
unless routine data systems are accurate, complete, and timely. In addition, data
that tracks key processes and outcomes that are targeted by the intervention need
to be shared frequently with frontline staff and system leaders to inform ongoing
improvement.

Learning systems

Large-scale change requires a mechanism for collecting, vetting, and rapidly sharing
change ideas or interventions. The Develop the Scalable Unit phase is the most intensive period of innovation, usually resulting in a large array
of change ideas that are being tested and require vetting. During this phase, change
ideas that are shown to result in improved performance are assembled into a change
package. The BTS model is an effective way to share knowledge between units that are
undertaking similar work 39]. Proven change ideas, tested in a variety of settings, that are assembled into the
change package during the early phases can be disseminated with confidence and little
further modification when the initiative goes to full scale.

Design for sustainability

Sustainability is a key design consideration throughout the three activity phases
of getting to full scale—Develop the Scalable Unit, Test of Scale-up, and Go to Full Scale. The learnings about sustainability at each phase should be built into the expanding
change package. The activities associated with sustainability are well described 40] (e.g., high reliability of the new processes, inspection systems to ensure desired
results are being achieved, support for structural elements, ongoing learning systems),
and their purpose should be to ensure that the system cannot revert to its prior state
of performance. In addition, leadership commitment is required to continue to nourish
and replenish these key support elements for scale-up. To sustain the scale-up process,
leaders need to commit to a learning system that includes the continuous feedback
of data to identify and close gaps in performance 41].

Methods of implementation

While a range of methods 38] can be deployed to achieve the aims of each of the phases of the Framework for Going
to Full Scale (Table 2), the Model for Improvement 42] is a foundational element of adaptive design that we believe is required to address
improvement in the different contexts encountered throughout scale-up. The Set-up phase gathers information about the change and the system within which the change
will be taken to full scale; the Develop the Scalable Unit phase uses structured improvement methods to learn deeply from a small number of
sites; the Test of Scale-up phase uses methods that engage a larger number of sites in testing the intervention
under a wider set of contexts and settings; and the Go to Full Scale phase uses methods that have been shown to be effective in large-scale initiatives.

Table 2. Methods of implementation that can be used with each scale-up phase

Case examples

Context

The centralized and integrated health systems typical of African countries build off
a policy framework that is founded on evidence-based knowledge. Those policies are
implemented through guidelines, protocols, and associated clinical training and supported
by resources required to deliver those programs. Often, programs are also supported
by reporting systems to track performance. Recent specific efforts to scale up comprehensive
interventions directed at saving lives of infants and children in Africa have not
consistently shown evidence of improved outcomes 43], 44]. These failures underscore the major challenges faced when taking existing evidence-based
interventions and promising new interventions to full scale rapidly within a variety
of different contexts. We describe two case examples—one from South Africa and one
from Ghana—that used a sequential approach, accompanied by spread and infrastructural
support efforts, to take a set of interventions to full national scale within a few
years.

South Africa: improving perinatal PMTCT of HIV

South Africa has more HIV-infected people than any other nation and more than 30 %
of pregnant women infected with HIV. The prevention of mother-to-child transmission
(PMTCT) program was grafted onto South Africa’s well-attended antenatal care service
platform, offering, in a primary care setting, the multi-step PMTCT processes of care:
testing pregnant mothers for HIV, initiating antiretroviral treatment, and testing
babies for evidence of infection at 6 weeks of age. The South African PMTCT program
was launched reluctantly by the existing political administration, which may have
affected the evolution of the PMTCT scale-up 3]. Given the lack of familiarity with QI methods in South Africa at the time, and the
political ambivalence about addressing the HIV epidemic, the project was designed
initially as a demonstration of effectiveness of a scalable intervention, without
knowing if it would be scaled.

The set-up phase lasted two and a half years because of a struggle to assemble the
political will and leadership required to ensure that the Department of Health (DoH)
led the project. The scalable unit was the health district, but the initial testing and demonstration work was done
in self-contained “wedges” within three health districts; each wedge included a district
hospital and its 25–30 feeder primary care clinics. These initial demonstrations provided
crucial evidence of effectiveness of the approach (rapid improvement in process performance),
experience for district managers, supervisors and provincial leaders of how to lead
and manage using QI methods, and a demonstration to central health system planners
of the effectiveness of QI approaches for improving HIV. With support from local technical
partners, the national DoH then led a test of scale-up of this approach by initiating QI learning collaboratives in five more districts
across the country. This provided opportunities for further refinement of the package
of implementation strategies, testing of data systems, indicators and collection tools,
and, importantly, the opportunity for the government to assume ownership of the process
through its own testing of the process. Following these successful tests, the South
African DoH took full leadership and responsibility for taking the program to full scale, using a set of implementation strategies that led to a decline in HIV transmission
rates from 19 % in 2005 to 5 % in 2010 45]. To get to full scale (52 districts across the country), the intervention expanded
from 3 districts (Develop the Scalable Unit) to 8 (Test of Scale-up) to 52 (Go to Full Scale), demonstrating the opportunity to scale exponentially when using this approach.
The innovation and spread methods used were the Model for Improvement, collaborative
networks (Develop the Scalable Unit, Test of Scale-up), and campaign (Go to Full Scale).

Ghana: national scale-up of MCH programming

In Ghana, a country of 23 million in West Africa, a similar sequence of scale-up unfolded,
except that national scale-up was designed from the outset. In the case of Ghana,
efforts were underway to reach the country’s Millennium Development Goals (MDGs) 4
and 5 (i.e., reduce child and maternal mortality, by 60 and 75 %, respectively) 46]. A national health systems improvement initiative was introduced in Ghana in 2008
to supplement and accelerate Ghana’s existing maternal and child health (MCH) programs
and efforts to reach its MDGs. A set of evidence-based maternal and child survival
interventions already existed. The purpose of the QI project was to improve the reliable
application of those clinical interventions. Partnering with a faith-based health
system, National Catholic Health Service (NCHS), IHI introduced QI methods to more
effectively implement an evidence-based package of clinical interventions developed
by the Ghana Health Service (GHS).

In Ghana, the district was the scalable unit. The district was made up of sub-districts
that included a hospital and other facilities, including primary care clinics. Using
a phased design, the initiative scaled exponentially from the north of the country
to the south. The sequential design and exponential scale-up approach have been described
elsewhere 47]. Starting with the sub-district teams, each district management team built capability
to support the work. In the Test of Scale-up phase as the project scaled across three northern provinces, the project required
significant redesign as it became apparent that more training was required to build
sufficient local capability for using QI methods among district and regional supervisors
and high-level leaders to support the fast-moving scale-up design. Successful demonstrations
of improvement for key maternal and child outcomes (e.g., hospital-based deaths) were
actively disseminated, and these results became a prime motivator for increasing adoption
of the approach by the regional and then national leadership. The disadvantage of
not having the Health Ministry and the GHS involved deeply in the design from the
outset is that it was not possible to work on sustainability mechanisms (e.g., a national
plan to replenish QI mentors who are lost through turnover) until the Ministry developed
major interest near the end of the project. To get to full scale, the project scaled
exponentially from 35 sub-districts in the Develop the Scalable Unit phase, to 265 sub-districts in the Test of Scale-up phase, to 554 sub-districts in the Go to Full Scale phase over 6 years. The Breakthrough Series Collaborative design was the primary
method of learning and spread at each phase of the project. The project reached more
than 80 % of all public and faith-based hospitals in the country.