Evaluation for health policy and health care: a contemporary data-driven approach

Content:

List of Figures and Tables

Preface

Acknowledgments

About the Editors

PART I. SETTING UP FOR EVALUATION
Chapter 1. Introduction
Background: Challenges and Opportunities

Evaluation and Health Care Delivery System Transformation

The Global Context for Considering Evaluation Methods and Evidence-Based Decision Making

Book’s Intent

Chapter 2. Setting the Stage
Typology for Program Evaluation

Planning an Evaluation: How Are the Changes Expected to Occur?

Developing Evaluations: Some Preliminary Methodological Thoughts

Prospectively Planned and Integrated Program Evaluation

Summary

Chapter 3. Measurement and Data
Guiding Principles

Measure Types

Measures of Structure

Measures of Process

Measures of Outcomes

Selecting Appropriate Measures

Data Sources

Looking Ahead

Summary


PART II. EVALUATION METHODS
Chapter 4. Causality and Real-World Evaluation
Evaluating Program/Policy Effectiveness: The Basics of Inferring Causality

Defining Causality

Assignment Mechanisms

Three Key Treatment Effects

Statistical and Real-World Considerations for Estimating Treatment Effects

Summary

Chapter 5. Randomized Designs
Randomized Controlled Trials

Stratified Randomization

Group Randomized Trials

Randomized Designs for Health Care

Summary

Chapter 6. Quasi-experimental Methods: Propensity Score Techniques
Dealing With Selection Bias

Comparison Group Formation and Propensity Scores

Regression and Regression on the Propensity Score to Estimate Treatment Effects

Summary

Chapter 7. Quasi-experimental Methods: Regression Modeling and Analysis
Interrupted Time Series Designs

Comparative Interrupted Time Series

Difference-in-Difference Designs

Confounded Designs

Instrument Variables to Estimate Treatment Effects

Regression Discontinuity to Estimate Treatment Effects

Fuzzy Regression Discontinuity Design

Additional Considerations: Dealing With Nonindependent Data

Summary

Chapter 8. Treatment Effect Variations Among the Treatment Group
Context: Factors Internal to the Organization

Evaluation Approaches and Data Sources to Incorporate Contextual Factors

Context: External Factors That Affect the Delivery or Potential Effectiveness of the Treatment

Individual-Level Factors That May Cause Treatment Effect to Vary

Methods for Examining the Individual Level Heterogeneity of Treatment Effects

Multilevel Factors

Importance of Incorporating Contextual Factors Into an Evaluation

Summary

Chapter 9. The Impact of Organizational Context on Heterogeneity of Outcomes: Lessons for Implementation Science
Context for the Evaluation: Some Examples From Centers for Medicare and Medicaid Innovation

Evaluation for Complex Systems Change

Frameworks for Implementation Research

Organizational Assessment Tools

Analyzing Implementation Characteristics

Summary


PART III. MAKING EVALUATION MORE RELEVANT TO POLICY
Chapter 10. Evaluation Model Case Study: The Learning System at the Center for Medicare and Medicaid Innovation
Step 1: Establish Clear Aims

Step 2: Develop an Explicit Theory of Change

Step 3: Create the Context Necessary for a Test of the Model

Step 4: Develop the Change Strategy

Step 5: Test the Changes

Step 6: Measure Progress Toward Aim

Step 7: Plan for Spread

Summary

Chapter 11. Program Monitoring: Aligning Decision Making With Evaluation
Nature of Decisions

Cases: Examples of Decisions

Evidence Thresholds for Decision Making in Rapid-Cycle Evaluation

Summary

Chapter 12. Alternative Ways of Analyzing Data in Rapid-Cycle Evaluation
Statistical Process Control Methods

Regression Analysis for Rapid-Cycle Evaluation

A Bayesian Approach to Program Evaluation

Summary

Chapter 13. Synthesizing Evaluation Findings
Meta-analysis

Meta-evaluation Development for Health Care Demonstrations

Meta-regression Analysis

Bayesian Meta-analysis

Putting It Together

Summary

Chapter 14. Decision Making Using Evaluation Results
Research, Evaluation, and Policymaking

Program/Policy Decision Making Using Evidence: A Conceptual Model

Multiple Alternatives for Decisions

A Research Evidence/Policy Analysis Example: Socioeconomic Status and the Hospital Readmission Reduction Program

Other Policy Factors Considered

Advice for Researchers and Evaluators

Chapter 15. Communicating Research and Evaluation Results to Policymakers
Suggested Strategies for Addressing Communication Issues

Other Considerations for Tailoring and Presenting Results

Closing Thoughts on Communicating Research Results


Appendix A: The Primer Measure Set

Appendix B: Quasi-experimental Methods That Correct for Selection Bias: Further Comments and Mathematical Derivations
Propensity Score Methods

An Alternative to Propensity Score Methods

Assessing Unconfoundedness

Using Propensity Scores to Estimate Treatment Effects

Unconfounded Design When Assignment Is at the Group Level

Index

Evaluation for Health Policy and Health Care: A Contemporary Data-Driven Approach explores the best practices and applications for producing, synthesizing, visualizing, using, and disseminating health care evaluation research and reports. This graduate-level text will appeal to those interested in cutting-edge health program and health policy evaluation in this era of health care innovation. Editors Steven Sheingold and Anupa Bir’s core text focuses on quantitative, qualitative, and meta-analytic approaches to analysis, providing a guide for both those executing evaluations and those using the data to make policy decisions. It is designed to provide real-world applications within health policy to make learning more accessible and relevant, and to highlight the remaining challenges for using evidence to develop policy.
(https://us.sagepub.com/en-us/nam/evaluation-for-health-policy-and-health-care/book262472#description)