Scott Levin

Mehdi Jalalpour3
Andrea Freyer Dugas2
Andrea Dugas2
Richard Rothman2
3Mehdi Jalalpour
2Andrea Freyer Dugas
2Andrea Dugas
2Richard Rothman
2Gladston Prates Moreira
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  • Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa +1 other
  • 2013
BACKGROUND We developed a practical influenza forecast model based on real-time, geographically focused, and easy to access data, designed to provide individual medical centers with advanced warning of the expected number of influenza cases, thus allowing for sufficient time to implement interventions. Secondly, we evaluated the effects of incorporating a(More)
STUDY OBJECTIVE The objective of this investigation is to determine time-dependent workload patterns for emergency department (ED) physician teams across work shifts. A secondary aim was to demonstrate how ED demand patterns and the timing of shift changes influence the balance of workload among a physician team. METHODS Operational measurements of an(More)
BACKGROUND Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found(More)
OBJECTIVE Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. METHODS We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous(More)
Wait or queuing time is a principal performance measure for many discrete-event simulation (DES) models in healthcare. However, variation in wait time is often caused by both occupied downstream servers (e.g., beds) and organizational and human transition processes. DES models that attribute wait solely to occupied servers, ignoring transition process(More)
  • Joseph Klembczyk, Mehdi Jalalpour, Scott Levin, Raynard Washington, Jesse M. Pines, Richard Rothman +1 other
  • 2015
and reproduction in any medium, provided the original work is properly cited. Objective To test if Google Flu Trends (GFT) is predictive of the volume of influenza and pneumonia emergency department (ED) visits across multiple United States cities. Introduction GFT is a surveillance tool that gathers data on local internet searches to estimate the emergence(More)
BACKGROUND Hospital surge capacity (HSC) is dependent on the ability to increase or conserve resources. The hospital surge model put forth by the Agency for Healthcare Research and Quality (AHRQ) estimates the resources needed by hospitals to treat casualties resulting from 13 national planning scenarios. However, emergency planners need to know which(More)
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