Abstractmillions of poor out of vicious cycle of poverty, but that performance has come at a price. The unprecedented growth that we witness today is also rapidly driving resource consumption to unsustainable levels. Local production and consumption-led growth is causing a considerable increase in external costs such as deforestation, and the knock-on effects such as increased emissions, including greenhouse gas (GHG); depletion of non-renewable resources; pollution of rivers; desertification; flooding and long term climate change. Currently, the region accounts for about 40% of GHG emissions, which is expected to exacerbate with the ongoing rapid industrialization and urbanization. By 2030, if the business-as-usual scenario persists, rise to almost 50%. Given the rapidly increasing resource use and associated environmental externalities, the governments in the region are becoming increasingly interested in pursuing the transition to a green economy as it helps achieve a better balance between the environment, the economy, and social welfare. Asia, when compared to the economies of other regions, has the highest rate of policy innovations that help transition to a green economy. However, for a region as big as Asia to make timely and sustainable transition to green economy, fiscal instruments that facilitate transition to a green economy will have to be adopted in a sufficiently large scale. In this paper, we look at the fiscal instruments that are currently adopted in Asia and their effectiveness in decoupling economic growth from emissions, pollution, and resource use, and advancing economic and social well-being. We will compare the design characteristics that influence the potential impact of these instruments and outline incentives and strategies needed to tackle the barriers for widespread adoption of effective fiscal instruments.

Nursing homes may respond to the pressure to reduce costs by reducing quality of care, so the two are related. This study examines the determinants of nursing home costs and cost efficiency, and investigates how various measures of nursing home care quality influence both of these. It applies a one-step stochastic frontier approach to a large panel of California nursing homes surveyed between 2009 and 2013. Quality is measured by three different ratings available on the Nursing Home Compare website: rating on quality measures, rating on the health inspection, and rating on staffing levels. Results show that the rating on quality measures, an outcome-based measure of quality, is inversely related to costs but unrelated to mean cost efficiency. In other words, a better rating on quality measures is associated with lower nursing home costs. The health inspection rating is not associated with either costs or mean cost efficiency. The rating for staffing levels, a structural measure of quality, is negatively associated with cost efficiency. These findings reveal that different measures of quality have different relationships with costs and cost efficiency. The findings suggest that better quality outcomes in nursing homes may be achievable with fewer resources and/or improved care procedures, which in turn should reduce nursing home costs.

This study investigates associations between five-star quality ratings and technical efficiency of nursing homes. The sample consists of a balanced panel of 338 nursing homes in California from 2009 through 2013 and uses two-stage data envelopment (DEA) analysis. The first-stage applies an input oriented variable returns to scale DEA analysis. The second-stage uses a left censored random-effect Tobit regression model. The five-star quality ratings i.e., health inspections, quality measures, staffing available on the Nursing Home Compare website are divided into two categories: outcome and structure form of quality. Results show that quality measures ratings and health inspection ratings, used as outcome form of quality, are not associated with mean technical efficiency. These quality ratings, however, do affect the technical efficiency of a particular nursing home and hence alter the ranking of nursing homes based on efficiency scores. Staffing rating, categorized as a structural form of quality, is negatively associated with mean technical efficiency. These findings show that quality dimensions are associated with technical efficiency in different ways, suggesting that multiple dimensions of quality should be included in the efficiency analysis of nursing homes. They also suggest that patient care can be enhanced through investing more in improving care delivery rather than simply raising the number of staff per resident.

232 Background: Electronic health records (EHRs) are a widely adopted but underutilized source of data for systematic assessment of healthcare quality. Barriers for use of this data source include its vast complexity, lack of structure, and the lack of use of standardized vocabulary and terminology by clinicians. This project aims to develop generalizable algorithms to extract useful knowledge regarding prostate cancer quality metrics from EHRs. Methods: We used EHR ICD-9/10 codes to identify prostate cancer patients receiving care at our academic medical center. Patients were confirmed in the California Cancer Registry (CCR), which provided data on tumor characteristics, treatment data, treatment outcomes and survival. We focused on three potential pretreatment process quality measures, which included documentation within 6 months prior to initial treatment of prostate-specific antigen (PSA), digital rectal exam (DRE) performance, and Gleason score. Each quality metric was defined using target terms and concepts to extract from the EHRs. Terms were mapped to a standardized medical vocabulary or ontology, enabling us to represent the metric elements by a concept domain and its permissible values. The structured representation of the quality metric included rules that accounted for the temporal order of the metric components. Our algorithms used natural language processing for free text annotation and negation, to ensure terms such as ‘DRE deferred’ are appropriately categorized. Results: We identified 2,123 patients receiving prostate cancer treatment between 2008-2016, of whom 1413 (67%) were matched in the CCR. We compared accuracy of our data mining algorithm, a random sample of manual chart review, and the CCR. (See Table.) Conclusions: EHR systems can be used to assess and report quality metrics systematically, efficiently, and with high accuracy. The development of such systems can improve and reduce the burden of quality reporting and potentially reduce costs of measuring quality metrics through automation. [Table: see text]