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Posts Tagged ‘Simulation’

Product Description
Deals with the estimation of natural resources using a Monte Carlo methodology. Includes a set of tools to describe the morphological, statistical and stereological properties of spatial random models. Discusses the geostatistical simulation techniques in such a specific way. … More >>

Geostatistical Simulation: Models and Algorithms

Product Description
The coupling of models from different physical domains and the efficient and reliable simulation of multidisciplinary problems in engineering applications are important topics for various fields of engineering, in simulation technology and in the development and analysis of numerical solvers. The volume presents advanced modelling and simulation techniques for the dynamical analysis of coupled engineering systems consisting of mechanical, electrical, hydraulic and … More >>

Simulation Techniques for Applied Dynamics

Product Description
To perform computer simulation successfully, two rather different sets of skills are required. One of these relates to programming: a simulation program should do what its author intends and do it efficiently. The other is concerned with the collection and analysis of data: statistical tools have to be used in order to obtain with a minimum of effort, accurate and reliable estimates for the desired performance measures. Dr Mitrani covers both of these aspects of the… More >>

Simulation Techniques for Discrete Event Systems

New Simulation Tool Could Shorten Manufacturing Design Process
Novel research on improving the simulation performance of hardware models created in a language called SystemC, often used to shorten manufacturing design cycles to improve the time it takes to bring a product to the marketplace, has garnered a best paper award at the 15th Asia and South Pacific Design Automation Conference (ASP-DAC) for a team led by Sandeep Shukla, Virginia Tech associate …

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This completely revised and updated edition of Applied Risk Analysis includes new case studies in modeling risk and uncertainty as well as a new risk analysis CD-ROM prepared by Dr. Mun. On the CD-ROM you’ll find his Risk Simulator and Real Options Super Lattice Solver software as well as many useful spreadsheet models. “Johnathan Mun’s book is a sparkling jewel in my finance library. Mun demonstrates a deep understanding of the underlying mathematical… More >>

Modeling Risk: Applying Monte Carlo Simulation, Real Options Analysis, Forecasting, and Optimization Techniques

Switching between the two Leslie simulations in the Nord C2. Both sound good. The new sounds very close to my own Leslie 145. And this is logical since they used a 145 leslie for this simulation. You have to add a little bass on the Eq (according to my taste) on this model if you play left hand bass. The simulation model to use is really a matter of taste and mood i think.

Product Description
Probability, Markov Chains, Queues, and Simulation provides a modern and authoritative treatment of the mathematical processes that underlie performance modeling. The detailed explanations of mathematical derivations and numerous illustrative examples make this textbook readily accessible to graduate and advanced undergraduate students taking courses in which stochastic processes play a fundamental role. The textbook is relevant to a wide variety of fields, includin… More >>

Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling

Six Sigma has become a buzzword in industry circles since its introduction in the 1990′s. The methodology is based on a disciplined and data driven approach and is used for eliminating defects and achieving near perfection by restricting the number of possible defects to less than 3.4 defects per million. It is being used effectively for managing processes of both, manufacturing and services industry. In the manufacturing industry, it is used for limiting defects in the goods produced whereas in the services sector it is used mainly for reducing transactional errors.

What Is Process Simulation And Modeling?

Simulation software based on Six Sigma methodologies can be used for simulating a wide range of processes in order to eliminate common industry problems related to defects, wastage of productive resources, and quality control. The basic idea governing Six Sigma process simulation is to provide an accurate system of measuring defects in a process, so that these can be eliminated during the actual production. The emphasis is on measurement and statistics for ensuring that the process is properly set up and measured. The methodology is also used for measuring baseline performance, which helps predict the effects of any improvements made in the process. Proper application of Six Sigma methodologies leads to tangible improvements effected by decisions that have been explored and proven in detail.

