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.
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Beyond Scaling Up: Understanding Health Systems as Complex Adaptive Systems
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?
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
4
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
5
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
6
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
7
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
8
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
9
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
10
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
11
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.
12
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
13
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
14
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
15
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
16
Notas do Editor
To set the stage for country case studies, summarizes the examples of scaling up health services.
Key message: Scaling-up efforts to date have not been able to account for the dynamic and complex nature of health systems, particularly those in developing countries.
CAS phenomena provide a deeper understanding of the pathways to scaling up
Key message:
What is it, non-health example
Why is it important for health systems
Health system example
Key message:
What is it, non-health example
Why is it important for health systems
Health system example
Key message:
What is it, non-health example
Why is it important for health systems
Health system example
Key message:
What is it, non-health example
Why is it important for health systems
Health system example
Key message:
What is it, non-health example
Why is it important for health systems
Health system example