WSC 2009

WSC 2009 Final Abstracts

Applications - Health Care Track

Monday 10:30:00 AM 12:00:00 PM
Healthcare in the UK

Chair: Sally Brailsford (University of Southampton)

Three Critical Challenges for Simulation and Modeling in Healthcare
Terry Young, Julie Eatock, Mohsen Jahangirian, and Aisha Naseer (Brunel University) and Richard J Lilford (University of Birmingham)

By most measures, the adoption of modeling and simulation techniques in healthcare service development falls well short of the uptake of such techniques evident in other sectors, such as business and commerce or aerospace and the military. The question is, why? To answer this, we consider three questions and then turn to the nature of answer which might lead towards greater adoption. The first is the vexed question of how good is good enough? The second concerns how best modeling should link through to decision-making; and the third concerns the culture needed to make the most of modeling and simulation (and whether it is worth the effort to make the transformation). From these, we draw an agenda for further enquiry in terms of stakeholders, their culture, data, and expectations, and the case in terms of value.

Implementation Issues of Modeling Healthcare Problems: Misconceptions and Lessons
Tillal Eldabi (Brunel University)

Since the beginning of the 21st century, the role of modeling and simulation in healthcare domain has seen unprecedented attention from the academic community. So much literature has focused on barriers facing implementation and uptake of modeling and simulation by the healthcare community. This article focuses on this issue and goes beyond that and examines the nature of healthcare problems and the causes of barriers using the concept of wicked problems. There is a clear mismatch between the wicked nature of healthcare problems and the tame approaches proposed by the modeling community. The article concludes that with the existence of such mismatch, implementation cannot be used to measure success, and further suggests guiding principles when developing modeling and simulation approaches to tackle wicked healthcare problems. These are based around redefining the meaning of success, problem profiling, modeling methods, and modeling skills.

Stakeholder Engagement in Health Care Simulation
Sally C. Brailsford, Timothy Bolt, Con Connell, Jonathan H. Klein, and Brijesh Patel (University of Southampton)

The RIGHT project (Research Into Global Healthcare Tools, is a collaborative project involving five British universities, funded by the UK’s Engineering and Physical Sciences Research Council. Phase 1 of the project is due to be completed in October 2009, and this paper describes one strand of the research, namely identifying some of the is-sues with involving stakeholders in simulation modeling in healthcare. Not the least of these is actually identifying who the stakeholders are! Other problems identified are equally tricky, as they involve deeply rooted cultural and behavioral attitudes as well as complex organizational relationships. One of the underlying aims of the next phase of RIGHT is to tackle these problems and to develop a methodology for more effective stakeholder engagement with simulation modeling.

Monday 1:30:00 PM 3:00:00 PM
Simulation of Emergency Systems

Chair: Adrian Ramirez (Arizona State University)

Ambulance Redeployment: An Approximate Dynamic Programming Approach
Matthew S. Maxwell, Shane G. Henderson, and Huseyin Topaloglu (Cornell University)

Emergency medical service (EMS) providers are charged with the task of managing ambulances so that the time required to respond to emergency calls is minimized. One approach that may assist in reducing response times is ambulance redeployment, i.e., repositioning idle ambulances in real time. We formulate a simulation model of EMS operations to evaluate the performance of a given allocation policy and use this model in an approximate dynamic programming (ADP) context to compute high-quality redeployment policies. We find that the resulting ADP policies perform much better than sub-optimal static policies and marginally better than near-optimal static policies. Representative computational results for Edmonton, Alberta are included.