Scope

The Six Sigma philosophy covers different aspects of industrial processes ranging from pure defect management to process performance improvement. With the increasing use of Six Sigma methodologies, it has become widely accepted that process simulation and modeling techniques can be used for improving almost any type of manufacturing or transactional process. Most of the process simulation and modeling techniques are based on DMAIC (Define, Measure, Analyze, Improve, Control). In the ‘Define’ phase, the process simulation techniques are used for identifying the changes that would have made the most impact on the outcome. This helps in making the most appropriate changes. Various simulation tools are used throughout the other phases for providing detailed statistical data on the effect of any proposed changes related to throughput, utilization, delays, service levels, and any other factors related to the process.

How The Simulation Model Works

-The simulation software coverts numerical data into graphic representations, which are relatively easier to understand.
-Commercial companies that specialize in providing Six Sigma services offer customized services, wherein the simulation model is an exact replica including all the elements used in the actual process such as machines, labor, conveyor and belts.
-All these elements are represented by icons and once they are in place, one can define the link between these elements, establish the process flow, and add as much detail as possible for simulating the actual process more accurately and more effectively.
-After setting up the simulation, one can simulate an infinite number of hypothetical scenarios both positive and negative that might affect the process. For example with the help of simulation, a manager can know exactly what will happen if a crucial machining tool breaks down or half the labor force does not turn up for work.
-All these factors can severely affect production. However if the extent of potential damage is known beforehand, appropriate contingency plans can be put in place for restricting or even eliminating the possible loss of production.

Process simulation and modeling techniques help executives make informed decisions. The data generated by the simulation tools empowers decision makers with a deep insight about the process and procedures, allowing them the ability to make the necessary changes.

Predictive analytics is a solution used by many businesses today to gain more value out of large amounts of raw data by applying techniques that are used to predict future behaviors within an organization, it’s customer base, it’s products and services. Predictive analytics encompasses a variety of techniques from data mining, stastics and game theory that analyze current and historical facts to make predictions about future events.

Predictive models examine patterns found in historical and transactional data to identify opportunities and risks. Predictive models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions.

There are some basic and more complex predictive analytics techniques. Three basic techniques include:

-Data Profiling and Transformations
-Sequential Pattern Analysis
-Time Series Tracking

Data profiling and transformations are functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Sequential pattern analysis discovers relationships between rows of data. Sequential pattern analysis is used to identify frequently observed sequential occurrence of items across ordered transactions over time. Such a frequently observed sequential occurrence of items (called a sequential pattern) must satisfy a user-specified minimum support. Understanding long-term customer purchase behavior is an example of the sequential pattern analysis. Other examples include customer shopping sequences, click-stream sessions, and telephone calling patterns.

Time series tracking tracks metrics that represent key behaviors or business strategies. It is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Examples include patterning customer sales that indicate product satisfaction and buying habits, budgetary analysis, stock market analysis, census analysis, and workforce projections.

More advanced predictive analytics techniques include:

-Time Series Forecasting
-Data Profiling and Transformations
-Bayesian Analytics
-Regression
-Classification
-Dependency or Association Analysis
-Simulation
-Optimization

Time series forecasting predicts the future value of a measure based on past values. Time series forecasting uses a model to forecast future events based on known past events. Examples include stock prices and sales revenue.

Data profiling and transformation uses functions that analyze row and column attributes and dependencies, change data formats, merge fields, aggregate records, and join rows and columns.

Bayesian analytics capture the concepts used in probability forecasting. It is a statistical procedure which estimate parameters of an underlying distribution based on the observed distribution. An example is used in a court setting by an individual juror to coherently accumulate the evidence for and against the guilt of the defendant, and to see whether, in totality, it meets their threshold for ‘beyond a reasonable doubt’.

Regression analysis is a statistical tool for the investigation of relationships between variables. Usually, the investigator seeks to ascertain the causal effect of one variable upon another-the effect of a price increase upon demand, for example, or the effect of changes in the money supply upon the inflation rate.

Classification used attributes in data to assign an object to a predefined class or predict the value of a numeric variable of interest. Examples include credit risk analysis, likelihood to purchase. Examples include acquisition, cross-sell, attrition, credit scoring and collections.

Clustering or segmentation separates data into homogeneous subgroups based on attributes. Clustering assigns a set of observations into subsets (clusters) so that observations in the same cluster are similar. An example is customer demographic segmentation.

