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Multilevel and Multi-method Designs

Multilevel and Multi-method Designs. Capturing the Effects of Organizational Context and Process in Implementation Research AHRQ 2007 Annual Conference: Improving Healthcare, Improving Lives September 26, 2007 Jeff Alexander, Ph.D. Department of Health Management and Policy

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Multilevel and Multi-method Designs

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  1. Multilevel and Multi-method Designs Capturing the Effects of Organizational Context and Process in Implementation Research AHRQ 2007 Annual Conference: Improving Healthcare, Improving Lives September 26, 2007 Jeff Alexander, Ph.D. Department of Health Management and Policy University of Michigan School of Public Health

  2. Background • Health care provided in organizational context • behavior of clinicians influenced by the organizations in which they work • policies (e.g. P4P) vary greatly in how they are implemented and what effects they have at the local level. • recognition of the interconnections among components of organizations (clinical teams function within hospitals, interact with other clinical teams, - embeddedness

  3. 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 Context Internal: • Organizational culture • Organizational structure • Practice setting characteristics • Local stakeholders (e.g., attitudes and behaviors) • Resources External: • Networks • regulation • Economic (e.g., reimbursement) • Competition

  4. 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

  5. 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)

  6. 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

  7. In a multilevel analysis, variance in the dependent variable is decomposed into within and between group components. Two equations result; • a within-unit model: Yij=Boj +rij • and a between-unit model: Boj=Goo +Uoj

  8. Environmental Context • Market Conditions • Managed care penetration • Number of competitors • regulation QI/TQM Implementation • Scope • Number of participating units • % hospital staff on QI Teams • % hospital managers on QI Teams • % MD/FTEs in QI teams • Intensity _# of statistical process tools _#guidelines in use _Use of Quality of care data Potentially Avoidable Adverse Outcomes Risk adjusted mortality Organizational Context -Resources / Infrastructure for QI • financial resources/support • IT • Leadership support

  9. 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

  10. Issues with Multilevel Analysis • Data requirements • Statistical power • Analysis and interpretation issues

  11. Multi-method Designs • Quantitative-Qualitative • RCTs-case study • Process studies-outcome studies • Sustainability- long term studies

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