Hospital Ownership Mix Efficiency in the US: An Exploratory Study

This paper offers an empirical test of ownership mix efficiency in the U.S. hospital services industry. The test compares the benefits of quality assurance with the costs from the attenuation of property rights that result from an increased presence of nonprofit organizations. The empirical results suggest that too many not-for-profit and public hospitals may exist in the typical market area of the U.S. The policy implication is that more quality of care per dollar might be obtained by attracting a greater percentage of for-profit hospitals into some market areas. This conclusion, however, is tempered with several caveats. We discuss these and also make recommendations for further research.

Testing for Ownership Mix Efficiency: The Case of the Nursing Home Industry

This paper offers an empirical test of ownership mix efficiency in the U.S. nursing home industry. We test to compare the benefits of quality assurance with the costs from the attenuation of property rights that result from an increased presence of nonprofit organizations. The empirical results suggest that too few nonprofit nursing homes may exist in the typical market area of the U.S. The policy implication is that more quality of care per dollar might be obtained by attracting a greater percentage of nonprofit nursing homes into most market areas.

Explaining Pharmaceutical R&D Growth Rates at the Industry Level: New Perspectives and Insights

This paper uses aggregate data for the major pharmaceutical companies in the U.S. to study the rate of growth in pharmaceutical R&D intensity over the period from 1952 to 2001. The theoretical model argues and the empirical findings suggest that pharmaceutical R&D spending increases with real drug prices, after holding constant other determinants of R&D. Simulations based on our multiple regression model indicate that the capitalized value of pharmaceutical R&D spending would have been about 30 percent lower if the federal government had limited the rate of growth in drug price increases to the rate of growth in the general consumer price index during the period 1980 to 2001. Moreover, a drug price control regime would have resulted in 330 to 365 fewer new drugs brought to the global market during that same time period.Health and Safety

A Cost-Benefit Analysis of Drug Price Controls in the U.S.

This paper uses national data for the period 1960 to 2000 to estimate an aggregate private consumer demand for pharmaceuticals in the U.S. The estimated demand curve is then used to simulate the value of consumer surplus gains from a drug price control regime that holds drug price increases to the same rate of growth as the general consumer price level over the time period from 1981 to 2000. Based upon a 7 percent real interest rate, we find that the future value of consumer surplus gains from this hypothetical policy would have been $319 billion at the end of 2000. According to an earlier study, that same drug price control regime would have led to 198 fewer new drugs being brought to the U.S. market over this period. Therefore, we approximate that the average social opportunity cost per drug developed during this period to be approximately$1.6 billion. Recent research on the value of pharmaceuticals suggests that the social benefits of a new drug may be far greater than this estimated social opportunity cost. Based on empirical findings of the productivity of pharmaceutical R&D over a similar time period, our first approximation of the cost-benefit ratio for this hypothetical price control policy ranges between 62 and 68. This suggests price controls on pharmaceutical prices between 1980 and 2000 would have caused much more harm than good. Society may be better off discovering more efficient ways than price controls to improve access to existing drugs.Health and Safety, Regulatory Reform

Automated Pleural Effusion Detection on Chest X-Rays

In this paper we present a lightweight solution to help iden- tify a pathological condition called pleural effusion using chest x-rays (CXR). Patients with pleural effusion have been found to have increased mortality rates, and if left undiagnosed effusion has been found to con- tribute to congestive heart failure, malignancy, pulmonary embolism, and tuberculosis [15] [13]. Using convolutional neural network architectures we developed a model to assist in the successful diagnosis of pleural ef- fusion. The effectiveness of our model was evaluated against 200 studies manually labeled by consensus from 3 board certified radiologist. We demonstrate that our model is able to reproduce current baseline perfor- mance for this task with a model that is 10x smaller and 30x faster. This lighter architecture allows for more flexibility in deployment including the ability to deploy directly on an edge node. We present this model as a tool for the radiologists to diagnose the presence of pleural effusion from a diagnostic imaging study

sEMG Gesture Recognition With a Simple Model of Attention

This paper presents a novel method for fast classification of surface electromyography(sEMG) signals, using a simple model of attention. The brain transmits electrical signals throughout the body to contract and relax muscles. sEMG measures these signals by recording muscle activity from the surface above the muscle on the skin. By classifying these signals with low latency, they can be used to control a prosthetic limb using an amputee\u27s brain power. On a difficult, industry benchmark sEMG dataset, the proposed attentional architecture yields excellent results, classifying 36 more gestures (53 in total) with about 20% higher accuracy (87% overall) than the current standards in the field. These results have direct and immediate application in the fields of robotics, myoelectric control, and prosthetics

Professor Text: University Fundraising Optimization

University fundraising campaigns are a unique type of cause-related marketing with its own challenges and opportunities. Campaigns like this typically last an extended period, such as five or more years, and goals exist beyond the dollar amount raised. These supplemental goals, such as awareness among potential future donators or brand reputation within the local community, are important to consider and strategize. There can also be unique limitations, such as requiring advertising specifically on recent large gifts or endowment programs. This research explores how machine learning techniques such as natural language processing can be used to optimize a fundraising campaign strategy, execution, and overall performance

A Machine Learning Method of Determining Causal Inference applied to Shifts in Voting Preferences between 2012-2016

This research investigates the application of machine learning techniques to assist in the execution of a synthetic control model. This model was performed to analyze counties within the United States that showed a voter shift from a majority of Democratic voter share to Republican between the 2012 and 2016 election cycles. The following study applies two steps of machine learning analysis. The first, which is the treatment discovery process, leverages a Random Forest to evaluate feature importance. The second step was the execution of the synthetic control model with two predictor variable lists. The first was the parametric method: a hand curated predictor variable list based on domain knowledge. The second was the non-parametric method: all available predictor (descriptive) variables were used. The Random Forest treatment discovery process resulted in two uncommon variables applied as treatment effects: WIC women enrollment and a decrease of vegetable farm acreage. The opportunity to research these atypical treatment variables allows for the potential of surfacing counterfactual arguments for further research. The use of the parametric and non-parametric methods offers a system of comparison for the research in this paper. The result from the decrease in vegetable farm acreage treatment variable was negative for the non-parametric model. However, the parametric model did show strong statistical evidence towards a treatment effect from the decrease in farm acreage. It is likely that the decrease in vegetable farm acreage is a proxy for poverty or a population density metric. These data results suggest that this model was likely suffering from omitted variable bias for representation of one or both of these metrics in the predictor variable list. The WIC women enrollment treatment variable investigation resulted in the synthetic control model having difficulty in forming a synthetic control comparison. These results suggest there is a fundamental difference between those counties used to create the synthetic control and the other counties that saw a treatment effect. Additional research needs to be performed, and it could result in a different application of the data for use in a synthetic control model. The results of this study, while not surfacing causal inference, did open questions for further research. Given the opportunity these joined causal inference and machines learning practices could continue and potential offer assistance to traditional causal modeling methods. Allowing researchers to understand data and relationships between the data more intimately, theoretically allowing for new causal inferences to be discovered