Dependency or association analysis describes significant associations between data items. An example is market basket analysis. Market basket analysis is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

Simulation models a system structure to estimate the impact of management decisions or changes. Simulation model behavior will change in each simulation according to the set of initial parameters assumed for the environment. Examples include inventory reorder policies, currency hedging, military training.

Optimization models a system structure in terms of constraints to find the best possible solution. Optimization models form part of a larger system which people use to help them make decisions. The user is able to influence the solutions which the model produces and reviews them before making a final decision as to what to do. Examples include scheduling of shift workers, routing of train cargo, and pricing airline seats.

The use of simulation as a training tool is not new; indeed, it has been used since the 1900s to enhance the overall learning experience. The most well understood use of simulation is flight simulators in the aviation industry.

Several minutes into US Airways A320 Flight 1549 from LaGuardia Airport, N.Y. to Charlotte, N.C., the Captain was put in a position where a difficult decision had to be made: could he guide his US Airways plane to a small airport from just 1,600 feet above the Bronx or would he have to attempt a ‘landing’ in the Hudson River? In the end, he had no choice. Captain Sullenberger executed a text-book landing in the Hudson River. The wings were perfectly balanced, the speed of the aircraft was slow enough to not tear the aircraft apart, and so the Captain was able to save the lives of 155 people. Of course, it didn’t take long for Internet bloggers to claim that they had been landing planes in the Hudson River on Microsoft Flight Simulator for years!

Pilots must spend time on simulators. They have no choice. In simulators, pilots are presented with a problem or scenario and then asked what to do next – to which they must respond as they would in real life. A range of routine emergencies are practiced on a regular basis, for example: engine failures, wind shear, total hydraulic failure and two-engine loss at high altitude. Simulators also allow pilots to practice ‘not so normal’ emergency situations which are all undertaken in the safe confines of a land-based computer controlled flight simulator.

Military use of simulation is also well understood. For years US and Australian military forces have trained together in simulation exercises known as Tandem Thrust to enhance their military skills and develop better cooperation. Tandem Thrust 2003 provided training for a maritime-based commander and staff in crisis action planning and for execution of contingency operations. It involved approximately 8,000 personnel.

Military exercises occur every year. Exercise Cooperative Spirit 2008 was a multinational exercise intended to test and improve interoperability and strengthen ties amongst American, British, Canadian, Australian and New Zealand armed forces. This multinational exercise provided a realistic, counterinsurgency-based, Afghanistan-focused environment, used to test and improve interoperability and ultimately prepare units for coalition combat operations.

Stories emerge from battlefields around the globe where troops are fighting in real-life. Most stories link the strength of our military forces (and their successes) to the highly professional and sophisticated training methods we use.

The investment made in simulation appears to be paying significant dividends in the airline industry and in the military; but can simulation deliver benefits to the broader community or other industries within it?

To answer these questions we must take a step back and look at the history of simulation, its application and its use as a training tool.

What is simulation?

Put simply, simulation is a training tool. The term training is used deliberately. In my view simulation is a planned activity and training is something you do, while learning is something that happens. Of course, people learn while they are training; indeed, the use of simulation creates a synthetic learning environment for participants.

While there are many learning activities that can contribute to a person altering or improving their knowledge, skills and attitudes, the objective of using simulation is to train a person through planned activities.

Simulation-based training generally focuses on the ‘application’ of knowledge. This is the key to understanding its importance. It is one thing for a pilot to learn the theory of landing a plane in a river, but it is another thing to actually apply this acquired knowledge without having practiced it in a controlled environment. Through simulation it is possible to train a pilot to land a plane in a river.

