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Methods and Metrics Issues in Delivery System Research

Methods and Metrics Issues in Delivery System Research. The Challenge and Promise of Delivery System Research: A Meeting of AHRQ Grantees, Experts, and Stakeholders Doubletree Dulles – Sterling, Virginia February 16, 2011 . JEFF ALEXANDER The University of Michigan. Challenges.

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Methods and Metrics Issues in Delivery System Research

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  1. Methods and Metrics Issues in Delivery System Research The Challenge and Promise of Delivery System Research: A Meeting of AHRQ Grantees, Experts, and Stakeholders Doubletree Dulles – Sterling, Virginia February 16, 2011 JEFF ALEXANDER The University of Michigan

  2. Challenges • Framing the issues around a common theme • Striking a balance between depth and breadth • Pushing the envelope without being unrealistic • Examples from proposals

  3. What do We Want to Know about Delivery System Effectiveness? • Can it work? • Will it work? • When will it work (for which patients and settings)? • What is necessary for it to work? • Is it worth it? • Value • How can we put it into practice? • Implementation research

  4. Delivery System Change • Manner in which a given DSC is implemented and practiced is influenced by range of factors surrounding the specific change • These factors constitute the social system or social context of change • These factors may explain why, when, and how a DSC works (or not)

  5. Limits of Experimentalism • “Experimentalists have pursued too single-mindedly the question of whether a [social] program works at the expense of knowing why it works.” • Pawson and Tilley (1997) • “….. although the OXO model seeks generalizable knowledge, in that pursuit it relies on – it depends on – removing most of the local details about “how” something works and about the “what” of contexts.” • Berwick (2008)

  6. Five Methods and Metrics Issues • Modeling intervention context • Assessing intervention fidelity and sustainability • Incorporating time in delivery system models • Measuring readiness for change • Assessing complex, multi-component interventions

  7. Modeling Intervention Context • Interventions can display different strengths, causal directions, and base rates depending on the ecological conditions under which the processes or programs are observed • Measure context as an analytic variable in our models- not as study setting descriptions • Analytic techniques for assessing contextual effects are robust- concepts and measures of context are not

  8. Assessing Intervention Fidelity and Sustainability • Measurement of treatment fidelity provides a means for determining whether key program components were delivered as specified by the program logic model/theory • Sustainability- are intervention components active long enough to produce the desired effect on individuals? • Fidelity and sustainability follow from interrelationships among a range of internal and external factors that constitute the social system that surrounds the intervention- not just individual attitudes.

  9. Time • Time as an important analytic concept in its own right, not simply as an element of the research design • Individual growth trajectories • Organizational/ system level developmental trajectories

  10. Readiness for Change • Not all delivery systems/organizations are good candidates for interventions and change • Readiness for change a precursor to the successful implementation of complex changes in health care settings • Readiness measured as collective motivation and collective capability

  11. Complex, Multi-Component Interventions • How the combined effects of multiple intervention elements affect outcomes • How, and the extent to which, individual elements of the intervention contribute to these collective efforts • Mixed methods designs are difficult to implement in a manner that creates synergistic benefits from the use of different forms of data collection and analysis.

  12. General Recommendations • Don’t reject traditional designs- complement them • Consider methods and metrics challenges as a package of related issues • Increase the synergistic value of mixed methods research • Time as an analytic variable • Robust theoretical frameworks to guide the application of analytic methods and measures

  13. From DSC Model to Practice GOAL: To increase the adoption, reach and impact of DSC Science Push Investigation, improvement and communication of DSC for widespread use Delivery Capacity Building the capacity of relevant systems to deliver improved care Market Pull/Demand Building a market and demand for DSC Increase the number of systems providing evidence-based DSC Increase the number of practitioners providing evidence-based DSC Increase the number of individuals receiving evidence-based DSC ULTIMATE GOAL: Improve patient health and well being

  14. Thank you

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