Improving Emergency Department Patient Flow Through Near Real-Time Analytics

ABSTRACT IMPROVING EMERGENCY DEPARTMENT PATIENT FLOW THROUGH NEAR REAL-TIME ANALYTICS This dissertation research investigates opportunities for developing effective decision support models that exploit near real-time (NRT) information to enhance the operational intelligence within hospital Emergency Departments (ED). Approaching from a systems engineering perspective, the study proposes a novel decision support framework for streamlining ED patient flow that employs machine learning, statistical and operations research methods to facilitate its operationalization. ED crowding has become the subject of significant public and academic attention, and it is known to cause a number of adverse outcomes to the patients, ED staff as well as hospital revenues. Despite many efforts to investigate the causes, consequences and interventions for ED overcrowding in the past two decades, scientific knowledge remains limited in regards to strategies and pragmatic approaches that actually improve patient flow in EDs. Motivated by the gaps in research, we develop a near real-time triage decision support system to reduce ED boarding and improve ED patient flow. The proposed system is a novel variant of a newsvendor modeling framework that integrates patient admission probability prediction within a proactive ward-bed reservation system to improve the effectiveness of bed coordination efforts and reduce boarding times for ED patients along with the resulting costs. Specifically, we propose a cost-sensitive bed reservation policy that recommends optimal bed reservation times for patients right during triage. The policy relies on classifiers that estimate the probability that the ED patient will be admitted using the patient information collected and readily available at triage or right after. The policy is cost-sensitive in that it accounts for costs associated with patient admission prediction misclassification as well as costs associated with incorrectly selecting the reservation time. To achieve the objective of this work, we also addressed two secondary objectives: first, development of models to predict the admission likelihood and target admission wards of ED patients; second, development of models to estimate length-of-stay (LOS) of ED patients. For the first secondary objective, we develop an algorithm that incorporates feature selection into a state-of-the-art and powerful probabilistic Bayesian classification method: multi-class relevance vector machine. For the second objective, we investigated the performance of hazard rate models (in particual, the non-parametric Cox proportional hazard model, parametric hazard rate models, as well as artificial neural networks for modeling the hazard rate) to estimate ED LOS by using the information that is available at triage or right after as the covariates in the models. The proposed models are tested using extensive historical data from several U.S. Department of Veterans Affairs Medical Centers (VAMCs) in the Mid-West. The Case Study using historical data from a VAMC demonstrates that applying the proposed framework leads to significant savings associated with reduced boarding times, in particular, for smaller wards with high levels of utilization. For theory, our primary contribution is the development of a cost sensitive ward-bed reservation model that effectively accounts for various costs and uncertainties. This work also contributes to the development of an integrated feature selection method for classification by developing and validating the mathematical derivation for feature selection during mRVM learning. Another contribution stems from investigating how much the ED LOS estimation can be improved by incorporating the information regarding ED orderable item lists. Overall, this work is a successful application of mixed methods of operation research, machine learning and statistics to the important domain of health care system efficiency improvement

Probabilistic forecasting and comparative model assessment, with focus on extreme events

Probabilistic forecasts allow to quantify the prediction uncertainty and are essential for informed decision making. We investigate how to evaluate probabilistic forecasts with an emphasis on extreme events, and how to make and evaluate forecasts based on simulation output in Bayesian forecasting models. Further, we propose new models and estimation approaches for statistical post-processing of ensemble forecasts in numerical weather prediction

The tragedy of the commons in artisanal gold mining: evaluation of mechanisms of cooperation with simulation and economic experiments