Tips for landing your plane in a river
(Source: John Ladd, Pilot, American Airlines)

Land the airplane as slow as possible without stalling Catch both engines in the water at the same time.
(Note: Hit one wingtip first, and you will ‘cartwheel’ the plane) Keep the landing gear retracted Extend the wing slats and flaps Maintain the optimal glide speed Set the tail of the plane into the water first Land as soft as possible and as slow as possible.
(Note: If the nose is too low and going too fast, you risk flipping the plane tail-over-nose)

Fidelity of training

Simulation is a ‘synthetic’ environment; however, it is made to represent reality. The term fidelity is commonly used to describe the extent to which a simulation replicates the reality. For example, the flight simulators used to train Qantas pilots are considered ‘high-fidelity’, i.e. the simulators look, feel and behave like a real plane.

To create a synthetic learning environment that mimics reality, a significant investment must be made. The extent of investment depends on the subject matter and level of fidelity and resources needed. On 14 October 2008, Captain Mark Ford guided the new $300 million Qantas Airbus A380 in for touch-down at Perth International Airport for the first time. He had been trained on the plane’s $34 million flight simulator so that he could confidently carry out this activity in real-life.

Simulation does not necessarily need to be high-fidelity in order for it to be effective. Role play is a common low-fidelity simulation technique. Role play involves a scenario set by the facilitator where training participants are assigned different roles. The roles assigned to participants usually relate to their real-life role; however, this form of simulation also provides an opportunity to ‘act out’ a role not normally performed by the participant in real life. Role play simulations can sometime use external (professional) actors to increase the level of fidelity (e.g. in the health sector).

One important outcome from simulation is that participants are given the opportunity to consider a scenario from the perspective of others. This opportunity can result in the development of a greater level of sensitivity when working in the real-life environment. The corollary is that simulation can also be used to challenge the conventional way of doing things, assumptions, attitudes and working environment.

In their research paper – A Comparison of Simulation-Based and Conventional Training Methods – Experience Builders llc conclude with the following statement: “A sizable body of research suggests that simulation-based training is generally superior to conventional training methods”. Their paper highlights how participants in simulation-based training can: achieve a deeper understanding of the subject matter, retain knowledge for longer periods of time, display a greater level of interest in the subject matter and be better able to transfer their learning experience to their job than participants using conventional training methods.

More and more organisations are turning to simulation-based training to produce meaningful outcomes.

Advances in technology

Flight simulators are very effective. The 9/11 Commission in the US concluded in 2004 that those responsible for flying the planes into World Trade Centre and Pentagon had used PC-based flight simulators during training.

Flight simulators have been in existence for a long time. Pilots of the first powered aircraft trained by proceeding through a graded sequence of exercises; i.e. they used simulation-based training back in the early 1900s.

Over time, there have been significant advances in technology. Computers entered our everyday lives in the 1960s. Those computers had less processing power than the typical modern mobile phone. Today, computers can recreate virtual environments – clearly demonstrated in flight simulators.

Flight simulators come in many shapes and sizes. Even the most basic home computer can be turned into a powerful flight simulator which can download real-time weather data to ‘fly’ in; fly to thousands of different airports; simulate numerous aircraft types; offer multiplayer ‘games’, operate within a virtual airport and also facilitate online talking with other ‘pilots’. These simulators use real charts and ‘navigate’ using typical instruments and aviation navigation beacons.

It is also possible to include moving cars, trucks, boats, and other aircraft into the surrounding environment as you fly. Finally, you can even join the Virtual Pilots Association.

Today, no airline would dream of sending their pilots up without simulation-based training. The use of these high-fidelity simulators has been so successful it is firmly established as a training tool in pilot certification programs throughout the world.

Law enforcement and intelligence agencies have also entered the simulation scene, one of the largest exercises in Australia being Mercury 04. This multi-jurisdictional counter-terrorism simulation was designed by the National Counter-Terrorism Committee. The objective was to exercise the Australian, state and territory government agencies’ ability to prevent, respond to and recover from terrorist threats and acts of terrorism simultaneously in multiple jurisdictions, testing Australia’s approach to national security and the National Counter-Terrorism Plan.

The exercise ran from 22-26 March 2004 and was Australia’s largest and most ambitious counter-terrorism exercise ever undertaken. It encompassed four jurisdictions, with the primary jurisdictions being the Northern Territory and Tasmania, and secondary jurisdictions being Victoria and South Australia.

