A non-linear discrete stochastic optimization model for Advanced Access patient appointment scheduling

Access to healthcare is a major problem in which patients are deprived of receiving timely admission to healthcare. Poor access has resulted in significant but avoidable healthcare cost, poor quality of healthcare, and deterioration in the general public health. Advanced Access is a simple and direct approach to appointment scheduling in which the majority of a clinic's appointments slots are kept open in order to provide access for immediate or same day healthcare needs and therefore, alleviate the problem of poor access the healthcare. This research formulates a non-linear discrete stochastic mathematical model of the Advanced Access appointment scheduling policy. The model objective is to maximize the expected profit of the clinic subject to constraints on minimum access to healthcare provided. Patient behavior is characterized with probabilities for no-show, balking, and related patient choices. Structural properties of the model are analyzed to determine whether Advanced Access patient scheduling is feasible. To solve the complex combinatorial optimization problem, a heuristic that combines greedy construction algorithm and neighborhood improvement search was developed. The model and the heuristic were used to evaluate the Advanced Access patient appointment policy compared to existing policies. Trade-off between profit and access to healthcare are established, and parameter analysis of input parameters was performed. The trade-off curve is a characteristic curve and was observed to be concave. This implies that there exists an access level at which at which the clinic can be operated at optimal profit that can be realized. The results also show that, in many scenarios by switching from existing scheduling policy to Advanced Access policy clinics can improve access without any decrease in profit. Further, the success of Advanced Access policy in providing improved access and/or profit depends on the expected value of demand, variation in demand, and the ratio of demand for same day and advanced appointments. The contributions of the dissertation are a model of Advanced Access patient scheduling, a heuristic to solve the model, and the use of the model to understand the scheduling policy trade-offs which healthcare clinic managers must make. ^

Three essays on hospital efficiency

This dissertation analyzes hospital efficiency using various econometric techniques. The first essay provides additional and recent evidence to the presence of contract management behavior in the U.S. hospital industry. Unlike previous studies, which focus on either an input-demand equation or the cost function of the firm, this paper estimates the two jointly using a system of nonlinear equations. Moreover, it addresses the longitudinal problem of institutions adopting contract management in different years, by creating a matched control group of non-adopters with the same longitudinal distribution as the group under study. The estimation procedure then finds that labor, and not capital, is the preferred input in U.S. hospitals regardless of managerial contract status. With institutions that adopt contract management benefiting from lower labor inefficiencies than the simulated non-contract adopters. These results suggest that while there is a propensity for expense preference behavior towards the labor input, contract managed firms are able to introduce efficiencies over conventional, owner controlled, firms. Using data for the years 1998 through 2007, the second essay investigates the production technology and cost efficiency faced by Florida hospitals. A stochastic frontier multiproduct cost function is estimated in order to test for economies of scale, economies of scope, and relative cost efficiencies. The results suggest that small-sized hospitals experience economies of scale, while large and medium sized institutions do not. The empirical findings show that Florida hospitals enjoy significant scope economies, regardless of size. Lastly, the evidence suggests that there is a link between hospital size and relative cost efficiency. The results of the study imply that state policy makers should be focused on increasing hospital scale for smaller institutions while facilitating the expansion of multiproduct production for larger hospitals. The third and final essay employs a two staged approach in analyzing the efficiency of hospitals in the state of Florida. In the first stage, the Banker, Charnes, and Cooper model of Data Envelopment Analysis is employed in order to derive overall technical efficiency scores for each non-specialty hospital in the state. Additionally, input slacks are calculated and reported in order to identify the factors of production that each hospital may be over utilizing. In the second stage, we employ a Tobit regression model in order to analyze the effects a number of structural, managerial, and environmental factors may have on a hospital’s efficiency. The results indicated that most non-specialty hospitals in the state are operating away from the efficient production frontier. The results also indicate that the structural make up, managerial choices, and level of competition Florida hospitals face have an impact on their overall technical efficiency.