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Objective: There have been a large number of studies that have investigated the relationship between outcomes and provider volume for a wide variety of medical conditions and surgical conditions. The objective of this study was to explore the relation between hospital volume and risk-adjusted mortality following percutaneous coronary intervention between 2003 and 2004 in Korea. Methods: This is a retrospective analysis of database in National Health Insurance Review & Assessment Service and Korean National Statistical Office. The study data set confined to the ICD-10 diagnosis and procedure codes that were recorded in the National Health Insurance Review Agency. Risk modeling was performed through logistic regression and validated with cross-validation. The statistical performance of the developed model was evaluated using c-statistics, R², and Hosmer-Lemeshow statistic. Crude and risk-adjusted 30-day mortality was evaluated among patients who underwent Percutaneous Coronary Intervention (PCI) between 2003 and 2004 at low (less 200 cases/year), medium (200-399 cases/year), and high (400 cases or more/year) PCI volume hospitals. Results: The final risk-adjustment model consisted of ten risk factors for 30-day mortality. These factors were found to have statistically sig-nificant effects on patient mortality. The c-statistic and Hosmer-Lemeshow y} goodness-of-fit test and the model's performance were good [R²=0.147, c-statistic 0.823,4.1037 (p=0.8476)|. A total number of 60 low-volume hospitals (9.071 patients) and 27 medium-volume hospitals (15.623 patients) and 15 high-volume hospitals (19.669 patients) were included. Crude 30-day mortality rate was 1.4%, 1.1%, and 1.0% (p=0.0106) in each volume hospitals. But risk-adjusted mortality rate was not significantly different among three groups (1.3%, 1.0%, and 1.1% in each volume hospitals). Conclusion: Although we found a significant different crude 30-day mortality rates according to hospital PCI volume, but did not find a relation-ship between hospital volume and 30-day risk-adjusted mortality rates following PCI in Korea. [ABSTRACT FROM AUTHOR]

Advanced Transportation Management and Information Systems (ATMIS) can use dynamic origin-destination (OD) demand models to make short-term predictions regarding developments in traffic states. However, existing dynamic OD prediction models do not achieve this reliably for two main reasons. First, this is a bi-level system that consists of a traffic flow process at the lower level and a dynamic OD process at the upper level. Due to the inherent non-convexity of bi-level systems, it is difficult to guarantee that any calculated solution is globally optimal. In this paper, we propose a new traffic flow model that uses real-time traffic data, such as traffic flows, speed and occupancy, collected from vehicle detectors, to address the difficulties that arise in existing bi-level programming formulations. Second, in order to estimate a dynamic OD demand between on and off-ramps on the freeways, a traffic flow model is needed to estimate the proportion of traffic moving between them. In this paper, we present a dynamic traffic estimation model based on Kerner's three-phase traffic theory, which represents the complexity of traffic phenomena based on phase transitions between free-flow, synchronized flow and moving jam phases, and on their complex nonlinear spatio-temporal features. The present model explains and estimates traffic congestion in terms of speed breakdown, phase transition and queue propagation. We show how a genetic algorithm can be used to solve this to estimate dynamic OD flows and the associated link, on and off-ramp flows during each time interval using traffic data collected from vehicle detection systems implemented on Korean freeways. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]