A simulation such as Mercury 04 enhances capability, understanding and agency interoperability and contributes to the effectiveness of any response to a real terrorist event.

Two things emerge when one analyses the well-understood simulation environments created for pilots and military/law enforcement participants:

participant ‘immersion’ (i.e. whether the simulation is being taken seriously) is critical. Without it, reality is missing, and simulation-based training is particularly useful in high-risk environments.

Simulation in the Healthcare industry

It makes sense that healthcare professionals are embracing simulation as a highly-effective training tool. Simulation in this environment seems blindingly obvious to the mere mortal – why not practice on a synthetic mannequin before trying out the new surgical procedure on a patient?

When you scrape the surface, there are some more interesting (but less well-known) uses of simulation being deployed in the healthcare environment. Firstly, there are two distinct forms of training in healthcare:

technical skills training, and non-technical skills training.

Technical skills training typically relates to the following:

patient assessment and clinical diagnostic reasoning judgment and decision-making regarding therapy, and procedural knowledge and skills relevant to execution of medical procedures.

Simulation facilitates technical skills training in a risk-free environment.

Non-technical skills training is effectively ‘soft skills’ training which is used to develop practitioners so that they may manage sensitive situations surrounding medical procedures. A simple example of a non-technical skill is the ability to sensitively break bad news to a patient’s family. Non-technical skills are also recognised skills underpinning safe and effective clinical practice.

In the healthcare environment, simulation technologies can involve the use of:

Mannequins - Life-like aspects of people and situations are generated by a mannequin. This can also include ‘theatrical’ interaction of actors and props with mannequins to present high fidelity simulations.

Synthetic Models – These allow users to practise technical skills on synthetic tissue.

Virtual Reality – Where a realistic environment is reproduced on a computer display. The user interacts using instruments similar to those used in real life. Sometimes the realism can be improved by the addition of other sensory inputs, such as tactile (haptic) or auditory feedback.

Haptics – Where tactile information is fed back to the participant; for example, the feel of surgical instruments on tissue.

Actors – The use of real people who role play (act) to reproduce a real-life scenario.

Part-task trainers – Part-task trainers break a skill down into its component parts, which the trainee then practices. They allow repeated practice of individual skills while developing competency and confidence.

Augmented reality – A combination of real-life and virtual objects.

(Source: Australian Society for Simulation in Healthcare. Reproduced and modified with permission)

Teaching methods used within a simulation include (but are not limited to) the following:

Scenarios – Using any of the simulation technologies to enact whole events or components of events.

Case-based – Using other formats including written and oral presentations to present scenarios.

Role play – Using any of the simulation technologies to enact interactions between people, for example, in the health industry by health professionals, patients and the community.

Procedural training – Using any of the simulation technologies as a platform from which to conduct a procedure.

Multimodal formats – Refer to activities which integrate two or more discrete teaching methods or curricula which use a variety of specific formats to address specific individual training objectives.

Debriefing and reflection – To ensure participants maximise training outcomes.

(Source: Australian Society for Simulation in Healthcare. Reproduced with permission)

Advantages of simulation-based training

In the health industry, there are many advantages that arise from using simulation-based training.

The risk to patients or trainees is removed. Many scenarios can be presented, including uncommon but critical situations in which a rapid correct response is needed. Participants can see the results of their decisions and actions; errors can be allowed to occur and reach their conclusion (where in real life a more experienced clinician would have to intervene) so that trainees might be permitted to learn from their mistakes without undesirable consequences. Identical scenarios can be presented to different clinicians within teams. When using mannequin simulators, clinicians can use actual medical equipment, exploring the ergonomics of the human-machine interface. With full re-creations of actual clinical environments, complete interpersonal interactions with other clinical staff can be explored and training on teamwork, leadership, and communication provided. Intensive and non-intrusive recording of the simulation session is feasible, including audio taping, videotaping, and even physiological monitoring of participants (such as ECG or EEG). There are no issues of patient confidentiality and the recordings can be preserved for research, performance assessment, or accreditation. The use of simulation makes the learning experience highly realistic.

