Alexandermultilevel Analysis4

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Information about Alexandermultilevel Analysis4

Published on October 6, 2007

Author: ShawnHoke

Source: slideshare.net

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The Science and Practice of Implementation : Are We headed Down the Right Path?

The Science and Practice of Implementation : Are We headed Down the Right Path? 10th Biennial Regenstrief Conference Emerging Perspectives on Transformational Change in Healthcare Systems   October   2 – October   4, 2007. Jeff Alexander, Ph.D. Department of Health Management and Policy University of Michigan School of Public Health

Goals Problems with Quality Improvement Research- why it’s not contributing to systems change State of the art- Implementation Research- good, bad, and ugly Modest proposals for advancing the science of implementation and the usefulness of QI research

Problems with Quality Improvement Research- why it’s not contributing to systems change

State of the art- Implementation Research- good, bad, and ugly

Modest proposals for advancing the science of implementation and the usefulness of QI research

State of the Art – QI Research Single organizational samples Opportunistic not systematic Short duration studies No replication of studies No explicit consideration of context No explicit consideration of implementation

Single organizational samples

Opportunistic not systematic

Short duration studies

No replication of studies

No explicit consideration of context

No explicit consideration of implementation

Bottom line: We don’t know what works, when it works, or where it works

Bottom line:

We don’t know what works, when it works, or where it works

BEYOND THE LINEAR MODEL Basic Research Clinical Trial (Efficacy) Treatment Development Effectiveness Trial Treatment Deployment

Problems with the Linear Model of Implementation Little on causal pathways & nested interconnected structures and activities Little influence of OT & OB in QI studies RCT thinking: control context away

Little on causal pathways & nested interconnected structures and activities

Little influence of OT & OB in QI studies

RCT thinking: control context away

RCTs as the Gold Standard? Great for testing efficacy of molar interventions Not so great for assessing: Process related phenomena Complex interactions among program components Contextual effects implementation

Great for testing efficacy of molar interventions

Not so great for assessing:

Process related phenomena

Complex interactions among program components

Contextual effects

implementation

Background Health care provided in organizational context behavior of clinicians influenced by the organizations in which they work recognition of the interconnections among components of organizations (clinical teams function within hospitals, interact with other clinical teams, support systems - embeddedness

Health care provided in organizational context

behavior of clinicians influenced by the organizations in which they work

recognition of the interconnections among components of organizations (clinical teams function within hospitals, interact with other clinical teams, support systems - embeddedness

Implementation-State of the Art Emerging lists of “best practices” Continued assumption, encouraged by funding streams, of linear development of interventions Anecdotal information on implementation Some efforts to produce models, theories to test implementation

Emerging lists of “best practices”

Continued assumption, encouraged by funding streams, of linear development of interventions

Anecdotal information on implementation

Some efforts to produce models, theories to test implementation

Implementation: the influence of content, context, and process Implementation Content Process Opinion leaders, change champion Systemic processes (e.g., supervisory practices, quality improvement) Organizational learning Triability Innovation type Evidence interpretation and packaging Internal: Organizational culture Organizational structure Practice setting characteristics Local stakeholders (e.g., attitudes and behaviors) Resources External: Networks regulation Economic (e.g., reimbursement) Competition Context

Process

Opinion leaders, change champion

Systemic processes (e.g., supervisory practices, quality improvement)

Organizational learning

Triability

Innovation type

Evidence interpretation and packaging

Internal:

Organizational culture

Organizational structure

Practice setting characteristics

Local stakeholders (e.g., attitudes and behaviors)

Resources

External:

Networks

regulation

Economic (e.g., reimbursement)

Competition

Klein and Sora Model Management Support : Management communicates a rationale and priority Implementation Effectiveness : Consistency and quality of innovation use Innovation-Values Fit : The perceived fit between the end-user's values and the innovation Champion(s) : Champion(s) promotes the innovation with targeted org members and/or management Financial Resource Availability : Resources are made available to support implementation policies and practices Implementation Climate : The innovation is perceived as an organizational priority by targeted end users Implementation Policies and Practices : Formal organizational actions ensure user skills, create incentives and/or identify and address barriers to use

Why Context Matters Context may affect implementation directly Context may moderate the relationship between an innovation and outcomes of interest Context may establish the external validity of both implementation and QI research

