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Methods and Metrics Issues in Delivery Methods and Metrics Issues in Delivery

Methods and Metrics Issues in Delivery - PowerPoint Presentation

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Methods and Metrics Issues in Delivery - PPT Presentation

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

intervention system delivery dsc system intervention dsc delivery methods fidelity factors social research context works work interventions analytic time

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Slide1

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

14