Cooperative Strategies to Reduce Ambulance Diversion
Reidar Hagtvedt (University of Alberta), Paul Griffin, Pinar Keskinocak, and Mark Ferguson (Georgia Institute of Technology) and Gregory Todd Jones (Georgia State University & CNCR)

Overcrowding in the emergency departments (ED) has led to an increase in the use of ambulance diversion (AD), during which a hospital formally is not accepting patients by ambulance. We use a number of tools to considers methods by which hospitals in a metro area may cooperate to reduce diversion, including contracts and pressure from outside regulators. The tools include a birth-death process, discrete event simulations, agent-based simulation model, and some game theory to examine the potential for cooperative strategies. We use data to suggest a functional form for the payoff of such games. We find that a centralized form of routing is needed, as voluntary cooperation does not appear to be robust in the presence of noise or strategic behavior, and ethical considerations also have a significant impact.

Analysis of Ambulance Diversion Policies for a Large-size Hospital
Adrian Ramirez Nafarrate, John W. Fowler, and Teresa Wu (Arizona State University)

The overcrowding of Emergency Departments (EDs) is a well known problem that has been analyzed on multiple occasions. Queuing theory and simulation have been applied extensively to specific ED situations, such as staff planning, waiting time reduction and capacity investment. However, there are remaining problems in the EDs that need more study. One of them is the ambulance diversion, which may cause a delay in the treatment of urgent patients therefore jeopardizing their welfare. Since ED are complex system and setting the diversion state in EDs is a subjective decision, a detailed modeling and analysis of cause and effects of such a decision is beneficial. In this research, we build a case study and analyze the impact of diversion policies in various performance measures of the ED through a designed experiment using a discrete event simulation model.

Monday 3:30:00 PM 5:00:00 PM
Simulation of Emergency Departments

Chair: Hari Balasubramanian (University of Massachusetts Amherst)

Resource Management and Process Change in a Simplified Model of the Emergency Department
Ekkehard Beck and Hari Balasubramanian (University of Massachusetts) and Philip L. Henneman (Tufts University School of Medicine)

Using a simplified model of an emergency department (ED), we illustrate a 2-step methodology for determining the optimal mix of resources (beds, clerks, triage nurses, registered nurses, and physicians) for different arrival rates. These arrival rates cover the range of annual visit volumes typically observed in EDs in the United States. We also use the model to test a widely recommended process change in EDs: bedside registration. Rather than perform registration immediately after triage, registration is now performed only after the patient is placed in an ED bed and assessed by a nurse and physician. Our results show that bedside registration is efficient only when sufficient beds are available; when an ED is crowded and bed availability is low it actually leads to an increased length of stay. We view our model as a first step in the development of a more elaborate, multiple-acuity ED model.

Simulating the Effect of Physician Triage in the Emergency Department of Akershus University Hospital
Lene Berge Holm and Fredrik A. Dahl (Akershus University Hospital)

The Norwegian Board of Health Supervision has strongly recommended that all hospitals need to take action to improve the long waiting times before patients are seen by a physician in the Hospital Emergency Department. Akershus University Hospital has complied with this by introducing physician triage every weekday from 10am to 7pm. Because it is difficult to see the influence this has had on the patient flow in the ED, the Hospital Research Department has developed two simulation models to estimate the effect on patient waiting time by replacing nurse triage with that of a physician. The results of the simulations show that the waiting time for an initial physician evaluation was reduced from 117 minutes to 26 minutes, while the waiting time for a physician examination was reduced only by 7 minutes. The total waiting time in the ED was reduced from 297 to 288 minutes when introducing physician triage.

Estimating Patient Surge Impact on Boarding Time in Several Regional Emergency Departments
Martin Miller and David Ferrin (FDI Operations Modeling & Simulation) and Niloo Shahi (LAC+USC Healthcare Network)

The sudden or prolonged increase in patient arrivals to hospital Emergency Departments can cause overcrowding which adversely affects patient care. Healthcare leadership must anticipate and prepare for patient surge before it happens. They need to understand how much overcrowding will occur with each incremental increase in patient volume. This paper describes how simulation was used to determine the impact of various patient surge levels on three regional Emergency Departments. This paper also describes the impact of potential action items which the hospitals can take to mitigate their overcrowding.