(Source: Gaba D. Anesthesiology as a model for patient safety in health care, British Medical Journal 2000; 320:785-8)

In his thesis Virtual Reality Simulators in Surgical Education undertaken at the University of Sydney in 2006), Dr Peter Cosman observed that surgical practice is changing in three fundamental ways:

Surgical technique is evolving rapidly. The general public are becoming increasingly aware of aspects of healthcare provision that impact on outcomes, and are demanding high standards of highly trained health professionals. Workforce and resource issues diminish training opportunities for specialists-in-training, but do not address questions of how to train more specialists in a broader spectrum of practice in a shorter period of time with decreased resources and without causing adverse outcomes for patients.

Medical technology and innovation in procedures has evolved since the beginning of mankind; and it is not expected to stop evolving anytime soon.

His final two conclusions ring very true in the current political landscape. The general public and Ministers react to (very disturbing) news stories of deaths, serious injuries and emotional distress when the health system fails. The general public and the people directly involved then get caught in the standard Health System Crisis Cycle – a cycle of health system failure, media attack, political reaction, blame attribution, enquiry outcomes and band-aid measures until the next failure starts the cycle again.

In all the news surrounding patient safety, the general public find it difficult to understand why attention isn’t being paid to quality training. Dr Cosman succinctly concluded in his paper: “There is no doubt that simulation has a significant function in this environment”.

Simulation is being used to improve patient safety

For all the bad news that emerges from Royal North Shore Hospital, Sydney, Australia there are many goods news stories that go unheard.

At about 3:00am on the morning of 1 May 2008 a half-cabin marine vessel, allegedly stolen from its berth at Balmain overnight, was hit by a trawler heading out to sea off Bradleys Head in Mosman, NSW.

Dr John Vassiliadis, an Emergency Medicine Specialist at Royal North Shore Hospital (Sydney), was on-call; however, sound asleep when his pager sounded. He awoke to a crisis. As reported in the media, the nine survivors were taken to Royal North Shore Hospital, while five were killed. Dr Vassiliadis quickly prepared himself for a difficult day at the office.

Dr Vassiliadis applied all his skills, both technical and non-technical, to deal with the situation at hand which involved serious injuries, traumatised patients, distressed family and friends, media interest, politicians and the health bureaucrats from all corners of the system. When a crisis happens, everyone wants to be kept ‘fully informed’.

On that day, the patients, families, friends, and the health system was very fortunate to have Dr Vassiliadis involved. Not only is he a highly regarded clinician, but he also delivers the Emergency Medicine Crisis Management (EMCM) course for the Sydney Clinical Skills and Simulation Centre. Developed and run by specialists in Emergency Medicine and Anaesthesia, this course prepares doctors to manage serious acute problems in the Emergency Department.

This course provides an interesting insight into contemporary training techniques used in the healthcare sector. Not only does the course encompass technical skills training, recognition is given the importance of non-technical skills training – the latter being just as important in the context of healthcare, particularly in events such as the one mentioned earlier.

Crisis Resource Management (CRM) – Seven Key Behaviours

1. Know your environment

2. Prepare and plan

3. Call for help early

4. Take a leadership role

5. Allocate attention wisely and use all available resources

6. Prioritise and distribute the workload

7. Communicate effectively

(Source: Sydney Clinical Skills and Simulation Centre)

Developments in the Healthcare sector

The Commonwealth Department of Health and Ageing provided funding for the Australian Society for Simulation in Healthcare (ASSH) to conduct a project in non-technical skills training in synthetic learning environments. This project will conclude in April 2009.

In their Request for Tender[1]to engage a consultant for this project, ASSH state, “overall, simulation is under-utilised by all specialties”. One of the stated project objectives is to inform specialist colleges about simulation-based training for non-technical skills and the potential to incorporate this into training curricula.

