System Research JEFF ALEXANDER The University of Michigan The Challenge and Promise of Delivery System Research A Meeting of AHRQ Grantees Experts and Stakeholders Doubletree Dulles Sterling Virginia ID: 259856
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Methods and Metrics Issues in Delivery System Research
JEFF ALEXANDERThe University of Michigan
The Challenge and Promise of Delivery System Research: A Meeting of AHRQ Grantees, Experts, and Stakeholders
Doubletree Dulles – Sterling, Virginia
February 16, 2011 Slide2
Framing the issues around a common themeStriking a balance between depth and breadthPushing the envelope without being unrealisticExamples from proposals
Challenges
2Slide3
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?ValueHow can we put it into practice?Implementation research
What do We Want to Know about Delivery System Effectiveness?
3Slide4
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)
Delivery System Change4Slide5
“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)
Limits of Experimentalism
5Slide6
Modeling intervention contextAssessing intervention fidelity and sustainabilityIncorporating time in delivery system modelsMeasuring readiness for changeAssessing complex, multi-component interventions
Five Methods and Metrics Issues
6Slide7
Interventions can display different strengths, causal directions, and base rates depending on the ecological conditions under which the processes or programs are observedMeasure context as an analytic variable in our models- not as study setting descriptionsAnalytic techniques for assessing contextual effects are robust- concepts and measures of context are not
Modeling Intervention
C
ontext
7Slide8
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.
A
ssessing Intervention Fidelity and Sustainability8Slide9
Time as an important analytic concept in its own right, not simply as an element of the research designIndividual growth trajectoriesOrganizational/ system level developmental trajectories
Time
9Slide10
Not all delivery systems/organizations are good candidates for interventions and changeReadiness for change a precursor to the successful implementation of complex changes in health care settingsReadiness measured as collective motivation and collective capability
Readiness for Change
10Slide11
How the combined effects of multiple intervention elements affect outcomesHow, and the extent to which, individual elements of the intervention contribute to these collective effortsMixed methods designs are difficult to implement in a manner that creates synergistic benefits from the use of different forms of data collection and analysis.
Complex
,
Multi-Component Interventions
11Slide12
Don’t reject traditional designs- complement themConsider methods and metrics challenges as a package of related issuesIncrease the synergistic value of mixed methods researchTime as an analytic variable
Robust theoretical frameworks to guide the application of analytic methods and measures General Recommendations
12Slide13
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 useDelivery CapacityBuilding 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
13Slide14
Thank you
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