Upload
ids
View
1.960
Download
0
Embed Size (px)
Citation preview
Beyond Scaling Up
Pathways to Scaling up Health Services in Complex Adaptive
Systems
Ligia Paina & David Peters
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?
Do we have the right models for scaling up?
3
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 review4
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
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
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
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
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
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
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
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
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
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
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
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