Do hospitals react to random demand pressure by early discharges?

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Hospital costs and random demand

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Do hospitals react to random demand pressure by early discharges?

This project tries to assess whether hospitals react to random demand pressure by discharging patients earlier than expected. As a matter of fact, combining an unpredictable demand for medical services with limited and, to some extent, fixed medical resources, generates strong incentives to discharge patients earlier than expected when demand is high − increasing the risk of readmission and decreasing the benefit from treatment. This work was conducted as a way to determine whether those incentives actually affect discharging decisions. Analysis of Portuguese hospitals data shows that hospital utilization levels at the time of admission, prior to the admission and post admission do have a negative impact over the length of stay in hospital, although this impact is quantitatively irrelevant. More than that, larger utilization levels have a positive impact over the probability of being discharged at certain days of the week, indicating that an early discharges problem may exist.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: The Finite Horizon Case

We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy.

Coordinating Inventory Control and Pricing Strategies with Random Demand and Fixed Ordering Cost: the Infinite Horizon Case

We analyze an infinite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are identically distributed random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to maximize expected discounted, or expected average profit over the infinite planning horizon. We show that a stationary (s,S,p) policy is optimal for both the discounted and average profit models with general demand functions. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period.

A Theory of Random Consumer Demand

This paper presents a new theory of random consumer demand. The primitive is a collection of probability distributions, rather than a binary preference. Various assumptions constrain these distributions, including analogues of common assumptions about preferences such as transitivity, monotonicity and convexity. Two results establish a complete representation of theoretically consistent random demand. The purpose of this theory of random consumer demand is application to empirical consumer demand problems. To this end, the theory has several desirable properties. It is intrinsically stochastic, so the econometrician can apply it directly without adding extrinsic randomness in the form of residuals. Random demand is parsimoniously represented by a single function on the consumption set. Finally, we have a practical method for statistical inference based on the theory, described in McCausland (2004), a companion paper.

Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a \"regular\" \"L-utility\" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and posterior uncertainty in terms of distributions over the indefinite-dimensional parameter set of a flexible functional form. We propose a class of proper priors on the parameter set. The priors are flexible, in the sense that they put positive probability in the neighborhood of any L-utility function that is regular on a large subset bar(X) of X; and regular, in the sense that they assign zero probability to the set of L-utility functions that are irregular on bar(X). We propose methods of Bayesian inference for an environment with indivisible goods, leaving the more difficult case of indefinitely divisible goods for another paper. We analyse individual choice data from a consumer experiment described in Harbaugh et al. (2001).

Stratégie de gestion de stocks dans un réseau collaboratif

Le contenu de ce mémoire traite du problème de gestion des stocks dans un réseau constitué de plusieurs sites de stockage de produits. Chaque site i gère son stock de manière autonome pour satisfaire une demande déterministe sur un horizon donné. Un stock maximum Si est tenu à chaque site i. Lorsque le point de commande si est atteint, une commande de taille Qi est placée au centre de distribution qui alimente tous les sites. Qi est telle que Qi = Si - si. La quantité Qi est livrée dans un délai connu Li. Si, à un instant donné, la demande Di au site i excède la quantité en main, le site i fait appel à un ou à plusieurs autres sites du réseau pour le transfert d’une quantité Xji (j = 1, 2, …, n). Ce transfert s’effectue selon un certain nombre de règles de jeu qui tiennent compte des coûts de transfert, de stockage, d’approvisionnement et de pénurie. Ce mémoire examine six principales contributions publiées dans la littérature pour évaluer les contributions d’un modèle collaboratif aux performances, en termes de coûts et de niveau de service, de chaque site du réseau. Cette investigation se limite à une configuration du réseau à deux échelons : un entrepôt central et n (n > 2) sites de stockage. Le cas des pièces de rechange, caractérisé par une demande aléatoire, est examiné dans trois chapitres de ce mémoire. Une autre application de ces stratégies à la collaboration entre n centres hospitaliers (n > 2) est également examinée dans ce travail.

Robustness in facility location

Facility location concerns the placement of facilities, for various objectives, by use of mathematical models and solution procedures. Almost all facility location models that can be found in literature are based on minimizing costs or maximizing cover, to cover as much demand as possible. These models are quite efficient for finding an optimal location for a new facility for a particular data set, which is considered to be constant and known in advance. In a real world situation, input data like demand and travelling costs are not fixed, nor known in advance. This uncertainty and uncontrollability can lead to unacceptable losses or even bankruptcy. A way of dealing with these factors is robustness modelling. A robust facility location model aims to locate a facility that stays within predefined limits for all expectable circumstances as good as possible. The deviation robustness concept is used as basis to develop a new competitive deviation robustness model. The competition is modelled with a Huff based model, which calculates the market share of the new facility. Robustness in this model is defined as the ability of a facility location to capture a minimum market share, despite variations in demand. A test case is developed by which algorithms can be tested on their ability to solve robust facility location models. Four stochastic optimization algorithms are considered from which Simulated Annealing turned out to be the most appropriate. The test case is slightly modified for a competitive market situation. With the Simulated Annealing algorithm, the developed competitive deviation model is solved, for three considered norms of deviation. At the end, also a grid search is performed to illustrate the landscape of the objective function of the competitive deviation model. The model appears to be multimodal and seems to be challenging for further research.

