Browsing by Subject "Hospitals"

Now showing 1 - 5 of 5

Essays in health economics
(2022-05) Stripling, Sam; Geruso, Michael; Cabral, Marika; Oettinger, Gerald
The three chapters of this dissertation explore the effects of two of the largest policy levers in the United States, Medicare and Medicaid, on health and hospitals. My first chapter examines the effects of Medicare on short-run mortality. Despite being at the forefront of policy debates, credibly estimating whether health insurance reduces mortality remains empirically elusive. The key challenge is creating research designs that have the statistical power to reliably detect the effects of health insurance on mortality. This chapter presents new, population-level estimates of the impact of Medicare on short-run mortality. I use restricted-access Census data to link complete, administrative death records to individual survey responses for nearly 30% of the US population. To understand the effects of Medicare on mortality, I use a regression discontinuity design, comparing the mortality of individuals just above and below the age-65 eligibility threshold. I also consider whether the impact of Medicare on mortality differs by demographics, previous health insurance status, and income-level. I find no statistically significant effects of Medicare on mortality for the full population, previously uninsured, or low-income individuals. The second chapter of my dissertation looks at the effects of the Affordable Care Act Medicaid Expansion on mortality. Given that Medicaid and Medicare are two of the largest policy levers for improving health in the United States, it is important to compare the mortality effects of Medicare to those of Medicaid. This chapter examines whether Medicaid eligibility reduces mortality for near-elderly individuals. I begin by using CDC data and a differences-in-differences design to analyze whether the ACA Medicaid Expansion reduced the mortality rate for individuals aged 55-64. I note several potentially important limitations in using CDC data for studying the effects of Medicaid on mortality. I discuss the merits of circumventing these limitations by using the restricted Census infrastructure to link survey data to administrative death records. I conclude with a cross-study comparison of the effects of Medicare and Medicaid on mortality, and discuss the policy relevance of my findings. The third chapter of my dissertation studies how hospitals respond to the Affordable Care Act Medicaid Expansions. While the first two chapters of the dissertation focus on the benefits of public health insurance for insurance recipients, public health insurance can also significantly benefit health care providers. Hospitals frequently provide health care to uninsured patients without receiving compensation. ACA Medicaid expansions reduced hospitals’ uncompensated care burdens by providing the uninsured with a means of payment in the form of insurance. Anecdotal evidence from hospital administrators suggests hospitals in expansion states respond to their improved financial positions by increasing capacity, purchasing equipment, and hiring more workers. I investigate such claims using hospital financial report data from CMS. Using a differences-in-differences regression framework, I find no evidence that hospitals in expansion states increased bed capacity, capital expenditures, or FTEs relative to hospitals in non-expansion states.
Health care and corporate finance
(2015-12) Towner, Mitch Scott; Starks, Laura T.; Cohn, Jonathan B.; Fracassi, Cesare; Alti, Aydogan; McInnis, John
This dissertation examines issues in U.S. healthcare and capital structure. In the first chapter I give a brief summary of the institutional details of the U.S. healthcare sector with a special emphasis on healthcare finance. In addition to its large size, U.S. healthcare has four unique features that can be used to help answer corporate finance questions: segmented markets, variation in corporate type, extensive data requirements and recent consolidation. I explain how changes over the last 100 years have led to each of these features. Next, I delve deeper into bargaining between insurance companies and hospitals, Medicare pricing, and hospital capital structure decisions during my sample period, 2008-2012. Finally, I conclude with a brief discussion on how the Affordable Care Act has contributed to these factors. In the second chapter I use the health care industry as a novel laboratory in which to study a firm's strategic use of debt to enhance their bargaining power during negotiations with non-financial stakeholders. I show that reimbursement rates negotiated between a hospital and insurers for a specific procedure are higher when the hospital has more debt. I also show that this effect is stronger when hospitals have less bargaining power relative to insurers ex ante, and that hospitals take on more debt when they have less bargaining power using six proxies including differences in state Medicare laws to further strengthen identification. This is the first paper to provide direct evidence that debt improves a firm's bargaining outcomes.