Context may affect implementation directly

Context may moderate the relationship between an innovation and outcomes of interest

Context may establish the external validity of both implementation and QI research

Problems with Implementation Context Measurement and Analyses assigning the same group value to all members of a group aggregating individual outcomes to the group level Separate analyses of organizational and individual phenomena

assigning the same group value to all members of a group

aggregating individual outcomes to the group level

Separate analyses of organizational and individual phenomena

Advantages of Multilevel Designs statistically efficient estimates of regression coefficients Use of clustering information provides correct standard errors, confidence intervals and significance tests Allows for uneven assessments and different program tenures (for longitudinal studies)

statistically efficient estimates of regression coefficients

Use of clustering information provides correct standard errors, confidence intervals and significance tests

Allows for uneven assessments and different program tenures (for longitudinal studies)

Advantages of MLD Measurement at any of the levels of a hierarchy enables examination of whether differences in average outcomes between organizations are explained by factors such as organizational practices/structures , or other characteristics of individual patients or providers

Measurement at any of the levels of a hierarchy enables examination of whether differences in average outcomes between organizations are explained by factors such as organizational practices/structures , or other characteristics of individual patients or providers

 

Potential applications of MLD Effects of organizational infrastructure on implementation in micro teams Effects of org. culture on individual provider attitudes and behavior (e.g. physician use of clinical guidelines) Translational research Effects of micro-team structure and process on patient outcomes

Effects of organizational infrastructure on implementation in micro teams

Effects of org. culture on individual provider attitudes and behavior (e.g. physician use of clinical guidelines)

Translational research

Effects of micro-team structure and process on patient outcomes

Issues with Multilevel Analysis Data requirements Statistical power Analysis and interpretation issues

Data requirements

Statistical power

Analysis and interpretation issues

Multi-method Designs Quantitative-Qualitative RCT-case study Process study-outcome study Sustainability- long term studies

Quantitative-Qualitative

RCT-case study

Process study-outcome study

Sustainability- long term studies

Life Cycle of Quality Improvement

Science of Complexity Assumptions regarding interactions among components or “agents” of the system Heterogeneity- agents differ in important characteristics (e.g. preferences) Dynamic-agents change, how system changes are non linear, chaotic Feedback- change often results from feedback that agents receive from their own behavior

Assumptions regarding interactions among

components or “agents” of the system

Heterogeneity- agents differ in important characteristics (e.g. preferences)

Dynamic-agents change, how system changes are non linear, chaotic

Feedback- change often results from feedback that agents receive from their own behavior

Complexity Science Organization- agents organized into groups or hierarchies that influence how system evolves over time Emergent behavior- what results from the actions and interaction of individual agents

Organization- agents organized into groups or hierarchies that influence how system evolves over time

Emergent behavior- what results from the actions and interaction of individual agents

Engaged Scholarship Theory Solution Model Reality Problem Formulation Theory Building Research Design Problem Solving Describe Problem/Issue - visit & study it - map & diagnose it Formulate the Question - from users’ perspective? Criterion - Relevance Answers & Arguments - plausible alternatives - clarify context - identify key variations - cross levels of abstraction Criterion - Validity Obtain the Evidence - case/field/experimental study - unit selection & sampling - measurement & observation - data analysis Criterion - Truth Application & Implementation - knowledge for what? who? - for science & profession - apply findings to problem - develop implementation plan Criterion - Impact

Answers & Arguments

- plausible alternatives

- clarify context

- identify key variations

- cross levels of abstraction

Criterion - Validity

Capacity Building for Implementation Research Data Funding Multi-disciplinary teams Make implementation part of the intervention Bring in users of intervention/innovation Long term studies Basic research on implementation

Data

Funding

Multi-disciplinary teams

Make implementation part of the intervention

Bring in users of intervention/innovation

Long term studies

Basic research on implementation

Other questions What aspects of care are modular and what aspects are inherently interdependent? To what extent can one element of a system be altered without consequences to other elements? Should intervention content and context dictate implementation process?

What aspects of care are modular and what aspects are inherently interdependent?

To what extent can one element of a system be altered without consequences to other elements?

Should intervention content and context dictate implementation process?

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