A discrete-time queueing system with feedback and optional service

Queueing systems constitute a central tool in modeling and performance analysis. These types of systems are in our everyday life activities, and the theory of queueing systems was developed to provide models for forecasting behaviors of systems subject to random demand. The practical and useful applications of the discrete-time queues make the researchers to con- tinue making an e ort in analyzing this type of models. Thus the present contribution relates to a discrete-time Geo/G/1 queue in which some messages may need a second service time in addition to the rst essential service. In day-to-day life, there are numerous examples of queueing situations in general, for example, in manufacturing processes, telecommunication, home automation, etc, but in this paper a particular application is the use of video surveil- lance with intrusion recognition where all the arriving messages require the main service and only some may require the subsidiary service provided by the server with di erent types of strategies. We carry out a thorough study of the model, deriving analytical results for the stationary distribution. The generating functions of the number of messages in the queue and in the system are obtained. The generating functions of the busy period as well as the sojourn times of a message in the server, the queue and the system are also provided.

Estimation of random coefficients logit demand models: an application to the Brazilian fixed income fund market

Estimation of demand and supply in differentiated products markets is a central issue in Empirical Industrial Organization and has been used to study the effects of taxes, merges, introduction of new goods, market power, among others. Logit and Random Coefficients Logit are examples of demand models used to study these effects. For the supply side it is generally supposed a Nash equilibrium in prices. This work presents a detailed discussion of these models of demand and supply as well as the procedure for estimation. Lastly, is made an application to the Brazilian fixed income fund market.

The rating of perceived exertion predicts intermittent vertical jump demand and performance

The aims of this study were (a) to assess the ability of the rating of perceived exertion (RPE) to predict performance (i.e. number of vertical jumps performed to a fixed jump height) of an intermittent vertical jump exercise, and (b) to determine the ability of RPE to describe the physiological demand of such exercise. Eight healthy men performed intermittent vertical jumps with rest periods of 4, 5, and 6s until fatigue. Heart rate and RPE were recorded every five jumps throughout the sessions. The number of vertical jumps performed was also recorded. Random coefficient growth curve analysis identified relationships between the number of vertical jumps and both RPE and heart rate for which there were similar slopes. In addition, there were no differences between individual slopes and the mean slope for either RPE or heart rate. Moreover, RPE and number of jumps were highly correlated throughout all sessions (r=0.97-0.99; P0.001), as were RPE and heart rate (r=0.93-0.97; P0.001). The findings suggest that RPE can both predict the performance of intermittent vertical jump exercise and describe the physiological demands of such exercise.

Demand for and availability of online support to stop smoking

OBJECTIVES: Estimate the frequency of online searches on the topic of smoking and analyze the quality of online resources available to smokers interested in giving up smoking. METHODS: Search engines were used to revise searches and online resources related to stopping smoking in Brazil in 2010. The number of searches was determined using analytical tools available on Google Ads; the number and type of sites were determined by replicating the search patterns of internet users. The sites were classified according to content (advertising, library of articles and other). The quality of the sites was analyzed using the Smoking Treatment Scale- Content (STS-C) and the Smoking Treatment Scale - Rating (STS-R). RESULTS: A total of 642,446 searches was carried out. Around a third of the 113 sites encountered were of the 'library' type, i.e. they only contained articles, followed by sites containing clinical advertising (18.6) and professional education (10.6). Thirteen of the sites offered advice on quitting directed at smokers. The majority of the sites did not contain evidence-based information, were not interactive and did not have the possibility of communicating with users after the first contact. Other limitations we came across were a lack of financial disclosure as well as no guarantee of privacy concerning information obtained and no distinction made between editorial content and advertisements. CONCLUSIONS: There is a disparity between the high demand for online support in giving up smoking and the scarcity of quality online resources for smokers. It is necessary to develop interactive, customized online resources based on evidence and random clinical testing in order to improve the support available to Brazilian smokers.

Random walk forecast of urban water in Iran under uncertainty

There are two significant reasons for the uncertainties of water demand. On one hand, an evolving technological world is plagued with accelerated change in lifestyles and consumption patterns; and on the other hand, intensifying climate change. Therefore, with an uncertain future, what enables policymakers to define the state of water resources, which are affected by withdrawals and demands? Through a case study based on thirteen years of observation data in the Zayandeh Rud River basin in Isfahan province located in Iran, this paper forecasts a wide range of urban water demand possibilities in order to create a portfolio of plans which could be utilized by different water managers. A comparison and contrast of two existing methods are discussed, demonstrating the Random Walk Methodology, which will be referred to as the â On uncertainty pathâ , because it takes the uncertainties into account and can be recommended to managers. This On Uncertainty Path is composed of both dynamic forecasting method and system simulation. The outcomes show the advantage of such methods particularly for places that climate change will aggravate their water scarcity, such as Iran.

Time patterns in UK demand for alcohol and tobacco: an application of the EM algorithm

Capturing the pattern of structural change is a relevant task in applied demand analysis, as consumer preferences may vary significantly over time. Filtering and smoothing techniques have recently played an increasingly relevant role. A dynamic Almost Ideal Demand System with random walk parameters is estimated in order to detect modifications in consumer habits and preferences, as well as changes in the behavioural response to prices and income. Systemwise estimation, consistent with the underlying constraints from economic theory, is achieved through the EM algorithm. The proposed model is applied to UK aggregate consumption of alcohol and tobacco, using quarterly data from 1963 to 2003. Increased alcohol consumption is explained by a preference shift, addictive behaviour and a lower price elasticity. The dynamic and time-varying specification is consistent with the theoretical requirements imposed at each sample point. (c) 2005 Elsevier B.V. All rights reserved.