Managing Operational Efficiency And Health Outcomes At Outpatient Clinics Through Effective Scheduling

A variety of studies have documented the substantial deficiencies in the quality of health care delivered across the United States. Attempts to reform the United States health care system in the 1980s and 1990s were inspired by the system\u27s inability to adequately provide access, ensure quality, and restrain costs, but these efforts had limited success. In the era of managed care, access, quality, and costs are still challenges, and medical professionals are increasingly dissatisfied. In recent years, appointment scheduling in outpatient clinics has attracted much attention in health care delivery systems. Increase in demand for health care services as well as health care costs are the most important reasons and motivations for health care decision makers to improve health care systems. The goals of health care systems include patient satisfaction as well as system utilization. Historically, less attention was given to patient satisfaction compared to system utilization and conveniences of care providers. Recently, health care systems have started setting goals regarding patient satisfaction and improving the performance of the health system by providing timely and appropriate health care delivery. In this study we discuss methods for improving patient flow through outpatient clinics considering effective appointment scheduling policies by applying two-stage Stochastic Mixed-Integer Linear Program Model (two-stage SMILP) approaches. Goal is to improve the following patient flow metrics: direct wait time (clinic wait time) and indirect wait time considering patient’s no-show behavior, stochastic server, follow-up surgery appointments, and overbooking. The research seeks to develop two models: 1) a method to optimize the (weekly) scheduling pattern for individual providers that would be updated at regular intervals (e.g., quarterly or annually) based on the type and mix of services rendered and 2) a method for dynamically scheduling patients using the weekly scheduling pattern. Scheduling templates will entertain the possibility of arranging multiple appointments at once. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. First, we use risk-neutral two-stage stochastic programming model where the objective function considers the expected value as a performance criterion in the selection of random variables like total waiting times and next, we expand the model formulation to mean-risk two-stage stochastic programming in which we investigate the effect of considering a risk measure in the model. We apply Conditional-Value-at-Risk (CVaR) as a risk measure for the two-stage stochastic programming model. Results from testing our models using data inspired by real-world OBGYN clinics suggest that the proposed formulations can improve patient satisfaction through reduced direct and indirect waiting times without compromising provider utilization

Containing Risk when Maximizing Supply-Chain Performance

The objective of this dissertation is to develop and test an approach that will quantify the level of risk in the supply chain, evaluate the cost and impact of risk mitigation strategies, validate event management protocols pre-implementation, and optimize across a portfolio of risk mitigation strategies. The research integrates a Mixed Integer Linear Programming (MILP) model and a Discrete Event Simulation model to investigate a production-inventory-transportation problem subject to risk. The MILP model calculates the optimal Net Profit Contribution of the supply chain in the absence of risk. Deviation risks are introduced as volatility in final demand and lead times, with lead time volatility affecting raw material lead times from suppliers to manufacturing plants and finished goods lead times from manufacturing plants to the warehouses. Disruption risks are modelled as temporarily impeding production at the manufacturing plants, in-bound distribution of raw materials from suppliers to the manufacturing plants, and out-bound distribution of finished goods from the manufacturing plants to warehouses. Computational experiments are run to examine the impact of risk on the supply chain. Further experiments explore the consequences of three risk mitigation strategies (inventory placement, expediting, and production flexibility) on supply chain performance in the presence of risk with the aim of discovering whether one strategy dominates or whether a portfolio approach to risk mitigation performs best. In sum, this research seeks to develop a framework that can inform efforts in understanding, planning for and controlling risk in the supply chain

Synergies, cooperation and syndication in venture capital game, portfolio optimization with genetic algorithms and asset auctions: essays in finance

This thesis looks at all scientific phenomenon of financial decision-making from both the empirical and theoretical side, with empirical trying to strengthen theoretical assumptions or even to expand it. In chapter 2, we propose a two-stage financing model with three players that consider the output elasticities of all parties using the Cobb-Douglas utility function. Theoretical findings in chapter 2 suggest that a higher complementary coefficient between players on both stages can lead to a higher level of effort from all three players, taking game dynamics away from the moral hazard problem and causing higher exit stage payoffs. Previous track record of the angel and VC and output elasticity of the entrepreneur, combined with the company’s shares offered the angel and VC, impact the three-player game dynamic, causing some players to reduce their efforts after specific funding rounds. Our empirical results show that VC syndication increases the average amount of funding offered to entrepreneurs as well as that syndicated ventures have a higher number of funding rounds, resulting in a higher number of possible entry-points provided by those start-ups. Our results in chapter 4 suggested that a two-point GA that minimized the risk for a given level of expected return slightly outperformed the results of the SPEA2. Compared with the previous industry standard for risk measure—Value-at-Risk, we show that both frontiers differed, especially at the low return side. The converted Value-at-Risk solutions were not evenly distributed along the efficient frontier and even inadequate for some ES values. In chapter 5, we use the game theory approach to examine the first-price package auction design for illiquid asset auctions. Our theoretical work suggests that every case that can be presented as a two or three asset game, as well as longer games that can be presented as two and three asset subgames, has a strong equilibrium if the bidders’ budgets and utilities for every asset are common knowledge