Thank you for reading experimental and quasi experimental designs for research. Maybe you have knowledge that, people have search numerous times for their favorite readings like this experimental and quasi experimental designs for research, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious virus inside their laptop.

Experimental And Quasi Experimental Designs For Research

America's health care system has become too complex and costly to continue business as usual. Best Care at Lower Cost explains that inefficiencies, an overwhelming amount of data, and other economic and quality barriers hinder progress in improving health and threaten the nation's economic stability and global competitiveness. According to this report, the knowledge and tools exist to put the health system on the right course to achieve continuous improvement and better quality care at a lower cost.The costs of the system's current inefficiency underscore the urgent need for a systemwide transformation. About 30 percent of health spending in 2009--roughly $750 billion--was wasted on unnecessary services, excessive administrative costs, fraud, and other problems. Moreover, inefficiencies cause needless suffering. By one estimate, roughly 75,000 deaths might have been averted in 2005 if every state had delivered care at the quality level of the best performing state. This report states that the way health care providers currently train, practice, and learn new information cannot keep pace with the flood of research discoveries and technological advances.About 75 million Americans have more than one chronic condition, requiring coordination among multiple specialists and therapies, which can increase the potential for miscommunication, misdiagnosis, potentially conflicting interventions, and dangerous drug interactions. Best Care at Lower Cost emphasizes that a better use of data is a critical element of a continuously improving health system, such as mobile technologies and electronic health records that offer significant potential to capture and share health data better. In order for this to occur, the National Coordinator for Health Information Technology, IT developers, and standard-setting organizations should ensure that these systems are robust and interoperable. Clinicians and care organizations should fully adopt these technologies, and patients should be encouraged to use tools, such as personal health information portals, to actively engage in their care.This book is a call to action that will guide health care providers; administrators; caregivers; policy makers; health professionals; federal, state, and local government agencies; private and public health organizations; and educational institutions.

Best Care at Lower Cost: The Path to Continuously Learning Health Care in America

The difference in difference (DID) design is a quasi-experimental research design that researchers often use to study causal relationships in public health settings where randomized controlled trials (RCTs) are infeasible or unethical. However, causal inference poses many challenges in DID designs. In this article, we review key features of DID designs with an emphasis on public health policy research. Contemporary researchers should take an active approach to the design of DID studies, seeking to construct comparison groups, sensitivity analyses, and robustness checks that help validate the method's assumptions. We explain the key assumptions of the design and discuss analytic tactics, supplementary analysis, and approaches to statistical inference that are often important in applied research. The DID design is not a perfect substitute for randomized experiments, but it often represents a feasible way to learn about casual relationships. We conclude by noting that combining elements from multiple quasi-experimental techniques may be important in the next wave of innovations to the DID approach.

Designing Difference in Difference Studies: Best Practices for Public Health Policy Research

This paper discusses the empirical literature on the economic consequences of disclosure and financial reporting regulation, drawing on U.S. and international evidence. Given the policy relevance of research on regulation, we highlight the challenges with (1) quantifying regulatory costs and benefits, (2) measuring disclosure and reporting outcomes, and (3) drawing causal inferences from regulatory studies. Next, we discuss empirical studies that link disclosure and reporting activities to firm-specific and market-wide economic outcomes. Understanding these links is important when evaluating regulation. We then synthesize the empirical evidence on the economic effects of disclosure regulation and reporting standards, including the evidence on International Financial Reporting Standards (IFRS) adoption. Several important conclusions emerge. We generally lack evidence on market-wide effects and externalities from regulation, yet such evidence is central to the economic justification of regulation. Moreover, evidence on causal effects of disclosure and reporting regulation is still relatively rare. We also lack evidence on the real effects of such regulation. These limitations provide many research opportunities. We conclude with several specific suggestions for future research.

The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research

Strategic Asset Allocation: Portfolio Choice for Long‐Term Investors.

In April 2006, the state of Massachusetts passed legislation aimed at achieving near universal health insurance coverage. A key provision of this legislation, and of the national legislation passed in March 2010, is an individual mandate to obtain health insurance. Although previous researchers have studied the impact of expansions in health insurance coverage among the indigent, children, and the elderly, the Massachusetts reform gives us a novel opportunity to examine the impact of expansion to near-universal health insurance coverage among the entire state population. In this paper, we are the first to use hospital data to examine the impact of this legislation on insurance coverage, utilization patterns, and patient outcomes in Massachusetts. We use a difference-in-difference strategy that compares outcomes in Massachusetts after the reform to outcomes in Massachusetts before the reform and to outcomes in other states. We embed this strategy in an instrumental variable framework to examine the effect of insurance coverage on utilization patterns. Using the Current Population Survey, we find that the reform increased insurance coverage among the general Massachusetts population. Our main source of data is a nationally-representative sample of approximately 20% of hospitals in the United States. Among the population of hospital discharges in Massachusetts, the reform decreased uninsurance by 36% relative to its initial level. We also find that the reform affected utilization patterns by decreasing length of stay and the number of inpatient admissions originating from the emergency room. Using new measures of preventive care, we find some evidence that hospitalizations for preventable conditions were reduced. The reform affected nearly all age, gender, income, and race categories. We also examine costs on the hospital level and find that hospital cost growth did not increase after the reform in Massachusetts relative to other states.

The Impact of Health Care Reform On Hospital and Preventive Care: Evidence from Massachusetts

Traditional models of insurance choice are predicated on fully informed and rational consumers protecting themselves from exposure to financial risk. In practice, choosing an insurance plan is a complicated decision often made without full information. In this paper we combine new administrative data on health plan choices and claims with unique survey data on consumer information to identify risk preferences, information frictions, and hassle costs. Our additional friction measures are important predictors of choices and meaningfully impact risk preference estimates. We study the implications of counterfactual insurance allocations to illustrate the importance of distinguishing between these micro-foundations for welfare analysis.

/pdf/health-insurance-for-humans-information-frictions-plan-2xyza6han4.pdf

Health Insurance for "Humans": Information Frictions, Plan Choice, and Consumer Welfare.

The payment approach known as “pay-for-performance” has been widely adopted with the aim of improving the quality of health care. Nonetheless, little is known about how to use the approach most effectively to improve care. We examined the effects in 260 hospitals of a pay-for-performance demonstration project carried out by the Centers for Medicare and Medicaid Services in partnership with Premier Inc., a nationwide hospital system. We compared these results to those of a control group of 780 hospitals not in the demonstration project. The performance of the hospitals in the project initially improved more than the performance of the control group: More than half of the pay-for-performance hospitals achieved high performance scores, compared to fewer than a third of the control hospitals. However, after five years, the two groups’ scores were virtually identical. Improvements were largest among hospitals that were eligible for larger bonuses, were well financed, or operated in less competitive markets. Th...

The effect of pay-for-performance in hospitals: lessons for quality improvement.

https://www.nber.org/system/files/working_papers/w16012/w16012.pdf

https://scholar.harvard.edu/files/laibson/files/consumers_jhe_sept2013.pdf

Jonathan T. Kolstad

Papers

The Impact of Health Care Reform On Hospital and Preventive Care: Evidence from Massachusetts

Health Insurance for "Humans": Information Frictions, Plan Choice, and Consumer Welfare.

The effect of pay-for-performance in hospitals: lessons for quality improvement.

The Impact of Health Care Reform On Hospital and Preventive Care: Evidence from Massachusetts

Consumers' Misunderstanding of Health Insurance.