Tuesday 8:30:00 AM 10:00:00 AM
Simulation of Health Procedures Systems

Chair: Ozgur Araz (Arizona State University)

Utilization of Discrete Event Simulation in the Prospective Determination of Optimal Cardiovascular Lab Processes
John S Pirolo (Saint Thomas Health Services), Abhijit Ray, Matt Gadzinski, Mario Manese, and Brannon Garvert (Cerner Corporation), George Scoville and Howard Walpole (Saint Thomas Heart, The Heart Group) and Bob Amland, Rebecca Boos, Ian Mamminga, Joan Brown, and Kipp Donlon (Cerner Corporation)

The clinical character of cardiovascular disease creates challenges in optimizing cardiovascular catheterization lab (CVL) throughput. These challenges are due to case load fluctuations caused by unscheduled Emergency Department patients and simultaneous conflicting demands on cardiologist time. The simulation model provides insight into the complex relationship between patient acuity, treatment, occurrence of queues and bottlenecks in the transfer of patients. The study performed a comparative analysis between CVL operational schemes and assessed how those schemes impacted a variety of metrics related to throughput improvement. A current state model was developed, pertinent data was collected for the patient group and validation of the model was performed. Analysis of simulation results determined the most efficient CVL schedule and resource allocation to improve throughput and resource utilization. The study provides objective guidance to the optimal process modification and allows comparison of the relative differences in cost between the several redesign options.

A Java Class Library for Simulating Peri-Operative Processes
Philip Troy (Les Entreprises TROYWARE) and Lawrence Rosenberg (Sir Mortimer B. Davis Jewish General Hospital)

To address the mismatch between supply and demand for peri-operative capacity, senior management of the Department of Surgical Services at the Sir Mortimer B. Davis Jewish General Hospital in Montreal designed and developed a discrete event simulation platform for modeling its peri-operative processes. Design goals included ensuring that the platform could be used for both long-term capacity planning and short-term scheduling, that it could be readily modified and extended, that it be comprehensive and fast, that it have a multi-level 2D animation capability, that it reuse software components, and that it could be embedded into other software. The primary outcomes achieved were the development of a Java class library to support the development of peri-operative process simulation models, and a preliminary model, built with the class library, that is currently being used to help understand the need for surgical beds.

Modeling and Simulation of Cataract Surgery Processes
Sonja Reindl and Lars Mönch (University of Hagen) and Maria Mönch and Andreas Scheider (Eye Hosptial, Kliniken Essen-Süd)

In this paper, we present results of a simulation study that is related to cataract surgeries in an eye hospital in Germany. Cataract extraction is one of the most common and cost effective surgical procedures. We describe the process flow of the cataract surgeries in detail. We are interested in reducing waiting time of the patients and increase utilization of the operating rooms (OR). The data collection and input modeling effort using real-world data is described. The proposed simulation model using the SLX simulation package is presented. We discuss some suggestions for improvements based on the conducted simulation study. Possible extensions of the present research are also suggested.

Tuesday 10:30:00 AM 12:00:00 PM
Simulation for Healthcare Resource Management

Chair: Steve Roberts (North Carolina State University)

Simulation Model to Investigate Flexible Workload Management for Healthcare and Servicescape Environment
Michael Thorwarth and Amr Arisha (Dublin Institute of Technology) and Paul Harper (Cardiff University)

High demand and poor staffing conditions cause avoidable pressure and stress among healthcare personnel which results in burnout symptoms and unplanned absenteeism which are hidden cost drivers. The work environment within an emergency department is commonly arranged in a flexible workload which is highly dynamic and complex for the outside observer. Using detailed simulation modeling within structured modeling methods, a comprehensive model to characterize the nurses’ time utilization in such flexible dynamic workload environment was investigated. The results have been used to derive a generalized analytic expression that describes certain settings that lead to an instable queuing system with serious consequences for the healthcare facility. Thus decision makers are hence equipped with a tool which allows identifying and preventing such conditions that affect service quality level.

