How We Can Pay for Health Care Reform

Describes savings and revenue sources and policies to reduce healthcare spending that could finance comprehensive reform with a public option, such as reducing physician and hospital payments, investing in prevention programs, and capping tax exclusions

A Rapid and Computationally Inexpensive Method to Virtually Implant Current and Next-Generation Stents into Subject-Specific Computational Fluid Dynamics Models

Computational modeling is often used to quantify hemodynamic alterations induced by stenting, but frequently uses simplified device or vascular representations. Based on a series of Boolean operations, we developed an efficient and robust method for assessing the influence of current and next-generation stents on local hemodynamics and vascular biomechanics quantified by computational fluid dynamics. Stent designs were parameterized to allow easy control over design features including the number, width and circumferential or longitudinal spacing of struts, as well as the implantation diameter and overall length. The approach allowed stents to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm constructed from medical imaging data. In the coronary bifurcation, we analyzed the hemodynamic difference between closed-cell and open-cell stent geometries. We investigated the impact of decreased strut size in stents with a constant porosity for increasing flow stasis within the stented basilar aneurysm model. These examples demonstrate the current method can be used to investigate differences in stent performance in complex vascular beds for a variety of stenting procedures and clinical scenarios

Simultaneous differential scanning calorimetry – synchrotron X-ray powder diffraction : a powerful technique for physical form characterisation in pharmaceutical materials

© 2016 American Chemical Society. We report a powerful new technique: hyphenating synchrotron X-ray powder diffraction (XRD) with differential scanning calorimetry (DSC). This is achieved with a simple modification to a standard laboratory DSC instrument, in contrast to previous reports which have involved extensive and complex modifications to a DSC to mount it in the synchrotron beam. The high-energy X-rays of the synchrotron permit the recording of powder diffraction patterns in as little as 2 s, meaning that thermally induced phase changes can be accurately quantified and additional insight on the nature of phase transitions obtained. Such detailed knowledge cannot be gained from existing laboratory XRD instruments, since much longer collection times are required. We demonstrate the power of our approach with two model systems, glutaric acid and sulfathiazole, both of which show enantiotropic polymorphism. The phase transformations between the low and high temperature polymorphs are revealed to be direct solid-solid processes, and sequential refinement against the diffraction patterns obtained permits phase fractions at each temperature to be calculated and unit cell parameters to be accurately quantified as a function of temperature. The combination of XRD and DSC has further allowed us to identify mixtures of phases which appeared phase-pure by DSC

Organizational Structure and Pricing: Evidence from a Large U.S. Airline

Although typically modeled as a centralized firm decision, pricing often involves multiple organizational teams that have decision rights over specific pricing inputs. We study team input decisions using comprehensive data from a large U.S. airline. We document that pricing at a sophisticated firm is subject to miscoordination across teams, uses persistently biased forecasts, and does not account for cross-price elasticities. With structural demand estimates derived from sales and search data, we find that addressing one team’s biases in isolation has little impact on market outcomes. We show that teams do not optimally account for biases introduced by other teams. We estimate that corrected and coordinated inputs would lead to a significant reallocation of capacity. Leisure consumers would benefit from lower fares, and business customers would face significantly higher fares. Dead-weight loss would increase in the markets studied. Finally, we discuss likely mechanisms for the observed pricing biases

Incorporating Sales and Arrivals Information in Demand Estimation

We propose a demand estimation method that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving con-sumers solves a standard discrete choice problem. We present a Bayesian IV estima-tion approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand

Organizational Structure and Pricing: Evidence from a Large U.S. Airline

We study how organizational boundaries affect pricing decisions using comprehensive data from a large U.S. airline. We document that the firm’s advanced pricing algorithm, utilizing inputs from different organizational teams, is subject to multiple biases. To quantify the impacts of these biases, we estimate a structural demand model using sales and search data. We recover the demand curves the firm believes it faces using forecasting data. In counterfactuals, we show that correcting biases introduced by organizational teams individually have little impact on market outcomes, but coordinating organizational outcomes leads to higher prices/revenues and increased deadweight loss in the markets studied

Organizational Structure and Pricing: Evidence from a Large U.S. Airline

Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct sub-units. Using comprehen-sive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision-maker. We show that observed prices, however, can be rationalized by account-ing for organizational structure and the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational unitary decision-maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision-makers

Incorporating Search and Sales Information in Demand Estimation

We propose an approach to modeling and estimating discrete choice demand that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers then solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data and measures of consumer search intensity. After presenting simulation studies, we consider an empirical application of air travel demand where product-level sales are sparse. We find considerable variation in demand over time. Periods of peak demand feature both larger market sizes and consumers with higher willingness to pay. This amplifies cyclicality. However, observed frequent price and capacity adjustments offset some of this compounding effect

Demand Estimation with Infrequent Purchases and Small Market Sizes

We propose a demand estimation method that allows for a large number of zero sale observations, rich unobserved heterogeneity, and endogenous prices. We do so by modeling small market sizes through Poisson arrivals. Each of these arriving consumers solves a standard discrete choice problem. We present a Bayesian IV estimation approach that addresses sampling error in product shares and scales well to rich data environments. The data requirements are traditional market-level data as well as a measure of market sizes or consumer arrivals. After presenting simulation studies, we demonstrate the method in an empirical application of air travel demand