A Model to Create an Efficient and Equitable Admission Policy for Patients Arriving to the Cardiothoracic ICU

To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons. Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs

Assessing Effect of Global Inventory Planning with Enterprise-wide Information for a Large Manufacturer

This paper studies how a global manufacturer with many subsidiaries can achieve enhanced business value for the organization by sharing information within its supply chain network. Specifically, the uncertainties in the demands from the downstream distribution center affect the inventory levels at the upstream distribution center under different inventory policies, considering the uncertain lead times and the given order fill rates. With a generic simulation model and real data, we evaluate the magnitude of savings in inventory under the new inventory policy where information can be shared among subsidiaries, compared to the status quo where subsidiaries run independently with no information sharing. The results show that average inventory level at the upstream DC under the new policy would be reduced by approximately 3%. Considering the given manufacturer\u27s global supply chain distribution network holds about $4 billion in average inventory, the 3% improvement is a very significant savings

Efficiency and Equity tradeoffs in Voting Machine Allocation Problems

Efficiency and equity are the two crucial factors to be considered when allocating public resources such as voting machines. Existing allocation models are all single-objective, focusing on maximizing either efficiency or equity despite the fact that the actual decision-making process involves both issues simultaneously. We propose a bi-objective integer program to analyze the tradeoff between the two competing objectives. The new model quantifies the sacrifice in efficiency in order to achieve a certain improvement in equity and vice versa. Using data from the 2008 United States Presidential election in Franklin County, Ohio, we demonstrate that our model is capable of producing significantly more balanced allocation plans, in terms of efficiency and equity, than current practice or other competing methods

Analyzing Patient Choices for Routine Procedures in U.S. Versus Overseas Before and after Affordable Care Act

Low prices and good medical care have caused an increasing number of Americans to travel abroad for affordable health care (medical tourism). This study analyzes the viability of medical tourism as an alternative to United States (U.S.) hospitals for routine surgical procedures by considering the total charges paid by U.S. insurance companies and by patients (out of pocket) to hospitals. A mathematical model based on rational choice theory is developed to approximate the most-favorable decisions for both patients and payers, and this is illustrated through an example involving the decision to undergo coronary artery bypass graft (CABG) surgery in India or the U.S. Before the Affordable Care Act (ACA), medical tourism positively offset the total treatment charges associated with CABG procedures, some fully insured and underinsured patients would often opt for overseas treatment. After the enactment of the ACA, more fully insured patients and more people from a much smaller set of underinsured patients may opt for overseas CABG treatment. This paper finds that the ACA pushes individual decisions closer to the system optimal situation, and that if the U.S. health-care industry is unable to eliminate waste and inefficiency and thus curb rising costs, it will continue to lose surgical revenue to foreign health providers

Improving Voting Systems Through Service Operations Management

We apply service-operations-management concepts to improve the efficiency and equity of voting systems. Recent elections in the United States and elsewhere have been plagued by long lines, excessive waiting times, and perceptions of unfairness. We build models for the waiting lines at voting precincts using both traditional steady-state queueing methods and simulation models. We develop solution methods to allocate voting machines optimally to precincts. Our objective functions consider both the efficiency and the equity of the voting system. We compare our allocation algorithm to several competing methods, including those used in practice. We examine several different strategies for improving voting operations on both the demand and the capacity side of voting systems, and we present a complete case study of applying our method to data from the 2008 election for Franklin County, Ohio. We conclude that our method is superior to existing polices in terms of efficiency and equity and that it is robust in terms of uncertainties regarding turnout rates on Election Day. We also suggest several operational improvements to the voting process drawn from the service-operations literature

A Robust Voting Machine Allocation Model to Reduce Extreme Waiting

Despite the fact that in the 2012 presidential election, two-thirds of voters waited less than 10 min and a mere 3% waited longer than an hour to cast their ballots, media accounts of excruciating waits have left a misleading impression on the general public. At the root of the problem is the allocation of voting machines based on efficiency as measured by average waiting time. This method does not account for the damaging consequences of the rare events that cause extremely long waits. We propose an extreme-value robust optimization model that can explicitly consider nominal and worst-case waiting times beyond the single-point estimate commonly seen in the literature. We benchmark the robust model against the published deterministic model using a real case from the 2008 presidential election in Franklin County, Ohio. The results demonstrate that the proposed robust model is superior in accounting for uncertainties in voter turnout and machine availability, reducing the number of voters experiencing waits that exceed two hours by 61%

The ICU Will See You Now: Efficient-equitable Admission Control Policies for a Surgical ICU with Batch Arrivals

Intensive Care Units (ICUs) are frequently the bottleneck in a hospital system, limiting patient flow and negatively impacting profits. This article examines admission control policies for a surgical ICU where patients arrive in batches. This problem is formulated as a Markov Decision Process (MDP) with an objective function that allows for varying degrees of emphasis on efficiency versus equity. Equity concerns are driven by a combination of surgery type and operating surgeon and are captured in a robust manner in the proposed models. A simple and efficient heuristic solution method related to our MDP formulation is proposed that provides a performance guarantee. The proposed admissions policy is applied to a real setting motivated by the cardiothoracic surgical ICU at Mount Sinai Medical Center in New York; the results demonstrate that the ICU can achieve large equity gains with no efficiency losses

A Robust Voting Machine Allocation Model to Reduce Extreme Waiting

Despite the fact that in the 2012 presidential election, two-thirds of voters waited less than 10 minutes and a mere 3% waited longer than an hour to cast their ballots, media accounts of excruciating waits have left a misleading impression on the general public. At the root of the problem is the allocation of voting machines based on efficiency as measured by average waiting time. This method does not account for the damaging consequences of the rare events that cause extremely long waits. We propose an extreme-value robust optimization model that can explicitly consider nominal and worst-case waiting times beyond the single-point estimate commonly seen in the literature. We benchmark the robust model against the published deterministic model using a real case from the 2008 presidential election in Franklin County, Ohio. The results demonstrate that the proposed robust model is superior in accounting for uncertainties in voter turnout and machine availability, reducing the number of voters experiencing waits that exceed two hours by 61%