Objective: The objective of this paper is to highlight a study on optimizing the full-time equivalent (FTE) for Spanish and Mandarin interpreters at Christiana Care Health System. In this study, there were multiple challenges that needed to be addressed, and a multi-method approach was taken. Methods: These methods include: (1) time-motion study to quantify interpreter workflow and variability of duration of time needed for each task; (2) an integer program to optimize the number of interpreters needed per hour based on historical demand patterns for interpreter services; (3) Discrete-Event Simulation (DES) to examine the use of agency interpreters in order to meet demand; (4) cost modelling to convert FTEs and the use of agency interpreters into overall costs to the hospital; and (5) sensitivity analysis to evaluate alternative number of interpreter FTEs and their corresponding costs to the hospital. Results: Overall cost to the hospital is predicted to decrease with additional FTE interpreters, up to a threshold level above which the cost will start to increase. Through this innovative methodology used in this paper, we predict that hiring 3.5 more FTEs for Spanish interpreters will result in 9.07% of cost savings, and predict that hiring one FTE for Mandarin interpreters will result in 25.87% in cost savings compared to the current expense of providing Mandarin language interpretation. Conclusions: Contrary to intuition, increasing number of FTEs results in cost savings. Besides the financial benefit, hospitals will also be able to ensure the quality of health services that Limited English Proficiency (LEP) patients and families receive.

No abstract

COVID-19, a novel disease that spreads across the globe, has posed multiple challenges to the healthcare systems around the world. Due to the lack of understanding of the spread and management of this disease, one major challenge is for healthcare systems to anticipate the volumes and needs of patients infected with the disease. In order to provide insights into optimal allocation of resources from preparing ChristianaCare for the pandemic to the recovery of the healthcare system, industrial engineering and predictive modeling approaches are used. This paper discusses five interrelated studies that utilize various techniques to inform multiple aspects of the healthcare system in order to be better prepared for the pandemic.

No abstract

This article is the second part of a two-part research study of a theoretical water-treatment system based on the NASA Baseline Values and Assumptions Document (BVAD) [7]. It focuses on the decisionmaking model created to choose the "best" policy to be applied to the water-treatment system based on hourly system conditions. Due to the resemblance between the behavior of this system and the Markovian process, this system is constructed based on the Markovian model. The water system consists of two subsystems: a hygiene-water subsystem that supplies water for laundry, urinal fl ush, dish wash, oral hygiene and shower; and a potable-water subsystem that supplies water for drinking and food rehydration. In order to assess the conditions of the water system, various aspects of the system, such as hourly and accumulated water defi ciency, and amount of clean water available for use, are captured on an hourly basis. A baseline policy and policies derived from it are tested to fi nd the best policy for the system to operate under the most economical conditions while providing enough clean water for crew consumption. The best policy is obtained through various mathematical modeling techniques. Outcomes are compared against a system that uses the baseline policy. Results show that an intuitively "good" policy may not always be the best policy for the system. The system performance is measured in terms of a reward value, which is assigned based on the system conditions.