WSC 2008

WSC 2008 Final Abstracts

Health Care Track

Monday 10:30:00 AM 12:00:00 PM
Health Systems Dynamics

Chair: Ron Giachetti (Florida International University)

A Simulation Study of Interventions to Reduce Appointment Lead-Time and Patient No-Show Rate
Ronald Giachetti (Florida International University)

A problem in health care is the lengthy waiting time for patients to receive an appointment. Long appointment delays cause patient dissatisfaction with the health care clinic and also has clinical ramifications. Long appointment delays are also found to increase patient no-shows, which further wastes medical resources and leads to a decrease in clinical care. A model of the health care clinic is built to understand the casual relationships in the system contributing to the problem. The model is used to investigate two possible policies. A policy of eliminating multiple appointment types can be effective in reducing appointment delay and as a consequent no-shows. Using data from several clinics, our study also suggests that an effective policy is to segregate habitual no-show patients and double-book them whenever they make appointments. This policy is equally effective as general over-booking without penalizing the entire patient population.

Applicability of Hybrid Simulation to Different Modes of Governance in UK Healthcare
Kirandeep Chahal (Brunel University) and Tillal Eldabi (Brunel Business School)

Healthcare organizations exhibit both detailed and dynamic complexity. Effective and sustainable decision-making in healthcare requires tools that can comprehend this complexity. Discrete event simulation (DES) due to its ability to capture detail complexity is widely used for operational decision making. However at the strategic level, System Dynamics (SD) with its focus on a holistic perspective and its ability to comprehend dynamic complexity has advantages over DES. Appreciating the complexity of healthcare, the authors have proposed the use of hybrid simulation in healthcare. As argued previously, effective decision making require tools which are capable of comprehending both detail and dynamic interactions of healthcare. The interactions in the organizations are governed by the governance design. In appreciation of that argument the authors have described the applicability of a hybrid approach to various modes of governance in UK healthcare.

System Dynamics: What's in it for Healthcare Simulation Modelers
Sally C Brailsford (University of Southampton)

In the past decade there has been an explosion in the use of system dynamics modeling in healthcare. Despite this, the approach is still far less well known than discrete-event simulation in the mainstream Operations Research community. This paper contains an introduction to system dynamics, illustrated by several examples in the field of healthcare, and discusses some of the possible reasons for the growth in the popularity of this approach for healthcare modeling.

Monday 1:30:00 PM 3:00:00 PM
Improving Hospital Performance

Chair: Michael Pidd (Lancaster University)

DGHPSim: Supporting Smart Thinking to Improve Hospital Performance
Murat M Gunal and Michael Pidd (Lancaster University Management School)

DGHPSim is a suite of discrete event simulation models that enable managers and clinicians to investigate improvement scenarios for UK general hospitals. The models were developed in Micro Saint Sharp and are configured using hospital data and nationally available health episode statistics. The models can be separately but function best as a single, overall system model that allow users to develop understanding of the interaction effects of possible changes. An example of the use of DGHPSim is given, using UK NHS data, demonstrating how it can be used to investigate improvement options whilst keeping an eye on side effects.

Simulation-Based Verification of Lean Improvement for Emergency Room Process
Nancy Khurma, Gheorghe M Bacioiu, and Zbigniew J Pasek (University of Windsor)

One of the key challenges to health care access in Canadian hospitals is growing overcrowding of the Emergency Departments (EDs), leading to the medical personnel overload, and the excessive waiting times to receive proper care. These adverse effects directly impact the patient satisfaction levels, the ability of the medical professionals to attend promptly to patients’ health issues, and generate unnecessary costs. Addressing the sources of waste and improving the process provides better care and higher patient satisfaction, as well as increases operational efficiency and the ability of the medical professionals to intervene on time. This paper describes an effort aimed at improvement of patients’ experience over their ED stay. A combination of Lean tools were used to analyze, assess and improve the current situation. Simulation models based on current and future (desired) states were developed. Comparative analysis of both enabled verification of feasibility of proposed solutions, and provided quantifiable results.

Optimizing Staffing Schedule in Light of Patient Satisfaction for the Whole Outpatient Hospital Ward
Soemon Takakuwa (Nagoya University) and Athula Wijewickrama (University of Sri Jayewardenepura)

The waiting time for patients in outpatient departments of hospitals is a problem throughout the world. In this context, a discrete-event-simulation model was developed to examine congestions and doctor schedules in all departments of an outpatient hospital ward of the Nagoya University hospital. The method of gathering the required data on times for all outpatients and their routes is described in this paper as part of a performing simulation, especially by making use of electronic medical records. This study identified some of the best doctor schedule mixes by integrating the simulation model into an optimization program in order to reduce patient waiting time as well as doctor idle-time without adding a single additional resource.