Hospitals as the Intersection of Care, Business and Policy in the U.S. Healthcare System with Reference to the Individual Insurance Mandate
(2021-05) Garza, Angelica
This study evaluates the U.S. healthcare system through the interactions of care, business and policy as modeled by the ability of hospitals to remain financially viable in lieu of healthcare reform under the Patient Protection and Affordable Care Act and the proceeding non-enforcement of the individual mandate of insurance. To do so, I develop historical and political context in respect to each of the three functions of U.S. healthcare (care, business and policy) for hospitals and their interactions with the individual insurance mandate. In order to evaluate this interaction, I use a difference-in-difference empirical model to measure the changes in hospital financial stability after non-enforcement of the individual insurance mandate in Massachusetts and Connecticut. In combining these various approaches and analysis to evaluate the functions of the U.S. healthcare system, the ineffective aspects of the system will be identified, thus providing insight for improving U.S. health outcomes and optimizing health spending.
An Integrated Methodology for Estimating Demand for Essential Services with an Application to Hospital Care
(Council for Advanced Transportation Studies, 1975-04) Briggs, Ronald; Enders, Wayne T.; Fitzsimmons, James; Jensen, Paul;
A methodology to estimate the demand for essential services by enumeration district is developed. The framework of the methodology considers total demand for essential services and associated transportation to be the sum of latent and satisfied demand. The origins of latent demand are indicated by examining the barriers which must be overcome by an individual to satisfy an existing need. The method for estimating satisfied , demand begins with census data by enumeration district. Actual usage rates of a service, cross-classified by factors influencing actual usage such as age, sex, race, and income are obtained from national surveys and then applied to the local census data to obtain an estimate of satisfied demand for the service by enumeration district. The methodology was used to estimate the satisfied demand for hospital care and was found to be accurate to within 7/10 of one percent for the study region. Total demand for a service may be estimated in a similar fashion by substituting barrier-free usage rates in the above methodology. Latent demand by enumeration district then became the difference between total and satisfied demand. The total transportation associated with a service system is obtained by assigning the demand by enumeration district to the closest facility up to its capacity with spillovers to the next closest facility. Total travel is then calculated using these origin-destination links and the frequency of trips.
Solving midterm and short-term nurse scheduling problems
(2005-05) Purnomo, Hadi Waskito; Bard, Jonathan F.
As in many service organizations, hospitals use a variety of shift types when scheduling nurse resources. In general, the operational decisions of workforce planning can be divided into two interrelated problems: (1) midterm planning in terms of shift assignments for up to six weeks at a time, and (2) the short-term daily adjustment of schedules. Individual nurse profiles are a function of a unit's skill requirements, labor laws, and other qualifications, and are results of the long-term planning decision. At the midterm level, the goal is to match nurse resources with the expected workload over the planning horizon. Rosters are designed to maximize personnel preferences as well as minimize cost. To investigate this problem, a large-scale integer program model was developed and solved with two methodologies. The first is based on Lagrangian Relaxation based heuristic, which uses a combination of subgradient optimization and Bundle methods, with variable fixing strategy and IP-based heuristic. The second methodology is a branch-and-price algorithm that makes use of several new branching rules, an extremely effective rounding heuristic, a dual bound procedure, and specialized aggregation scheme. To extend the algorithms to solve different levels of nursing skills, a downgrading strategy is used by giving scheduling priorities to higher level of worker. The midterm schedules provide a blueprint for the monthly work assignments of the staff. Because of absenteeism and unpredicted demand fluctuations, though, a hospital-wide reallocation of resources is needed on a daily basis. While the overall goal is to ensure adequate coverage at minimize cost, a secondary goal is to minimize changes to the assigned rosters. Nevertheless, to allow more flexibility, nurses are permitted to work in several units during a shift rather than just their home unit. An IP-based column generation methodology was developed to solve this problem and applied within a rolling horizon framework. The idea is to consider 24 hours at a time, but implement the results for only the first 8 hours. All algorithms were tested on data obtained from a 400-bed US hospital. The results show an order-of-magnitude improvement over current approaches in terms of solution quality and computation times.