Task Network Model of an Ambulance Dispatch Center1
Task Network Model of an Ambulance Dispatch Center
(Micro Saint implementation)
Master Thesis
Institute for Experimental and Work Psychology
University of Groningen, The Netherlands
by
Richard J.C. Vos
October 23, 2001
Richard J.C. Vos
Kornalijnlaan 196
9743 JR Groningen
The Netherlands
Student number: 0793787 (Functieleer)
Supervision: dr ir. L.J.M. Mulder, drs. R.J. van Ouwerkerk, and drs. J. Bos
Task Network Model of an Ambulance Dispatch Center
(Micro Saint implementation)
Master Thesis
submitted to the Department of Psychology in Partial Fulfillment of the Requirements for the Degree of Master of Science in Psychology at the Institute for Experimental and Work Psychology, University of Groningen, The Netherlands.
by
Richard J.C. Vos
October 23, 2001
Richard J.C. Vos
Kornalijnlaan 196
9743 JR Groningen
The Netherlands
E-mail:
Internet:
Student number: 0793787 (Functieleer)
Supervision: dr ir. L.J.M. Mulder, drs. R.J. van Ouwerkerk, and drs. J. Bos
Copyright © 2001 Richard J.C. Vos. All rights reserved.
The author hereby grants to University of Groningen permission to reproduce and distribute publicly paper and electronic copies of this thesis and to grant others the right to do so.
Table of Contents
Table of Contents......
Preface......
Abbreviations......
Acknowledgements......
Abstract......
Conclusion......
1.Overview......
1.1Introduction......
1.2General context......
1.2.1Social and scientific significance......
1.2.2Workplace for Analysis of Task Performance......
1.3General definitions......
1.3.1System and model classification......
1.3.2Simulation......
1.3.3Scenarios......
1.4Objectives and outlines......
2.Relating model and ADC-system......
2.1Introduction......
2.2The ambulance dispatch center......
2.3The ambulance dispatch task......
2.4Call for scenarios......
2.5What can be simulated and manipulated?......
3.Towards building the ADC-model......
3.1Introduction......
3.2Model basics......
3.2.1Scenarios......
3.2.2Events......
3.2.3Length of simulation......
3.3Model assumptions and validity......
3.3.1Introduction......
3.3.2Model restrictions......
3.3.3Model assumptions......
3.4Model tuning......
3.5Layout and the role of animation......
3.6Implementation issues......
3.6.1Task configuration......
3.6.2The ADC-dispatcher......
3.6.3Time and simulation cycles......
4.Method - The ADC-model in Micro Saint......
4.1Introduction......
4.2Objectives......
4.2.1Introduction......
4.2.2Modeling the ADC-reality......
4.2.3Suitable research tool......
4.2.4Configuration options......
4.3Task network modeling......
4.3.1Introduction......
4.3.2Simulation software environment: Micro Saint - version and classification......
4.4Functional description of the ADC task network model......
4.4.1Schemes and model structure......
4.4.2Relevant objects and their properties......
4.4.3Functionality......
4.4.4Variables......
4.4.5Defining tag structure......
4.4.6Dealing with events: the timetable......
4.4.7Dealing with events: generating events......
5.Simulation procedures......
5.1Introduction......
5.2The length of the simulation run(s)......
5.3The number of independent simulation run(s)......
5.4Initial conditions for the simulation run(s)......
5.4.1Halting the model......
5.4.2Setting ambulance speed and tuning duration of rides......
5.4.3Setting the duration of dispatch actions......
5.4.4Setting the covering indicators......
5.4.5Adjusting the timetable of transports......
5.4.6Forcing a patient stop......
5.4.7Setting the dispatcher's planning ahead time......
5.5Performance monitoring and output......
5.6Scenario types and their short descriptions......
6.Results......
6.1Introduction......
6.2'Ordinary day' experiment specification......
6.3'Ordinary day' simulation results......
6.3.1Frequencies of events......
6.3.2Covering......
6.3.3Task parameters......
6.3.4A1-exceedings......
6.3.5Duration of ambulance rides......
7.Discussion......
7.1Introduction......
7.2ADC-model versus ADC-system......
7.2.1Introduction......
7.2.2Frequencies of events......
7.2.3Covering......
7.2.4Ambulance speed and duration of rides......
7.3The ADC-model as a research tool......
7.4Future steps......
7.4.1Introduction......
7.4.2Annual statistics......
7.4.3Population density and the stream of transports......
7.4.4Dispatcher vigilance: on the circadian rhythm and fatigue......
7.4.5Dispatcher task......
7.4.6Ambulance speed, movement, and duration of ambulance rides......
7.4.7Towards covering data per location......
7.5ADC-model usability......
7.5.1Research tool......
7.5.2Coupling using DWAT-Simulator......
7.5.3Education......
7.5.4Related questions that could benefit from scenarios......
7.6In conclusion......
Appendix A.Micro Saint Version 3.0......
Advantages......
Disadvantages......
Bugs and peculiarities......
Time and simulation cycles......
Data collection......
Animation......
Appendix B.The ADC-model......
Relevant objects and their properties......
Functionality......
Function Library and Variable Catalog......
Miscellaneous items......
Tag structure......
Input parameters......
Icons......
ActionViews......
Posts, ambulances, cities, and covering regions......
Adjusting ADC-objects......
References......
Preface
When people think of a model, they may come up with memories of miniatures, for instance a car model. Such a scale model is often an imitation of a real-world car. We may need to make imitations of real-world systems in science in order to study them. Imitations like these – or models – are made up of a number of assumptions about the working of such a system. During training practice, I have built a model of an ambulance dispatch center (ADC). While working on that project, two major accidents happened in The Netherlands: Enschede[1] and Volendam[2]. Both disasters dramatically illustrate the common interest of optimal functioning dispatch centers, and underscore the relevance to research such systems. The current coverage describes the assumptions, characteristics, and behavior of the ADC-model.
Abbreviations
- ADC - Stands for Ambulance Dispatch Center. In this thesis, ADC refers to the ambulance dispatch center or central post located in Groningen-city. This is also the case when it is used as a suffix in ADC-task, ADC-model, ADC-system, ADC-simulation, and so forth. It must be clear that the ADC is unable to perform without active involvement of persons and systems in its region. Terms like ADC-model and ADC-system cover this interaction as well.
- CAD - Computer Aided Dispatch system.
- CPA - Central Post Ambulancetransport (in Dutch: "Centrale Post Ambulancevervoer" or "Meldkamer Ambulancezorg"). This is the central post or communications center within the province of Regional Ambulance Services Groningen. The CPA in Groningen-city is referred to as ADC in this study.
- DWAT - Stands for Workplace for Analysis of Task Performance (also: Digital Workplace). The Digital Workplace is located at the University of Groningen, department of Experimental and Work Psychology. Researchers can conduct experimental studies in this realistic simulated (cognitive) task environment.
- EMS - Emergency Medical Services.
- GIS/GPS - Geographic Information System / Global Positioning System are communication and identification systems, which can be used to cut off the time between the emergency call and the arrival of the ambulance. Ambulances are deployed more efficient and faster using these systems. The global positions of ambulances are sent (via a mobile data network) to a central Mobile Information Server. The Geographic Information System at the CPA uses these data to display the positions of ambulances at the map. Furthermore, this system calculates which ambulance can be at the incident location within the smallest amount of time, and thus supports the dispatcher in the selection of ambulances. Additional advantages: GPS supports an active route accompanying; information about patient and incident can be sent to the ambulance via mobile data network; departure and arrival times are collected. (Summarized from: Personal Finance Online, 2001). GIS/GPS is introduced at the ADC in 2001.
- MAB - It stands for Ambulance and Fire Dispatch Center (in Dutch: "Meldkamer Ambulancezorg & Brandweer"). This is an integrated center for ambulance and fire dispatches. The central post in Groningen-city is also such an integrated center (with separate dispatchers for ambulance and fire dispatches).
- RAV - Stands for Regional Ambulance Services (in Dutch: "Regionale Ambulance Voorziening"). RAV-Groningen was established in 1998 (Jaarverslag 1998, 1998). In Groningen, RAV-management is formed by ambulance services, the Central Post Ambulancetransport (CPA), the hospitals, and the General Practioners (Gedeputeerde Staten der provincie Groningen, 2000). The RAV is responsible for the ambulance transports within the RAV-province. The Netherlands is split into 26 RAV-provinces. RAV-Groningen (also RAVG) is counted among the largest provinces: it coincides with the Dutch province Groningen.
- SSL - stands for Scenario Specification Language, which is a script language. The simulator of the Digital Workplace has a built-in scenario specification language. Its purpose is to describe scenarios that contain the complete description, and course of an experiment within the Digital Workplace. Moreover, this scenario may contain an ADC-scenario.
Acknowledgements
In the course of building the ADC-model and writing my thesis, I felt greatly supported by several people. My appreciation goes out especially to the following persons. In the first place L.J.M. Mulder and R.J. van Ouwerkerk (researchers at the Digital Workplace, department of Experimental and Work Psychology, Groningen) who have been quite valuable. They allowed for discussions on current and upcoming developments. Also, they provided me with expertise, comments, and advice. I really appreciated their ongoing attentiveness and guidance. I would also like to thank two other researchers at the Digital Workplace, J. Bos for supporting me fighting some peculiarities of Micro Saint, and L. Quispel for his commendable attitude towards students, but most of all for their humor and the time we spent together. Further, thanks go to T. Snijders (professor of Stochastic Models for the Social and Behavioral Sciences, Groningen) for being there when I needed advice regarding the distribution used in the model; R.F.Th. Vos for proofreading draft versions; J.C. Meijer for deepening my knowledge of style processing. Here I also want to remember late G. Mulder (professor of Experimental Psychology, Groningen) for his encouraging enthusiasm, knowledge, and friendliness.
Personal thanks go to the Lord, my parents, and in-laws for their love and support.
I wish to dedicate this thesis to my family: I love you lots, Haike and Simcha.
Richard J.C. Vos
Groningen, October 23, 2001
Abstract
In complex and dynamic tasks, the efficiency of task performance is largely depending on the overview of the task. Research of these tasks must address issues regarding workload, planning and fatigue, situational awareness, etc. In a recent approach to research such complex dynamic tasks, an ambulance dispatch task was implemented in a simulation environment. The experiments in this environment are based on task scenarios that contain the description and course of the experiments. The scenarios can be tailored to fit the specific needs of the researcher. To be able to define realistic scenarios, a realistic model of the ambulance dispatching system is a necessity. Moreover, a model that can be configured to fit the query of the experimenter will become a valuable research tool. In the current study, a task network model of the ambulance dispatch task is designed and implemented in Micro Saint to be supportive in designing tasks / scenarios suitable for the ambulance dispatching simulation environment.
Conclusion
The current thesis presents a working model of an ambulance dispatch center. By now, we have a better understanding which objects, functionality, and cognitive parameters are essential and how they influence each other. The dispatch task itself is characterized as a complex dynamic task. Dispatch task performance involves aspects of communication, strategy, individual differences, and situational awareness.
The ADC-model is able to generate a wide range of specific task scenarios to study these aspects. The model running with default settings results in an 'ordinary day' scenario. However, the researcher can set a number of configuration parameters to match the model's behavior with the experimental question. Furthermore, the model supports the researcher in assessing the difficulty of task scenarios based on a number of cognitive task parameters.
Keywords: Task network model, ambulance dispatch center, Micro Saint, modeling, simulation, behavior of human-system.
Task Network Model of an Ambulance Dispatch Center1
1.Overview
1.1Introduction
Effectiveness of performance in complex dynamic tasks depends heavily on the understanding, planning and organization of behavior (Bainbridge, 1998; Vincente, 1999).
The information era made its impact especially on working society. Workplaces have become lookalikes of each other more and more. Ergonomic shaped desks are accommodated with PC's to interact with information available all over the world. Superficially, there is not that much difference between a dairy farmer monitoring and interacting with automatic milking machines, and an EMS[3] dispatcher monitoring and guaranteeing the covering of ambulances in the region. Both involve processes that are part of information systems, although the conditions to achieve the work may differ largely. The tasks of the first job can roughly be accomplished by ('passively') responding to stimulus changes. The tasks can be handled by applying simple routines, because they are not interdependent. This contrasts strongly with the latter job, whereby efficiency of task performance is largely depending on the overview of the task. Understanding the present and future situation is required to cope with the complexity and dynamics of the job. An adequate mental model is needed to accomplish tasks like this. A mental model is basically a temporary structure of inference; therefore, it has to be updated continuously. It will help the task executor to determine the focus of attention, and eventually to make appropriate decisions. Cognitive goals required by the task guide the process of keeping this structure reliable. Elements that serve as building blocks for such a mental model are results of previous thinking, information extracted from knowledge bases, and relevant information from the environment (Bainbridge, 1998).
Developments in content at a technical and organizational level changed the character of work, forcing a shift to great emphasis on its cognitive aspects. Numerous ways to get the job done and the possibility for the executor to choose a certain (own) completion is typical of complex (cognitive) task performance. Therefore, recent scientific research concentrated on the analysis of human task performance in complex and dynamic environments. Task analysis can help identifying the goals and tasks in such particular domain. Task analyses according to a classic approach - using a normative or descriptive model - do not meet the requirements needed when applied to several types of mental work. The formative model, a recent approach (Vincente, 1999), specifies constraints at a technical and organizational level, which must be satisfied before a digital information system can offer maximum space in task performance to its user. This is needed in complex and dynamic tasks, since new and unfamiliar situations may arise. The dynamics of the task require therefore flexible and adaptive behavior from the executor. The cognitive workload forces to distribute resources economically among goals and sub-goals. Decisions must be made with respect to which (sub-)goal must be reached or which (sub-)task must be carried out, taking into account results of planning and anticipation on expected future developments (Bainbridge, 1998).
The Workplace for Analysis of Task Performance (Bos, Mulder, and Ouwerkerk, 1998; Bos, Mulder, and Ouwerkerk, 2001; Brookhuis, Bos, Mulder, and Veldman, 2000) wants to deploy research on this kind of cognitively demanding tasks in which complexity and dynamics play a leading role. This Digital Workplace is an environment in which experimental and work psychological research can take place by conducting experiments in a realistic, simulated (cognitive) task environment. It offers a wide range of tools to register responses at task, physiological and behavioral level. Also, experimenters have the possibility to alter several environmental conditions of the simulated task before and even during the simulation (Bos, Mulder, and Ouwerkerk, 1998).
A real-life task that can be simulated in the Digital Workplace is the work of ambulance dispatchers. Recently a simulation of this task has been implemented. The nature of this task is complex and dynamic and shows great similarity with tasks that can be found in the domain of air traffic control. The ambulance dispatch center (from now: ADC[4]) in Groningen, The Netherlands, served as an example to achieve a high level of realism. American 911 TV-series may sometimes give the impression that ambulance dispatchers have one of the most exciting jobs in the entire world. In contrast to this, down-to-earthness characterizes the reality of the complex dispatch job performed at the ADC. Highly trained professionals do their work that includes a lot of spatial planning. They face a lot of job-related stressors, and have their brakes at the desk, because they cannot risk missing any information. They always need to be sharp, alert, controlling, checking, communicating, and planning ahead as far as reasonable (Duijndam and Messer, 1999). Although periods of high workload may occur, periods of low workload are also common. As one of the dispatchers stated: "You are waiting on a bus of which you are not sure whether it will ever arrive."
Not by accident, this dispatch task has interesting characteristics from a researcher's point of view. Among others: aspects of cognitive ergonomics (e.g., structural components of the system interface), several feasible scenarios (like ‘slippy’-days with lots of accidents), interaction with information systems, interaction with co-workers, ambulance personnel and callers, decision nodes, stress and stressors, and aspects of situational awareness.
Scenarios are key instruments to study these elements and their relations to strategy and complexity. For example, a scenario that may evoke a certain task strategy can be used to examine the usage of task strategies. Yet, experimenters need to have specific scenarios at their disposal in order to conduct their experiments in the simulation environment of the Digital Workplace. A scenario specification is the sequence of beforehand-specified time-related events[5]. Such an event in an ADC-scenario could be for instance the occurrence of an accident, at some place, at some time. The present study focuses on the specification of scenarios with regard to strategy and complexity. The scenarios need to be suitable for a realistic task setting. A realistic model of the ambulance dispatch center is therefore the starting-point in this study. The ADC-model can be used on its turn for composition of ADC-scenarios, whereby specific scenarios can be put together by configuring the ADC-model.
For example, adjusting the occurrences of emergency transportation in proportion to the number of ordered transportation would result in a more or less loaded scenario. This way, specific scenarios will contribute in giving grip to researchers when addressing all kinds of issues regarding workload, planning and fatigue, situational awareness, etc.
The need arises to put efforts in model and scenario development to gain insight in the human-system behavior.
The main objective of the present thesis is the design and implementation of a realistic model of an ambulance dispatch center (1) supportive in designing tasks / scenarios suitable for the ADC-simulation environment (2).
1.2General context
1.2.1Social and scientific significance
An optimal functioning dispatch center is in everybody's interest. Scientific research can contribute to a better understanding of the dispatch task and related cognitive aspects. The usage of scenarios offers possibilities to study the complexity and dynamics in this cognitive demanding work under certain conditions.