Monday 3:30:00 PM 5:00:00 PM
Simulating the Emergency Department

Chair: Mark Lawley (Purdue University)

Modelling Patient Arrivals When Simulating an Accident and Emergency Unit
Le Yin Meng (Mount Elizabeth Hospital) and Trevor Spedding (University of Wollongong)

This paper presents a case study of a discrete event simulation model of an Accident and Emergency Unit in a hospital in the UK. The objective of the study is to create a simulation study of the A&E Unit, to evaluate alternative scenarios and hence reducing patient waiting time. The case study uses a novel approach to predict the arrival time of patients and hence results in a more realistic platform on which to base the subsequent scenario analysis. The scenario analysis illustrates that significant reductions in the waiting time of patients can be obtained by relatively minor changes in operations.

Reducing Emergency Department Overcrowding – Five Patient Buffer Concepts in Comparison
Erik Michael Wilhelm Kolb (RWTH Aachen University), Jordan Peck (MIT Parc Center for Complex Systems), Sebastian Schoening (MAG Europe GmbH) and Taesik Lee (Complex System Design Lab)

Emergency Department (ED) overcrowding is a common medical care issue in the United States and other developed nations. One major cause of ED crowding are holding patients waiting in the Emergency Room (ER) for inpatient unit admission where they block critical ED resources. With input data from a hospital in Massachusetts/USA, we tested five patient buffer concepts which aim at relieving pressure of the ER. The buffers are also assumed to improve patient and staff satisfaction through their design tailored to needs in patient flow. To ensure patients safety, we performed tests with discrete event simulation in which we discovered ‘triage to bed time’ reductions of up to 22% and ‘diversion hour’ decreases of up to 24%. All buffers managed to run with significantly less resources than the ER. Our findings have a potential impact on hospital process flow due to clear results which offer substantial improvement of hospital organization.

Improving Patient Flow in a Hospital Emergency Department
D. J. Medeiros (Penn State University), Eric Swenson (United States Military Academy at West Point) and Christopher DeFlitch (Penn State Hershey Medical Center)

Hospital emergency departments in the US are facing increasing challenges due to growth in patient demand for their services, and inability to increase capacity to match demand. We report on a new approach to patient flow in emergency departments, and a simulation model of the approach. Initial results from the model show that the approach is feasible, and a pilot study demonstrates substantial improvements in patient care.

Tuesday 8:30:00 AM 10:00:00 AM
Hospital Operations Simulation

Chair: Julie Ivy (North Carolina State University)

A Simulation-Based Approach for Inventory Modeling of Perishable Pharmaceuticals
Ana R. Vila-Parrish, Julie Simmons Ivy, and Russell E. King (North Carolina State University)

Pharmaceutical expenditures are increasing for hospital systems nationwide. We model the inventory and ordering policies for perishable drugs in the setting of an inpatient hospital pharmacy. We consider two stages of inventory: raw material and finished good (e.g. intravenous). We use a two-phased approach to explore policy structures that could be implemented in the hospital pharmacy. We develop a policy which is based on the idea that hospitals can improve both costs and patient demand fulfillment by using knowledge of patient mix to guide their drug inventory and preparation decisions. We compare this policy to a simpler stationary base stock policy. The policies are evaluated on the basis of (1) shortage cost, (2) outdating cost (expirations), and (3) holding cost through a range of cost scenarios.

Simulation Based Decision-Making for Hospital Pharmacy Management
Alkin Yurtkuran and Erdal Emel (Uludag University)

Managing healthcare delivery systems plays an important role for healthcare providers in order to have high quality service performances. Inpatient pharmacy delivery systems are one of those that have a key role in hospital’s service quality. Simulation is the best tool to analyze the hospital pharmacy operations due to their inherent complexity. In this article, a simulation model is developed based on data collected from a hospital in Turkey to analyze its pharmacy delivery system. In comparison to the baseline system, two different scenarios with varying factors are investigated, seeking to minimize drug delivery time to patients. The results presented here indicate the possibility for improved system performance.

