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

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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
  • Pathways to scaling up health services Peters and Paina

    1. 1. Beyond Scaling Up Pathways to Scaling up Health Services in Complex Adaptive Systems Ligia Paina & David Peters
    2. 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. 3. Do we have the right models for scaling up? 3
    4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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