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Pathways to scaling up health services Peters and Paina

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Presentation given at the Global Symposium on Health Systems Research on Complex Adaptive Systems

Presentation given at the Global Symposium on Health Systems Research on Complex Adaptive Systems

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  • 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

Transcript

  • 1. 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?
  • 3. Do we have the right models for scaling up?
  • 4. Models for Scaling Up Health Services: Two Views Subramanian et al (2010). Under review 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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