Quality Control in the Healthcare Industry: An Analysis of Surgery Turnover Times

For virtually all hospitals, utilization rates are a critical managerial indicator of efficiency and are determined in part by turnover time. Turnover time is defined as the time elapsed between surgeries, during which the operating room is cleaned and preparedfor the next surgery. Lengthier turnover times result in lower utilization rates, thereby hindering hospitals’ ability to maximize the numbers of patients that can be attended to. In this thesis, we analyze operating room data from a two year period provided byEvangelical Community Hospital in Lewisburg, Pennsylvania, to understand the variability of the turnover process. From the recorded data provided, we derive our best estimation of turnover time. Recognizing the importance of being able to properly modelturnover times in order to improve the accuracy of scheduling, we seek to fit distributions to the set of turnover times. We find that log-normal and log-logistic distributions are well-suited to turnover times, although further research must validate this finding. Wepropose that the choice of distribution depends on the hospital and, as a result, a hospital must choose whether to use the log-normal or the log-logistic distribution. Next, we use statistical tests to identify variables that may potentially influence turnover time. We find that there does not appear to be a correlation between surgerytime and turnover time across doctors. However, there are statistically significant differences between the mean turnover times across doctors. The final component of our research entails analyzing and explaining the benefits of introducing control charts as a quality control mechanism for monitoring turnover times in hospitals. Although widely instituted in other industries, control charts are notwidely adopted in healthcare environments, despite their potential benefits. A major component of our work is the development of control charts to monitor the stability of turnover times. These charts can be easily instituted in hospitals to reduce the variabilityof turnover times. Overall, our analysis uses operations research techniques to analyze turnover times and identify manners for improvement in lowering the mean turnover time and thevariability in turnover times. We provide valuable insight into a component of the surgery process that has received little attention, but can significantly affect utilization rates in hospitals. Most critically, an ability to more accurately predict turnover timesand a better understanding of the sources of variability can result in improved scheduling and heightened hospital staff and patient satisfaction. We hope that our findings can apply to many other hospital settings.

Visualization Techniques For The Analysis Of Network Simulation Results

The Simulation Automation Framework for Experiments (SAFE) streamlines the de- sign and execution of experiments with the ns-3 network simulator. SAFE ensures that best practices are followed throughout the workflow a network simulation study, guaranteeing that results are both credible and reproducible by third parties. Data analysis is a crucial part of this workflow, where mistakes are often made. Even when appearing in highly regarded venues, scientific graphics in numerous network simulation publications fail to include graphic titles, units, legends, and confidence intervals. After studying the literature in network simulation methodology and in- formation graphics visualization, I developed a visualization component for SAFE to help users avoid these errors in their scientific workflow. The functionality of this new component includes support for interactive visualization through a web-based interface and for the generation of high-quality, static plots that can be included in publications. The overarching goal of my contribution is to help users create graphics that follow best practices in visualization and thereby succeed in conveying the right information about simulation results.

How Much Is Enough? an Analysis of the 5' Nontranslated Region of the Deformed Wing Virus in Honeybees

Honeybees are an essential component of today¿s agricultural system because of their role as pollinators. However, viruses, including a member of the Picornavirales order known commonly as Deformed Wing Virus (DWV), are compromising the health of honeybee colonies. Many picornaviruses, such as poliovirus, have been studied in depth because of their relation to human disease, but also because of their use of an Internal Ribosome Entry Site (IRES) to initiate translation. The primary goal of this thesis was to determine if the 5¿ Non-Translated Region (NTR) of Deformed Wing Virus (DWV) functions as an IRES. A secondary goal was to determine if there are specific parts of that 5¿ NTR that are important to IRES function. Six plasmids were constructed by inserting three different sections of the 5¿ NTR of DWV, in both sense and antisense directions, between two reporter genes. These plasmids, along with several control plasmids, were transfected into Sf9 cells, and post-transfection luciferase assays were conducted. Results were inconclusive. This could have been due to an inability of the plasmids to be expressed in Sf9 cells, an error in the construction of the plasmids, or a mechanical error in the assay procedure. At this time it appears most likely that the 5¿ NTR of DWV may be cell-type or species specific, and the next step would be to transfect the plasmids into a recently developed cultured honeybee cell line.