Forecasting emergency medical service call arrival rates

We introduce a new method for forecasting emergency call arrival rates that combines integer-valued time series models with a dynamic latent factor structure. Covariate information is captured via simple constraints on the factor loadings. We directly model the count-valued arrivals per hour, rather than using an artificial assumption of normality. This is crucial for the emergency medical service context, in which the volume of calls may be very low. Smoothing splines are used in estimating the factor levels and loadings to improve long-term forecasts. We impose time series structure at the hourly level, rather than at the daily level, capturing the fine-scale dependence in addition to the long-term structure. Our analysis considers all emergency priority calls received by Toronto EMS between January 2007 and December 2008 for which an ambulance was dispatched. Empirical results demonstrate significantly reduced error in forecasting call arrival volume. To quantify the impact of reduced forecast errors, we design a queueing model simulation that approximates the dynamics of an ambulance system. The results show better performance as the forecasting method improves. This notion of quantifying the operational impact of improved statistical procedures may be of independent interest.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS442 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Forecasting Emergency Department Volumes Using Time Series and Other Techniques

The aim of this research is to forecast patient volumes in the Emergency Department of a regional hospital in Minnesota, which eventually will aid in addressing the issue of registered nurse staffing fluctuation, more specifically, productivity and capacity planning in the ED. Several methods are applied to forecast arrival patient volume, and cumulative patient volume to evaluate each model’s performance. The methods considered are linear regression, time series models and dynamic latent factor method. Long term forecast for as long as six months ahead is the goal here due union regulations that only allows for significant changes in registered nurse staffing schedule be put in place six months in advance. This long term forecast will enable administrators implement effective and timely changes to enhance productivity. The patient arrival count, where each patient is counted once in the system, is analyzed to see how many patients the department encounters hourly. Also, cumulative patient count which gives us an idea of how many patients are in the department at any given time was also considered, here patients are counted for every hour they are in the emergency department (ED). Patient who come to the ED are categorized by their acuity level. Of all the patients that came to the ED, 52% need urgent care; this group is also analyzed to predict their arrival volume. Lastly data was simulated with different patterns and the forecasting results from the different methods were compared and estimated. The forecast accuracy and performance for these models is then evaluated using out-of-sample forecasts for up to six months ahead. Mean square error (MSE), Root mean square error (RMSE) and mean absolute error (MAE) were utilized tosee which method is most reliable and also consistent

A Big Bang versus a Small Bang Approach: A Case Study of the Expeditionary Combat Support System (ECSS) and the Maintenance, Repair, and Overhaul Initiative (MROi)

In 2003, the United States Air Force embarked on one of the largest and most comprehensive logistical transformation to delineate the logistics community’s strategy for supporting the warfighter. A key aspect of this campaign plan was to leverage information technology through an enterprise resource planning (ERP) solution called the Expeditionary Combat Support System (ECSS), a “big-bang” approach. In early 2012, the ECSS program was cancelled mainly due to uncontrollable increases in costs and schedule overruns. In late 2012, the Air Force Sustainment Center (AFSC) launched the Maintenance, Repair, and Overhaul initiative (MROi), a “small-bang” approach, to increase enterprise visibility and efficiency across all three Air Logistics Complexes and Aircraft Maintenance and Regeneration Group. Additionally, MROi should fill some of the gaps deferred by ECSS. MROi is a means to salvage, correct, and continue the work started during the ECSS project. AFSC attempts to transform itself into a more capable organization thru MROi while providing savings to the taxpayers from resulting improvements in efficiencies. The MROi team attempts not to ignore lessons learned from ECSS; however, MROi is delayed by acquisition category determination, system implementation source selection, and network architecture evaluation, which are out of their control. Critical success factors, antecedents, and theories were discovered that can help develop a framework that may be of great importance to the government

Modélisation et optimisation d'un centre d'appels téléphoniques : étude du processus d'arrivée

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal