WSC 2006 Abstracts

Simulation of a Multiple Operating Room Surgical Suite
Brian T Denton, Ahmed S. Rahman, Heidi Nelson, and Angela C. Bailey (Mayo Clinic)

Abstract:
Outpatient surgery scheduling involves the coordination of several activities in an uncertain environment. Due to the very customized nature of surgical procedures there is significant uncertainty in the duration of activities related to the intake process, surgical procedure, and recovery process. Furthermore, there are multiple criteria which must be traded off when considering how to schedule surgical procedures including patient waiting, operating room (OR) team waiting, OR idling, and overtime for the surgical suite. Uncertainty combined with the need to tradeoff many criteria makes scheduling a complex task for OR managers. In this article we present a simulation model for a multiple OR surgical suite, describe some of the scheduling challenges, and illustrate how the model can be used as a decisions aid to improve strategic and operational decision making relating to the delivery of surgical services.

Simulation Analysis of an Outpatient Department of Internal Medicine in a University Hospital
Apputantiri Kankanamge Athula Wijewickrama (University of Sri Jayewardenepura) and Soemon Takakuwa (Nagoya University)

Abstract:
Soaring health care costs and greater emphasis on preventative medicine have compelled researchers to examine new ways to reduce costs and improve efficiency in outpatient services. Extended waiting times for treatment in the outpatient department followed by short consultations has long been a complaint of patients. This issue is becoming increasingly important in Japan with its progressively aging society. In this context, a discrete event simulation model was developed to examine doctor schedule mixes (DSMs) and various appointment schedules (ASs) in a mixed-patient type environment in an outpatient department of internal medicine of a university hospital. It could identify some of the best DSMs by integrating a simulation model into an optimization program. Combining one DSM found via an optimization program with some ASs, the patient waiting time could be reduced drastically without adding extra resources.

The Use of Simulation to Determine Maximum Capacity in the Surgical Suite Operating Room
Sarah M. Ballard and Michael E. Kuhl (Rochester Institute of Technology)

Abstract:
Utilizing ambulatory care units at optimal levels has become increasingly important to hospitals from both service and business perspectives. With the inherent variation in hospitals due to unique procedures and patients, performing capacity analysis through analytical models is difficult without making simplifying assumptions. Many hospitals calculate efficiency by comparing total operating room minutes available to total operating minutes used. This metric both fails to account for the required non-value added tasks between surgeries and the delicate balance necessary between having patients ready for surgery when an operating room becomes available, which can result in increased waiting times, and maximizing patient satisfaction. We present a general methodology for determining the maximum capacity within a surgical suite through the use of a discrete-event simulation model. This research is based on an actual hospital concerned with doctor/resource acquisition decisions, patient satisfaction improvements, and increased productivity.

Modeling Emergency Care in Hospitals: A Paradox - The Patient Should Not Drive the Process
Andrew Hay (Hospital Navigator) and Edwin Valentin and Rienk Ate Bijlsma (Systems Navigator)

Abstract:
The objective in the creation of domain specific discrete event simulation environments is to facilitate model development in the chosen domain. In the creation of such environments, there has been a tendency to adopt a factory based world view. In this paper, we describe an approach to the creation of a generic modeling environment in the healthcare domain that breaks away from the conventional entity driven request for resource. Our approach has enabled us to create models of emergency care in four UK NHS hospitals that reflect more realistically the way emergency care is actually delivered. It appears, paradoxically, that in simulating emergency care, it is best if the patient does not come first.

Understanding Accident and Emergency Department Performance Using Simulation
Murat M Gunal and Michael Pidd (Lancaster University)

Abstract:
As part of a larger project examining the effect of performance targets on UK hospitals, we present a simulation of an Accident and Emergency (A&E) Department. Performance targets are an important part of the National Health Service (NHS) performance assessment regime in the UK. Pressures on A&Es force the medical staff to take actions meeting these targets with limited resources. We used simulation modelling to help understand the factors affecting this performance. We utilized real data from patient admission system of an A&E and presented some data analysis. Our particular focuses are the multitasking behaviour and experience level of medical staff, both of which affect A&E performance. This performance affects, in turn, the overall performance of the hospital of which it is part.

Simulation Model for Improving the Operation of the Emergency Department of Special Health Care
Toni Petteri Ruohonen (University of Jyväskylä)

Abstract:
This paper presents a simulation model which describes the operations in the Emergency Department of Special Health Care at the Central Hospital of Jyväskylä, Finland. It can be used to test different process scenarios, allocate resources and perform activity based cost analysis. By using the simulation model we demonstrate a new operational method, which makes the operation of the Emergency Department of Special Health Care more effective. This operational method is called the triage-team method and it has been studied from two different points of view. The results showed that this method improves the operation of the Emergency Department of Special Health Care substantially (over 25 %), if it is implemented properly and includes all the necessary tasks.

Modeling Tuberculosis in Areas of High HIV Prevalence
Georgina Rosalyn Hughes and Christine Susan Mary Currie (University of Southampton) and Elizabeth Corbett (London School of Hygiene and Tropical Medicine)

Abstract:
We describe a discrete event simulation model of tuberculosis (TB) and HIV disease, parameterized to describe the dual epidemics in Harare, Zimbabwe. TB and HIV are the leading causes of death from infectious disease among adults worldwide and the number of TB cases has risen significantly since the start of the HIV epidemic, particularly in Sub-Saharan Africa, where the HIV epidemic is most severe. There is a need to devise new strategies for TB control in countries with a high prevalence of HIV. This model has been designed to investigate strategies for reducing TB transmission by more efficient TB case detection. The model structure and its validation are discussed.

Incorporating Human Behavior in Healthcare Simulation Models
Sally Brailsford, Jennifer Sykes, and Paul Harper (University of Southampton)

Abstract:
For many years, simulation has been used to evaluate the outcomes from medical interventions designed to improve patients' health. However in practice these outcomes can be greatly affected by patient behavior. For example, patients may not complete a course of a prescribed medication because they find the side-effects unpleasant. A study designed to evaluate this medication which ignores such behavioral factors may give unreliable results. In this paper we discuss some of the issues involved in incorporating human factors in simulation models, and we describe two models for screening for different diseases which have attempted to include behavioral factors.

Use of Simulation to Determine Resource Requirements for End-stage Renal Failure
Ruth Davies (University of Warwick)

Abstract:
All Western countries are taking increasing numbers of patients onto their renal programs. Physicians now accept older and sicker patients than they would have done in the past. A discrete event simulation describes patient arrivals and the transfer between different modalities of treatment for different age and risk groups in order to project future demands for treatment. At the national level the only significant uncertainty arises from the expected number of new patients, which is on an upward trajectory and has not yet reached the level of most other European countries or the USA. At a local level the uncertainties are much greater because of the inherent randomness in smaller populations. In these smaller populations, simpler modeling methods that only take account of new arrivals, transplant and death rates may be equally valuable, providing the standard deviations of the estimates can be calculated.