Using Simulation in the Implementation of an Outpatient Procedure Center
Todd Huschka (Mayo Clinic Rochester), Brian Denton (North Carolina State University) and Bradly Narr and Adam Thompson (Mayo Clinic Rochester)

Creation of an Outpatient Procedure Center (OPC) is a complicated endeavor, requiring a detailed understanding of the resources available and the procedures to be performed. Miscalculation of resource allocation or patient flow through the area can result in the waste of expensive resources, patient dissatisfaction, and health care provider inefficiency. The use of discrete event simulation can assist in the design of an OPC with the ultimate goal of reducing resource waste and improving patient flow through the system. In this article we provide a case study of the application of a discrete event simulation model used to support analysis required for moving an existing group (interventional procedures for Pain Medicine) into a new area. This resulted in major changes to the group’s practice and modified the new facility utilization.

Tuesday 10:30:00 AM 12:00:00 PM
Outpatient Clinic Operations

Chair: Todd Huschka (Mayo Clinic)

A Simulation Study on the Impact of Physician Starting Inquiry Time in a Physical Examination Service
Wheyming Tina Song (National Ting Hua University), Aaron E Bair (Department of Emergency Medicine) and Mingchang Chih (National Ting Hua University)

The objective of our project was to improve the efficiency of a screening physical examination service of a large hospital system. We began with a detailed simulation model to explore the relationships between four performance measures and three decision factors. These included various dispatching rules, physician starting inquiry time, and scheduled patient arrival time. We then attempted to identify the optimal physician starting inquiry time. Our simulations show that (1) the three patient dispatching rules have negligible influence on any of the four outcome measures; (2) two types of patient arrival policies did not affect any of the four measures; (3) the proposed optimal physician starting inquiry time decreased patient wait time by 50% without increasing overall physician utilization. Based on these finding, we propose an innovative change to postpone or remove the physician inquiry stage which promises to reduce clinic resources and increase patient service quality.

Outpatient Appointment Scheduling in a Multi Facility System
Athula Wijewickrama (University of Sri Jayewardenepura) and Soemon Takakuwa (Nagoya University)

This study evaluates appointment systems used in hospitals by incorporating appointment rules and patient characteristics. Using an experiment unit at an internal medicine department of a large outpatient ward in Nagoya university hospital, a number of prevailing assumptions were relaxed, and twentyfive appointment systems were developed combining five appointment rules with five patient sequences. These appointment systems were evaluated under two different environments namely no-show and patient punctuality, with each of the two-levels totaling one hundred different environments. A best appointment system is capable of identifying the problems in terms of both patient waiting time and doctor idle time.

A Simulator to Improve Waiting Times at a Medical Imaging Center
Francisco J. Ramis (Universidad del Bio-Bio), Liliana P. Neriz (Universidad de Chile), Jose Sepulveda (University of Central Florida) and Felipe Baesler (Universidad del Bio-Bio)

Medical Imaging Centers (MIC) are critical units in every hospital or medical center because they are an important step in generating a patient’s diagnostic. This paper shows a simulator designed to facilitate the development of simulation studies of MIC, which departs from traditional modeling because it uses a pull paradigm for the patients. A group technology approach was used to minimize the number of objects in the simulator, which includes objects that have the functionality of the equipments and processes found in these facilities, so that the analyst only needs to provide the parameters of the center. All the data is provided to the simulator through a Graphic User Interface (GUI), requiring no programming capacities. As an application of the simulator, an example is provided where the simulator is used to improve the waiting times and equipment rate of utilization at a research hospital in Chile.

Tuesday 1:30:00 PM 3:00:00 PM
Analysis of Disease Control Policies

Chair: Stephen Roberts (North Carolina State University)

Infectious Disease Control Policy
Margaret L Brandeau (Stanford University)

Control of infectious diseases is a key global health priority. This paper describes the role that simulation can play in evaluating policies for infectious disease control. We describe ongoing simulation studies in three different areas: HIV prevention and treatment, contact tracing, and hepatitis B prevention and control.

Pandemic Influenza Response
Ali Ekici, Pinar Keskinocak, Julie L. Swann, and Randeep Ramamurthy (Georgia Institute of Technology)

Given the recent incidents of the avian flu in Asia and the pandemic influenza cases in history, many experts believe that a pandemic influenza is likely to happen in the near future; hence, governments and non-governmental organizations try to develop response plans. It is estimated that 20% of working adults may become ill, and there may be a 40% workforce loss during peak because of illness, fear of infection, and the need to care infected family members or school-aged children. Food and water supplies and transportation services may be interrupted. To aid with planning, we model the spread of pandemic influenza, both geographically and over time, using an agent-based simulation approach. We then combine this with an optimization model to identify and dynamically update the appropriate locations for food distribution facilities, and test our models using data from Georgia.

