The formation of multi-hospital systems represents one of the largest structural changes in the hospital industry. As of 2008, system affiliated hospitals outnumbered stand alone hospitals 2511 to 2167 and the percentage of system affiliated rural hospitals has increased dramatically from 24.8% in 1983 to 42.2% in 2008 (based on AHA data for non-federal acute care general hospitals). The effects of system membership on hospital performance have been of great interest to health care researchers, but the majority of research on multi-hospital systems has either focused exclusively on urban facilities or pooled urban and rural facilities in the same sample, and thus failed to allow for potential differences in membership effects between urban and rural hospitals. The result is that the effect of system membership on rural hospital performance has remained largely unexplored, creating a gap in the body of health services research. The objectives of this study are both theoretical and empirical. Theoretically, this study is intended to be a deliberate empirical application of contingency theory, which is the one major organizational theory that seeks to explain variations in organizational performance as its fundamental purpose. Empirically, this study seeks to explore the relationship between rural hospital system membership and rural hospital performance, taking into account the environment of the rural hospital and the structure of the multi-hospital system to which it belongs. The study sample consists of 1010 non-federal, short-term, acute care general rural hospitals with consistent system membership and critical access hospital (CAH) status from 2004 to 2008. Hospital economic performance is represented by the dependent variables of hospital total margin and a productive efficiency score calculated using Data Envelopment Analysis (DEA). Four contingent pairs containing measures for environmental munificence, system membership, the presence of local system partners, the presence of hierarchical system partners, and CAH status, were used to measure a hospital’s fit between environment and structure. Regression analysis was used to determine the relationship between hospital performance and the fit between a hospital’s environment and its organizational/system structure. Results of the analysis indicate that hospitals with a better fit have significantly higher total margins, but results for productive efficiency were largely insignificant.

Stressed Value-at-Risk (VaR) is a statistic used to measure an entity's exposure to market risk by evaluating possible extreme portfolio losses. Stressed VaR scenarios can be used as a metric to describe the state of the financial market and can be used to detect and counter procyclicality by allowing central clearing counterparities (CCP) to increase margin requirements. This thesis aims to implement and evaluate machine learning methods (e.g., neural networks) for selecting stressed VaR scenarios in price return stock datasets where one liquidity day is assumed. The models are implemented to counter the procyclical effects present in NASDAQ's dual lambda method such that the selection maximises the total margin metric. Three machine learning models are implemented together with a labelling algorithm, a supervised and unsupervised multilayer perceptron and a random forest model. The labelling algorithm employs a deviation metric to differentiate between stressed VaR and standard scenarios. The models are trained and tested using 5000 scenarios of price return values from historical stock datasets. The models are tested using visual results, confusion matrix, Cohen's kappa statistic, the adjusted rand index and the total margin metric. The total margin metric is computed using normalised profit and loss values from artificially generated portfolios. The implemented machine learning models and the labelling algorithm manage to counter the procyclical effects evident in the dual lambda method and selected stressed VaR scenarios such that the selection maximise the total margin metric. The random forest model shows the most promise in classifying stressed VaR scenarios, since it manages to maximise the total margin overall.