Abstract: This thesis is about how collective action –associative entrepreneurship– can be fostered in artisanal and small-scale gold mining. This kind of association is aimed at, among other things, allowing small-scale gold miners to gather the financial capital that is required to obtain the type of technologies that reduce mercury use in the gold recovery process, and therefore the harmful effects of mercury pollution of ecosystems and human health. Given the public-good dilemma that is faced by these individuals, I study possible institutional arrangements by which associative entrepreneurship may be encouraged. The methods to achieve this include the construction of a behavioral simulation model using System Dynamics. As part of both the model building and its validation process I make use of the results of economic experiments carried out both in the lab and the field. The results of the economic experiments do not reject the hypothesis which states that sustained collective action does not self-emerge as a solution to the public-good dilemma. In this thesis I analyze two institutional arrangements: co-management and exclusion from private benefits. Of these two, only co-management shows a statistically significant impact on the establishment of a permanent collective action. However, in the field experiment this effect of co-management is undermined when it is combined with exclusion from the private benefits. From the behavioral simulation model, it is shown that reciprocity, free-riding and profit maximization are the behavioral aspects that mainly drive decision-making when dealing with the public-good dilemma. With an external intervention such as co-management, individuals get more aware of the social dilemma they face and collective action is sustained over time. From a policy viewpoint these results suggest the importance of interventions programs such as education projects, training in alternative practices and technologies, and campaigns to foster social capital. Moreover, the experimental results cast doubt on the effectiveness of economic incentives to change some practices in the production process of gold. However, simulation results show that the implementation of stricter incentives could make miners to increase their commitment to sustain the entrepreneurial organizationResumen: Esta tesis está relacionada con la manera en que se puede promover la acción colectiva –asociación empresarial– en la minería aurífera artesanal y de pequeña escala. Bajo este esquema de asociación se pretende, entre otros, reunir el capital financiero necesario para obtener el tipo de tecnologías que permiten reducir el uso de mercurio en el proceso de recuperación de oro, así como los efectos nocivos que la contaminación por mercurio produce en ecosistemas y la salud humana. Dado el dilema de tipo bien público que enfrentan estos individuos, se estudian posibles arreglos institucionales bajo los cuales se promueva la asociación empresarial. Los métodos utilizados incluyen la construcción de un modelo de simulación de comportamiento usando Dinámica de Sistemas. Como parte del proceso de construcción del modelo y su validación, se hace uso de los resultados de experimentos económicos realizados en el laboratorio y en el campo. Los resultados de los experimentos económicos no rechazan la hipótesis según la cual una acción colectiva que se mantenga en el tiempo no surge como una solución al dilema de tipo bien público. En la tesis se estudian dos arreglos institucionales: co-manejo y exclusión de los beneficios privados. De los dos, solamente co-manejo muestra un impacto estadísticamente significativo sobre el establecimiento de una acción colectiva permanente. Sin embargo, en el experimento de campo este efecto se debilita al combinar co-manejo con exclusión de los beneficios privados. Del modelo de simulación de comportamiento se observa que reciprocidad, oportunismo y la búsqueda de maximización de beneficios son los aspectos conductuales que explican la toma de decisiones al afrontar el dilema de tipo bien público. Con una intervención tal como co-manejo, los individuos adquieren una mayor percepción del dilema social que enfrentan y la acción colectiva se sostiene en el tiempo. Desde el punto de vista de política, los resultados sugieren la importancia de programas de intervención tales como proyectos educativos, entrenamiento en prácticas y tecnologías alternativas, y campañas para incrementar el capital social. Además, los resultados experimentales ponen en duda la efectividad de incentivos económicos diseñados para cambiar algunas prácticas en el proceso de producción de oro. Sin embargo, los resultados de simulación muestran que la implementación de un incentivo más estricto podría hacer que los mineros incrementen su compromiso con el sostenimiento de la organización empresarialDoctorad

A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium

When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

SHIFTING GROUNDS: SCIENTIFIC AND TECHNOLOGICAL CHANGE AND INTERNATIONAL REGIMES FOR THE OCEAN AND OUTER SPACE

Emerging planetary-scale environmental problems, such as climate change and space debris, indicate a growing need for effective governance regimes for domains beyond the borders of territorial nation-states. This dissertation addresses the basic question: what explains patterns of success and dysfunction in regimes for non-terrestrial spaces? Under what conditions can global commons regimes function to achieve their goals? The answer depends in a fundamental way on scientific knowledge and technological capability, which create, define, and describe the problems, interests, and practices that shape the formation and features of governance regimes, and thus create the conditions for their effective functioning. This project employs and extends recent revivalist geopolitical approaches examining the influences of material factors (geography, ecology, and technology), and applies them to explain important features of regimes for the ocean and orbital space. This approach claims that geography, ecology, and technology together constitute an influencing context, which creates specific problem structures and constrains possible solution sets, and thereby sets conditions for regime performance. In contrast, recent post-modernist and constructivist approaches discount the importance and influence of material contexts in shaping politics, and are incapable of explaining important aspects of regimes. Rationalist (interest-centered) approaches to theorizing regimes employ thin treatments of the material context, limiting their ability to explain regime content and effectiveness. The explanatory traction of material-contextual factors is demonstrated by a detailed examination of regime formation, content and effectiveness over four periods of ocean governance across five centuries, and orbital space over the last sixty years. These cases demonstrate that successful regime formation must foreground scientific uncertainty, ecological dynamics, and the balance of technological capability. To the extent that global commons regimes ignore the existence and dynamism of these material structures, they are more likely to fail to achieve their goals. Greater consideration of material contexts produces a strengthened International Relations theory of regimes. These findings also suggest ways to improve regime design, outlined in the concluding chapter