Article

The application of forecasting techniques to modeling emergency medical system calls in Calgary, Alberta.

DIRO, Université de Montréal, Montréal, Canada.
Health Care Management Science (Impact Factor: 1.05). 03/2007; 10(1):25-45.
Source: PubMed

ABSTRACT We develop and evaluate time-series models of call volume to the emergency medical service of a major Canadian city. Our objective is to offer simple and effective models that could be used for realistic simulation of the system and for forecasting daily and hourly call volumes. Notable features of the analyzed time series are: a positive trend, daily, weekly, and yearly seasonal cycles, special-day effects, and positive autocorrelation. We estimate models of daily volumes via two approaches: (1) autoregressive models of data obtained after eliminating trend, seasonality, and special-day effects; and (2) doubly-seasonal ARIMA models with special-day effects. We compare the estimated models in terms of goodness-of-fit and forecasting accuracy. We also consider two possibilities for the hourly model: (3) a multinomial distribution for the vector of number of calls in each hour conditional on the total volume of calls during the day and (4) fitting a time series to the data at the hourly level. For our data, (1) and (3) are superior.

Download full-text

Full-text

Available from: Pierre L’Ecuyer, Jan 03, 2014
3 Followers
 · 
126 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Emergency Departments (EDs) require advanced support systems for monitoring and controlling their processes: clinical, operational, and financial. A prerequisite for such a system is comprehensive operational information (e.g. queueing times, busy resources,...), reliably portraying and predicting ED status as it evolves in time. To this end, simulation comes to the rescue, through a two-step procedure that is hereby proposed for supporting real-time ED control. In the first step, an ED manager infers the ED's current state, based on historical data and simulation: data is fed into the simulator (e.g. via location-tracking systems, such as RFID tags), and the simulator then completes unobservable state-components. In the second step, and based on the inferred present state, simulation supports control by predicting future ED scenarios. To this end, we estimate time-varying resource requirements via a novel simulation-based technique that utilizes the notion of offered-load.
    Proceedings of the 2009 Winter Simulation Conference, WSC 2009, Hilton Austin Hotel, Austin, TX, USA, December 13-16, 2009; 01/2009
  • Source
  • [Show abstract] [Hide abstract]
    ABSTRACT: A sufficient staffing level in fire and rescue dispatch centers is crucial for saving lives. Therefore, it is important to estimate the expected workload properly. For this purpose, we analyzed whether a dispatch center can be considered as a call center. Current call center publications very often model call arrivals as a non-homogeneous Poisson process. This bases on the underlying assumption of the caller’s independent decision to call or not to call. In case of an emergency, however, there are often calls from more than one person reporting the same incident and thus, these calls are not independent. Therefore, this paper focuses on the dependency of calls in a fire and rescue dispatch center. We analyzed and evaluated several distributions in this setting. Results are illustrated using real-world data collected from a typical German dispatch center in Cottbus (“Leitstelle Lausitz”). We identified the Pólya distribution as being superior to the Poisson distribution in describing the call arrival rate and the Weibull distribution to be more suitable than the exponential distribution for interarrival times and service times. However, the commonly used distributions offer acceptable approximations. This is important for estimating a sufficient staffing level in practice using, e.g., the Erlang-C model.
    Health Care Management Science 03/2012; 16(1). DOI:10.1007/s10729-012-9207-x · 1.05 Impact Factor