Simulating Public Health Emergency Response: A Case Study of the 2004 North Carolina State Fair E.coli Outbreak
Sharolyn Wynter and Julie Simmons Ivy (North Carolina State University)

Despite the investment of billions of dollars in federal funding towards emergency preparedness and response initiatives, broadly accepted performance measures for determining the efficacy of these systems have yet to be established. The inability to accurately capture this information creates knowledge gaps which hinder the ability to measure the true degree of preparedness. As a key communications component of North Carolina’s Public Health Information Network (NC PHIN), the North Carolina Health Alert Network (NCHAN) serves as a promising means to measure emergency preparedness and response. We seek to determine how NCHAN has increased emergency preparedness and response capacity by presenting a simulation of the 2004 State Fair E.coli Outbreak. We find that although the capacity exists within NCHAN to increase emergency preparedness and response, other factors limit NCHAN’s effectiveness. Our findings suggest that proper resource allocation will be necessary in order to realize the true efficacy of NCHAN.

A Bayesian Pharmacometric Approach for Personalized Medicine - a Proof of Concept Study with Simulated Data
Gary Blau and Seza Orcun (Purdue University)

The objective of this research program is to optimize drug dose regimen for an individual, using minimally invasive clinical testing, in order to reduce both the total cost of treatment and the risk for over or under medication using a Bayesian modeling approach. The challenge is to extract the PharmacoKinetic/PharmacoDynamic(PK/PD) parameters for an individual from population level plasma concentration information gathered in clinical trials along with one or two plasma samples from an individual and use these personalized parameters in determining most appropriate dose regimen for a specific patient. In this study we illustrate the plausibility of our methodology through a proof-of-concept study with simulated data.

Tuesday 1:30:00 PM 3:00:00 PM
Simulation of Pandemic Scenarios

Chair: Dionne Aleman (University of Toronto)

Accounting for Individual Behaviors in a Pandemic Disease Spread Model
Dionne M. Aleman, Theodorus G. Wibisono, and Brian Schwartz (University of Toronto)

Mathematical models to predict the spread of disease during a pandemic largely require overly simplistic assumptions about disease transmission within populations. One significant shortcoming of these models is the inability to account for varying types and amount of contact between individuals, to address individuals' behaviors or to assess the effectiveness of mitigation strategies. We present a non-homogeneous agent-based simulation of a pandemic in an urban population that accounts for individual behavior and transmission rates in different scenarios. The model is compact and parallelizable, and runs in reasonable computational time for an urban population of nearly five million individuals. Results are presented from modeling the spread of pandemic influenza in the Greater Toronto Area, Ontario, Canada.

A Pandemic Influenza Simulation Model for Preparedness Planning
Ozgur Merih Araz, John W. Fowler, Timothy Lant, and Megan Jehn (Arizona State University)

Pandemic influenza continues to be a national and international public health concern in today’s world and get significant attention recently with the recent swine flu outbreak worldwide. Many countries have developed and updated their preparedness plans for pandemic influenza. School closure has been recommended as one of the best ways to protect children and indeed all susceptible individuals in a community during a possible disease outbreak. In this paper, we present a geospatial and temporal disease spread model for pandemic influenza affecting multiple communities. School closure, one of the social distancing policies, is investigated in this paper with several questions such as: at what level should schools be closed, for how long should they be kept closed, and how should be the re-opening decisions made. These questions are considered in terms of minimizing: the total infection cases, total mortalities, and the impact on educational services to school children.

