Yossi Mesika

Simulation-based models of emergency departments:: Operational, tactical, and strategic staffing

The Emergency Department (ED) of a modern hospital is a highly complex system that gives rise to numerous managerial challenges. It spans the full spectrum of operational, clinical, and financial perspectives, over varying horizons: operational—a few hours or days ahead; tactical—weeks or a few months ahead; and strategic, which involves planning on monthly and yearly scales. Simulation offers a natural framework within which to address these challenges, as realistic ED models are typically intractable analytically. We apply a general and flexible ED simulator to address several significant problems that arose in a large Israeli hospital. The article focuses mainly, but not exclusively, on workforce staffing problems over these time horizons. First, we demonstrate that our simulation model can support real-time control, which enables short-term prediction and operational planning (physician and nurse staffing) for several hours or days ahead. To this end, we present a novel simulation-based technique that implements the concept of offered-load and discover that it performs better than a common alternative. Then we evaluate ED staff scheduling that adjusts for midterm changes (tactical horizon, several weeks or months ahead). Finally, we analyze the design and staffing problems that arose from physical relocation of the ED (strategic yearly horizon). Application of the simulation-based approach led to the implementation of our design and staffing recommendations.

Toward simulation-based real-time decision-support systems for emergency departments

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 EDs 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.

Exploratory search over social-medical data

In this demo we shall present the IBM Patient Empowerment System (PES), and more specifically, its social-medical discovery sub-system. Social and medical data are represented using entities and relationships and are explored using a combination of expressive, yet intuitive, query language, faceted search, and ER graph navigation. While this demonstration focuses on the healthcare domain, the underlining search technology is generic and can be utilized in many other domains. Therefore, this demo has two main contributions. First, we present a novel entity-relationship indexing and retrieval solution, and discuss its implementation challenges. Second, the demonstration depicts a practical entity-relationship discovery technology in a real domain setting within a real IBM system.

Comparing three basic models for seasonal influenza

In this paper we report the use of the open source Spatiotemporal Epidemiological Modeler (STEM, www.eclipse.org/stem) to compare three basic models for seasonal influenza transmission. The models are designed to test for possible differences between the seasonal transmission of influenza A and B. Model 1 assumes that the seasonality and magnitude of transmission do not vary between influenza A and B. Model 2 assumes that the magnitude of seasonal forcing (i.e., the maximum transmissibility), but not the background transmission or flu season length, differs between influenza A and B. Model 3 assumes that the magnitude of seasonal forcing, the background transmission, and flu season length all differ between strains. The models are all optimized using 10 years of surveillance data from 49 of 50 administrative divisions in Israel. Using a cross-validation technique, we compare the relative accuracy of the models and discuss the potential for prediction. We find that accounting for variation in transmission amplitude increases the predictive ability compared to the base. However, little improvement is obtained by allowing for further variation in the shape of the seasonal forcing function.

NitroGen: rapid development of mobile applications

Constructing a mobile application is expensive and time consuming. In this paper, we present NitroGen which is a platform independent tool that provides a consumable integrated set of capabilities to construct mobile solutions aiming at reducing development and maintenance costs. NitroGen is a visual, mostly codeless, cloud-based platform to construct mobile applications. It can easily connect to back-end services thus enable fast and facile development in enterprises. Evaluating NitroGen, we found among others, that participants learned it fast and found it simple and suitable for mobile applications development.

Modeling in Space and Time

This chapter describes the Spatiotemporal Epidemiological Modeler (STEM), now being developed as an open source computer software system for defining and visualizing simulations of the spread of infectious disease in space and time. Part of the Eclipse Technology Project, http://www.eclipse.org/ stem, STEM is designed to offer the research community the power and extensibility to develop, validate, and share models on a common collaborative platform. Its innovations include a common representational framework that supports users in creating and configuring the components that constitute a model. This chapter defines modeling terms (canonical graph, decorators, etc.) and key concepts (e.g., labels, disease model computations) are discussed. Figures illustrate the types of visualizations STEM provides, including geographical views via GIS and Google Earth™ and report generated graphics.

Developing enterprise mobile applications the easy way

Constructing a mobile application in the enterprise is expensive and time consuming. On average, deploying a mobile application is estimated at one week of effort per screen. We present an evaluation of NitroGen, a platform-independent tool for constructing mobile solutions. The tool provides a consumable integrated set of capabilities aimed at reducing development and maintenance costs. NitroGen is a mostly codeless, cloud-based, platform for visually constructing mobile applications. We demonstrate how students with some skills in web development and no skills in mobile development have successfully learned and used NitroGen to implement a small-scale three-screen application, which uses existing back-end services—all in less than 90 minutes.