Parallel Simulation of the Global Epidemiology of Avian Influenza
Dhananjai M. Rao (Miami University) and Alexander Chernyakhovsky (William Mason High School)

SEARUMS is an Eco-modeling, bio-simulation, and analysis environment to study the global epidemiology of Avian Influenza. Originally developed in Java, SEARUMS enables comprehensive epidemiological analysis, forecast epicenters, and time lines of epidemics for prophylaxis; thereby mitigating disease outbreaks. However, SEARUMS-based simulations were time consuming due to the size and complexity of the models. In an endeavor to reduce time for simulation, we have redesigned the infrastructure of SEARUMS to operate as a Time Warp synchronized, parallel and distributed simulation. This paper presents our parallelization efforts along with empirical evaluation of various design alternatives that were explored to identify the ideal parallel simulation configuration. Our experiments indicate that the redesigned environment called SEARUMS++ achieves good scalability and performance, thus meeting a mission-critical objective.

Tuesday 3:30:00 PM 5:00:00 PM
Surgery Systems

Chair: Evelyn Brown (East Carolina University)

Heuristics for Balancing Operating Room and Post-Anesthesia Resources Under Uncertainty
Jill Howard Iser, Brian T. Denton, and Russell E. King (North Carolina State University)

The Post-Anesthesia Care Unit (PACU) is a shared resource in the hospital where patients recover from surgery. It is fed by a set of Operating Rooms (OR’s) often spanning several surgical services. It is insufficient to determine the best surgery schedule for any single OR without considering available PACU capacity. We model this as a two-stage process where the first stage is surgery and the second, post-anesthesia recovery. An interesting aspect of the second-stage process is that it begins as soon as the first stage has concluded even if a PACU bed is not available. In this case, the OR continues to house the recovering patient until a PACU bed is available. We analyze the structure of the problem, evaluate several heuristics based on competing performance measures for surgical suite efficiency, and present results of numerical experiments and insights that can be derived from them.

Maximizing the Utilization of Operating Rooms with Stochastic Times Using Simulation
Jean-Paul Arnaout (Lebanese American University)

This paper addresses a surgery rooms scheduling problem. The problem is modeled as a parallel machine scheduling problem with sequence dependent setup times and an objective of minimizing the makespan. This is a NP-hard problem and in this paper, a solution heuristic is developed and compared to existing ones using simulation. The results and analysis obtained from the computational experiments proved the superiority of the proposed algorithm LEPST over the other algorithms presented.

Wednesday 8:30:00 AM 10:00:00 AM
Improving Healthcare Outcomes

Chair: Sallie Brailsford (University of Southampton)

How Much is a Health Insurer Willing to Pay for Colorectal Cancer Screening Tests?
Reza Yaesoubi and Stephen D. Roberts (North Carolina State University)

Colorectal Cancer (CRC) screening tests have proven to be cost-effective in preventing cancer incidence. Yet, as recent studies have shown, CRC screening tests are noticeably underutilized. Among the factors influencing CRC screening test utilization, the role of health insurers has gained considerable attention in recent studies. In this paper, we propose an analytical model for the market of CRC screening tests and show how the insurer can benefit from a computer simulation model to cope with the problem of incomplete and asymmetric information inherent in this market. Our estimates reveal that promoting CRC screening tests is not necessarily economically attractive to the insurer, unless the insurer’s valuation of life is greater than a certain limit. We use the proposed model to estimate such a threshold – the insurer’s willingness-to-pay to acquire one additional life year by covering the CRC screening tests.

Discrete Event Simulation: Optimizing Patient Flow and Redesign in a Replacement Facility
Marshall Ashby, Martin Miller, and David Ferrin (FDI) and Niloo Shahi (LAC+USC Healthcare Network)

This study observed the challenges of taking an existing facility’s inpatient volumes and procedures and projecting them into a replacement facility with differently sized units, overall scale, and layout. Discrete event simulation is used to examine the impacts of this transition as well as the operational impacts of capacity changes, process redesign, and process improvements. This effort to optimize patient flow throughout the inpatient units is done while modeling and observing the impacts on other interdependent parts of the hospital such as the Emergency Department, and Operating Rooms.

Allocating Outpatient Clinic Services Using Simulation and Linear Programming
David Ferrin (FDI Simulation)

A large number of operational tools exist to help researchers determine business solutions for their customers. Each individual tool serves a distinct purpose for specific types of problems. Deciding which tool to use requires knowledge and experience. Sometimes, the researcher should integrate several tools because each tool may get too complex or not form a complete solution. This paper discusses how simulation, linear programming and spreadsheet analysis were integrated to help a new hospital determine ideal space assignments, schedule configurations and throughput targets for numerous clinic services.