Staffing a Pandemic Urgent Care Facility During an Outbreak of Pandemic Influenza
Brendan D. See, Shih-Ping Liu, Yi-Wei Lu, and Qi Pang (University of Michigan - Ann Arbor)

A simulation of an influenza pandemic is analyzed for the greater Ann Arbor, Michigan region. Focus is placed on a Pandemic Urgent Care center (PUC), where patients of mild and moderate severity are treated. The number of registration assistants, doctors, and nurses to staff as well as the amount of capacity to add to the PUC and adjoining infusion clinic is analyzed for different attack rates. Focus is placed on the peak day of the pandemic, and patients arrive on that day according to an empirical distribution from emergency department arrival data. ProModel is used to evaluate the system and perform sensitivity analysis. The analysis finds that the optimal staffing levels to keep average patient waiting times at a reasonable level is dependent on the attack rate and the daily interarrival rate of patients, and that more staff is needed when arrival patterns have increased variability.

Tuesday 3:30:00 PM 5:00:00 PM
Simulation for Healthcare Planning in Social Networks

Chair: David Ferrin (FDI-Simulation)

Probabilistic Population Projection with James II
Christina Bohk, Roland Ewald, and Adelinde M. Uhrmacher (University of Rostock)

Predicting future populations and their structure is a central theme in demography. It is related to public health issues, political decision-making, or urban planning. Since these predictions are concerned with the evolution of a complex system, they exhibit a considerable uncertainty. Accounting for this inherent uncertainty is crucial for subsequent decision processes, as it reveals the range of possible outcomes and their likelihood. Consequently, probabilistic prediction approaches emerged over the past decades. This paper describes the probabilistic population projection model (PPPM), a recently developed method that allows detailed projections, but has a complex structure and requires much input data. We discuss the development of P3J, a tool that helps users in managing and executing projections and is built on top of the simulation system JAMES II. We outline how even specific tools like P3J profit from general-purpose simulation frameworks like JAMES II, and illustrate its usage by a simple example.

Modeling Interaction Between Individuals, Social Networks and Public Policy to Support Public Health Epidemiology
Keith Bisset, Xizhou Feng, Madhav Marathe, and Shrirang Yardi (Virginia Tech)

Human behavior, social networks, and civil infrastructure are closely intertwined. Understanding their co-evolution is critical for designing public policies. Human behaviors and day-to-day activities of individuals create dense social interactions that provide a perfect fabric for fast disease propagation. Conversely, people's behavior in response to public policies and their perception of the crisis can dramatically alter normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. The basic problem can be modeled as a coupled co-evolving graph dynamical system and can also be viewed as partially observable Markov decision process. As a way to overcome the computational hurdles, we describe an High Performance Computing oriented computer simulation to study this class of problems. Our method provides a novel way to study the co-evolution of human behavior and disease dynamics in very large, realistic social networks with over 100 Million nodes and 6 Billion edges.

Simulating Health Care in Prison Systems
Stephen L. Faller III, C. Tanner Flynn, and David M. Ferrin (FDI)

Due to the increase in gang violence in today’s prisons, Corrections Departments face the challenge of keeping prison staff and inmates safe while meeting aggressive goals to provide healthcare in a timely manner. This paper discusses the approach of using simulation modeling to analyze and identify the most effective business processes to meet these goals in a complex Receiving and Release area of a State Prison.

Wednesday 8:30:00 AM 10:00:00 AM
Simulation of Decision Support Systems

Chair: Reidar Hagtvedt (University of Alberta)

Toward Simulation-Based Real-Time Decision-Support Systems for Emergency Departments
Yariv N. Marmor (Technion Israel Institute of Technology), Segev Wasserkrug, Sergey Zeltyn, Yossi Mesika, Ohad Greenshpan, and Boaz Carmeli (IBM Haifa Research Lab) and Avraham Shtub and Avishai Mandelbaum (Technion Israel Institute of Technology)

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.

