This document discusses scaling up health services in complex adaptive systems. It argues that health systems behave like complex adaptive systems, characterized by heterogeneous actors that interact in dynamic and unpredictable ways. Scaling up is therefore not a linear or controlled process. The document outlines several concepts from complexity science that are relevant to scaling up, such as feedback loops, emergent behavior, tipping points, and path dependence. It suggests using theories and methods from complexity science to better understand scaling up and facilitate decision making. Key lessons are that scaling up requires flexibility, recognizing local conditions, and developing sustainable institutions over the long term through learning-based approaches.

1. Beyond Scaling Up
Pathways to Scaling up Health
Services in Complex Adaptive
Systems
Ligia Paina & David Peters

2. 2
The Problems of Scaling Up
 Many effective health interventions
known, but are not reaching universal
coverage
 Not known which models for scaling up
work best
 How can global health initiatives take
advantage of knowledge on scaling up?

3. Do we have the right models for
scaling up?
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4. Models for Scaling Up Health
Services: Two Views
Domain Scaling up to
Reach the MDGs
Scaling up Innovations and
Pilot Projects
Defining
Concerns
“Becoming large”;
more people
reached
Expanding impact, becoming
sustainable in quantitative,
functional, organizational,
political terms
Time Frame Short to medium
term
Medium to long term
Funding Money is a binding
constraint
Money is necessary but not
sufficient
Absorptive
Capacity
Ability to spend
external funds
Ability to find a fit between
capabilities of beneficiaries,
programs, and organizations
Subramanian et al (2010). Under review
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5. Misalignment between scaling up
assumptions and health system behavior
Scaling up
assumptions
Linear, blueprint process
Simplistic, deterministic
Standardized methods for
predicting human and
financial resources
Little adaptation to
emerging issues
Health system
behavior
Highly heterogeneous
groups of actors
Multiple levels, services,
and functions
Dynamic change
Rooted in unique local
context
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6. Complex Adaptive Systems (CAS):
Pathways to Scaling Up
 CAS involve large number of interacting
agents with adaptive capabilities in changing
environment
 Not conventionally “controlled”
 Not fully predictable
 Unintended consequences frequent
 Health systems behave like CAS
 Scaling up is better understood through CAS
phenomena
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7. Why CAS Phenomena are
Relevant to Scaling Up
 Intervention that may work on a small
scale or in one context cannot be simply
replicated elsewhere on a large scale
 “Control” over behaviors of communities
and providers is limited in real world
 Large efforts can produce small effects,
and small stimuli can create large changes
 Implementation is highly variable and
changing
 Even simple public health interventions
involve complex social interventions
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8. Path dependence: “History
matters”
 Single events can have system-wide
effects that persist for a long time
 Outcomes sensitive to initial
conditions and bifurcations/choices
along the way
 Complicates predictions of a system’s
evolution
Example: Can’t cut & paste reforms
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9. Feedback loops: “Vicious” and
“Virtuous” Circles
 An output of a process within
the system is fed back into the
same system
 Used to analyze variations in
supply and demand for health
services
Example: health & poverty
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10. Scale-free networks
 Networks which are dominated by
few hubs with an unlimited number
of preferentially attached links
 Provide insights into system entry
points and the diffusion of
knowledge, technology, and
practices
Example: Spread of HIV
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11. Emergent behavior
 The whole is greater than sum of parts:
the spontaneous creation of order –
small entities jointly contribute to
complicated behaviors
 Health system actors self-organize in
response to rapid changes, new policies
Example: Boda Boda drivers organize to
transport women for ANC and delivery
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12. Phase transitions
 Tipping points that occur when
radical changes take place in
features of health system
parameters as they reach certain
critical points
 Threshold effects and sometimes
abrupt changes happen in health
systems
Example: Rapid adoption of a policy
stalled for years.
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13. How CAS Can Inform Scaling Up
 Better understanding of dynamics between the
health system, contextual factors, and
population health
 Identify root causes of variations in service
delivery
 Identify multi-sectoral factors which promote
the diffusion of innovation in complex systems
 Better understanding of intended and
unintended consequences
 New tools and approaches to understand and
facilitate decision-making
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14. Relevant Theories and Methodologies
 Systems science
 Non-linear dynamics
and chaos theory
 Systems theory and
cybernetics
 Chaos theory
 Theory of critical
phenomena
 Agent-based modeling
 Network analysis
 Scenario modeling
 Sensitivity analysis
 Statistics of extreme
events
 Non-equilibrium
statistics (physics)
 Large-scale data
mining
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15. Revisiting assumptions behind scaling up
and other rapid health system change
 Understand dynamic health system
relationships
 Involve key, multi-sector policy and planning
stakeholders
 Ensure flexibility to adapt to emerging issues
 Recognize local conditions
 Maintain vision for long-term sustainability
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16. Lessons to be learned
 Scaling up is not predictable or controlled:
scrap the blueprint
 Employ “theories of change” to build local
organizational, functional, and political
capabilities
 Should develop sustainable institutions
 Use “learning by doing” approaches: use data,
engage key stakeholders, problem-solving
strategies
 Identify constraints and complex pathways
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