Suchergebnisse

Preface -- Tables -- Figures -- Acknowledgements -- Introduction -- 1. Acne -- 2. Adolescent preventive services -- 3. Allergic rhinitis -- 4. Asthma -- 5. Attention deficit/hyperactivity disorder -- 6. Cesarean delivery -- 7. Depression -- 8. Developmental screening -- 9. Diabetes mellitus -- 10. Diarrheal disease, acute -- 11. Family planning/contraception -- 12. Fever without source of infection in children under 3 years of age -- 13. Headache -- 14. Immunizations -- 15. Otitis media -- 16. Prenatal care -- 17. Sickle cell screening and select topics in prevention of complications -- 18. Tuberculosis screening -- 19. Upper respiratory infections -- 20. Urinary tract infection -- 21. Vaginitis and sexually transmitted diseases (diagnosis and treatment) -- 22. Well child care -- Appendix A: Definitions for indicator tables -- Appendix B: Panel rating sheets -- Appendix C: Indicator crosswalk tables

AbstractThis chapter provides an overview and exploration of outcomes measurement.

The health services literature contains many articles related to expanding and refining quality measures. But the rich body of empirical research on how people process information has rarely been applied to the challenge of presenting complex information about health care in ways that facilitate its comprehension and use. In this article, the authors review key findings from this research. Based on their review, the authors develop some general principles for presenting information and demonstrate their utility by assessing three Web sites that report performance data.

FrontMatter -- Reviewers -- Foreword -- Preface -- Acknowledgments -- Contents -- Boxes, Figures, and Tables -- Abstract -- Summary -- 1 Introduction -- 2 Approaches to Determining Covered Benefits and Benefit Design -- 3 Policy Foundations and Criteria for the EHB -- 4 Resolving ACA Intent -- 5 Defining the EHB -- 6 Public Deliberation -- 7 Program Monitoring and Research -- 8 Allowance for State Innovation -- 9 Updating the EHB -- Appendix A: Patient Protection and Affordable Care Act, Section 1302, and Web Questions for Public Input -- Appendix B: Stakeholder Decisions on Health Insurance -- Appendix C: Examples of Possible Degrees of Specificity of Inclusions in Small Group and Individual Markets -- Appendix D: Examples of Benefit Package Statutory Guidance -- Appendix E: Description of Small Group Market Benefits, Provided by WellPoint -- Appendix F: General Exclusions -- Appendix G: Medical Necessity.

ABOUT THE SERIES DIRECTIONS IN HEALTH SERVICES RESEARCH AND POLICY -- FOREWORD -- FIGURES -- TABLES -- ACKNOWLEDGMENTS -- INTRODUCTION -- WHAT DO STAKEHOLDERS WANT TO KNOW? -- WHAT IS WRONG WITH CURRENT HEALTH SYSTEM INFORMATION? -- BUILDING AN INTEGRATED INFORMATION SYSTEM -- DATA SOURCES FOR AN INTEGRATED INFORMATION SYSTEM -- WHAT KIND OF DATA SHOULD AN INTEGRATED INFORMATION SYSTEM INCLUDE? -- What Questions Do Stakeholders Want Answered? -- When Do Stakeholders Need Answers? -- What Are the Consequences of Acting on Inaccurate Information? -- WHEN IS EXPERT HELP REQUIRED? -- DATA SOURCES FOR AN INTEGRATED INFORMATION SYSTEM -- ENROLLMENT DATA -- Uses of Enrollment Data -- Problems with Existing Enrollment Data -- ADMINISTRATIVE DATA -- Uses of Administrative Data -- Problems with Existing Administrative Data -- CLINICAL DATA -- Uses of Clinical Data -- Problems with Existing Clinical Data -- SURVEY DATA -- Uses of Survey Data -- Problems with Survey Data -- OTHER DATA SOURCES -- STRATEGIES FOR IMPROVING THE UTILITY OF INTEGRATED INFORMATION SYSTEMS -- FOR MORE DETAIL -- WHAT CONSUMERS OF RESEARCH NEED TO KNOW ABOUT ANALYSIS -- FORMULATING THE QUESTION -- DESIGNING THE STUDY -- IDENTIFYING THE DATA SOURCE -- CONSTRUCTING THE ANALYSIS FILE -- Restricting the File to a Subset of Individuals -- Selecting Variables -- Merging Data from Different Sources -- Initial Data Quality Checks -- SELECTING AN APPROPRIATE ANALYTIC TECHNIQUE -- INTERPRETING THE RESULTS -- FOR MORE DETAIL -- THINKING STRATEGICALLY ABOUT INFORMATION SYSTEMS -- DESCRIPTION OF DATA SOURCES AND RELATED ISSUES -- INTRODUCTION -- DATA SOURCES: EXISTING SYSTEMS -- Administrative Data -- Enrollment Data -- Clinical Data -- Survey Data -- Other Data Sources -- PROBLEMS AND POTENTIAL SOLUTIONS TO IMPROVE THE UTILITY OF HEALTH DATA SYSTEMS.

In 2008, the Massachusetts state legislature mandated an examination of the feasibility of the state's participation in establishing a comparative effectiveness center (CEC) and requested recommendations for the entity's design. "Comparative effectiveness" research involves the direct comparative assessment of the efficacy and cost-effectiveness of health care interventions and strategies. The center's findings would guide purchasing and payment decisions related to medical procedures, devices, drugs, and biologics by public- and private-sector organizations. The state has several options in terms of its approach to comparative effectiveness research. It could establish an interstate CEC that synthesizes existing findings for regional decisionmakers, it could establish an interstate CEC that supports new research, it could join an existing CEC, it could join the Drug Effectiveness Review Project and the Medicare Evidence-Based Decisions Project and also establish a regional center, or it could elect not to establish a CEC at all. An exploration of the options and the types of research that could be sponsored reveals that all of the options are potentially feasible, but the legislature's decision with regard to design must consider the level of prioritization of comparative effectiveness research relative to other approaches to improving health care quality and reducing spending growth.

To avoid changes in current health coverage, the Patient Protection and Affordable Care Act exempts existing plans from some regulations. These exemptions may lead to higher employer-sponsored insurance enrollment and lower government spending.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For California, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in California will fall to 4 percent; without the law, it would remain at 20 percent. The model projects that total state government spending on health care will be 7 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Connecticut, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Connecticut will fall to 5 percent; without the law, it would remain at 11 percent. The model projects that total state government spending on health care will be 10 percent lower for the combined 2011–2020 period than it would be without the ACA, mostly because of federal subsidies for residents who would have been covered by Connecticut's state-run health insurance program (State-Administered General Assistance).

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Montana, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Montana will fall to 3 percent; without the law, it would remain at 18 percent. The model projects that total state government spending on health care will be 3 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Texas, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Texas will fall to 6 percent; without the law, it would remain at 28 percent, the highest in the nation. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.

The Patient Protection and Affordable Care Act (ACA) contains substantial new requirements aimed at increasing rates of health insurance coverage. Because many of these provisions impose additional costs on the states, officials need reliable estimates of the likely impact of the ACA in their state. To demonstrate the usefulness of modeling for state-level decisionmaking, RAND undertook a preliminary analysis of the impact of the ACA on five states—California, Connecticut, Illinois, Montana, and Texas—using the RAND COMPARE microsimulation model. For Illinois, the model predicts that, in 2016 (the year that all of the provisions in the ACA related to coverage expansion will be fully implemented), the uninsured rate in Illinois will fall to 3 percent; without the law, it would remain near 15 percent. The model projects that total state government spending on health care will be 10 percent higher for the combined 2011–2020 period because of the ACA.