Simulation Modeling Movable Hospital Assets Managed with RFID Sensors
Kemal Efe and Vijay Raghavan (University of Louisiana) and Suresh Choubey (GE Healthcare)

Discrete Event Simulation (DES) is often used to test the operational efficiency of new systems before they are used in practice. Our focus is on tracking movable assets with Radio Frequency Identification (RFID) sensors to enable efficient sharing of the assets between hospital departments. In a hospital, patients, equipment, and personnel interact in complex ways. Different types of assets require different models of usage in a simulation program. This paper presents a taxonomy for different movable assets, proposes appropriate simulation models for each asset type, and presents the results of simulating an asset tracking system for one particular movable asset type. In addition, the taxonomy in this paper can help other researchers understand the ways different assets are used, and develop software applications based on RFID sensor data.

Impacts of Radio-identification on Cryo-conservation Centers Through Simulation
Sylvain Housseman, Nabil Absi, Dominique Feillet, and Stéphane Dauzère-Pérès (Ecole des Mines de Saint-Etienne)

This paper deals with using simulation as a decision support tool for estimating the impact of RFID technologies within biological sample storage areas (called biobanks). Several indicators, including inventory reliability or human resource utilization, are compared and discussed for different scenarios of use of the technologies. A special emphasis is put on the so-called re-warehousing activity that RFID makes possible, and which consists in reassigning tubes to empty places when box are emptied. Optimization algorithms are developed and embedded in the simulator. Results demonstrate the potential interest of RFID in biobanks and the value of simulation for estimating and optimizing such complex socio-technical systems.

Wednesday 10:30:00 AM 12:00:00 PM
Modeling Healthcare Support Systems

Chair: Lars Mönch (University of Hagen)

Dynamic, Fuzzy Simulation Model for Reproduction of Mortality Curves
Andréia Alves dos Santos Schwaab and Paulo José de Freitas Filho (Federal University of Santa Catarina)

This paper presents the study and expansion of a dynamic simulation model for aging and death (Hargrove 1998), which contemplates the representation of physiological capacity and the generation of events of risk in the life of an individual. The study identified the most influential parameters in the results of the simulations and a health impact and recovery module was included. The simulation model incorporates a fuzzy module to treat uncertainty. The expansion conducted allowed adapting the results of the simulation to real mortality curves. The reproduction of mortality curves allowed the study of populations with similar characteristics as well as the factors that could influence their development. This is interesting principally because it is possible to calibrate parameters with risk values for diseases that have high associated costs for both public and private health plans.

A Simulation Model of HIV Treatment Under Drug Scarcity Constraints
Robert Koppenhaver (University of Pittsburgh)

We consider a population of HIV infected patients. In resource poor environments, decision makers must allocate antiretroviral drugs (ARVs) to patients in need of them the most. Further complicating matters is that once a patient is given ARVs, the decision maker must decide when to deny further access to ARVs. We compare various methods for determining which patients should receive ARVs and when to switch a patient off of ARVs. We examine the World Health Organization's (WHO) treatment recommendations and how the level of drug shortages can influence the performance of these recommendations. Instead of a single recommendation, the WHO offers three distinct treatment policies with no mention of when to use them. We find that the severity of drug shortages can greatly impact the performance of these policies and the performance gap can be as high as 1.4 years.

Controlling Direct-To-Consumer Advertising, Professional Promotion and The Price of Pharmaceutical Drugs
George W. Pasdirtz (University of Wisconsin-Madison)

Direct-to-consumer (DTC) advertising is the most visible and controversial part of contact between patients and the pharmaceutical manufacturers but it is only part of the current promotional mix. Advertising in medical journals, detailing and distribution of free samples are all used along with DTC advertising to induce product demand. In this paper, I exploit a new, detailed data set on pharmaceutical promotion to estimate two state space models: one model explains the dynamics of pharmaceutical promotion and another investigates the impact of pharmaceutical promotion on the market for pharmaceutical products. Simulation results suggest that limitations on professional detailing and free samples (not DTC advertising) could reduce cyclical instability of pharmaceutical promotion. However, the pharmaceutical market does not behave as a competitive market. As a result, promotional controls will reduce sales but not prices. Market failure suggests a range of interventions that might be applied to pharmaceutical pricing.