The Request for Tender identifies that – “even the specialties that have adopted simulation can do more to integrate its applications into the implementation of training curricula”. It also states – “Factors contributing to low utilisation include:

low awareness of its potential low engagement by stakeholders inadequate understanding by curriculum designers and clinical teachers of how it can be incorporated into curricula and training activities lack of knowledge of factors underpinning purchasing decisions and evaluation of its impact and value for money perceived or actual lack of infrastructure, funding and other resources, and a lack of awareness of, or access to, appropriate learning technologies and facilities”.

The adoption of new approaches is always challenging and the Request for Tender acknowledges that “new practices will be more successfully adopted and sustained if a strategic approach is employed”. One objective of the ASSH project is to see simulation better utilised by increasing awareness amongst stakeholders, engaging early adopters to champion it and drive change within their specialty and establishing a sustainable model for delivery which overcomes some of the identified obstacles.

The Commonwealth are also supporting other related initiatives.

The Royal Australasian College of Surgeons, through its Australian Safety and Efficacy Register of New Interventional Procedures – Surgical (ASERNIP-S), was engaged by the Commonwealth Government in 2007 to undertake research into the use of simulation in surgical skills training.  The programme, which will conclude at the end of 2010, aims to evaluate different classes of simulation to determine the utility of each in a variety of settings. Professor Guy Maddern, ASERNIP-S Surgical Director, states in the Annual Report 2007 “this important initiative will enable the College over the next three years to define the curriculum and infrastructure support required to provide simulation environments to trainees and Fellows at a variety of sites around the nation”.

These projects are timely in the context of new investment in the healthcare sector.

Show me the money

The Final Report of the Special Commission of Inquiry into Acute Care Services in NSW Public Hospitals by Peter Garling SC, 27 November 2008 identified a health system in a period of crisis. The report is known as the Garling Report.

NSW Health: A typical day
(Source: The Garling Report)

On a typical day for NSW Health across the state of NSW, there will be:

an ambulance responding to an emergency 000 call every 30 seconds; 6,000 patients arriving at Emergency Departments seeking treatment; 4,900 new people being admitted as an in-patient at a hospital; 17,000 people occupying a hospital bed of whom 7,480 are over 65 years old; 7,000 separate procedures performed; and $34 million spent on providing care in public hospitals and for the health of the people of NSW.

Many recommendations contained in the report focused on the need for increased or expanded workforce training capacity and capability. Not surprisingly, simulation was featured in recommendations to enhance skills training. It is worth noting that Garling described NSW hospitals as “good by world standards, in many cases ranking towards the top, but too often unable to deal with the sudden increase in patients, the rising cost of treatment, and the pressures on a skilled workforce spread too thinly and too poorly supported in the dozens of administrative tasks which take them away from their patients”.

The National Health Workforce Taskforce was established in 2006 by the Council of Australian Governments (COAG). In November 2008, COAG agreed to an unprecedented health workforce reform package of $1.6 billion – the single largest investment in the health workforce ever made by Australian governments. It comprises $1.1 billion of Commonwealth funding and $540 million in State funding. Amongst this was a $175.6 million investment in capital infrastructure to support training of the future health workforce, including funding for the construction of new and mobile high-tech simulated learning environments and the expansion of education and training facilities at major regional hospitals.

The cheque made out to: Greater use of simulation in the healthcare sector appears to be in the mail. The challenge will be to implement a coordinated approach to leverage the considerable simulation experience already evidenced in the healthcare sector. A national framework would surely serve to improve outcomes for the general public; however, the challenge may be too great for the system to deal with.

With the benefits of simulation being formally recognised in a high-risk sector; and with simulation being used for non-technical skills training, perhaps it is worth exploring the use of simulation in management training.

Simulation in management

A quick search on ‘Simulation in Management Training’ in Google will result in masses of gunk (that’s Google Junk for short); however, when you start wading through it, a number of areas emerge where simulation is being used:

Project Management Disaster Management Event Management Incident Management Call Centre Management Business Strategy & Planning Safety & Risk Assessment, and Corporate governance and business ethics.

There are countless business game providers. According to one provider, “A policy game offers the ultimate in business education”. While the marketing hype is sometimes hard to take, the complexities of live exercises can be overcome to a large extent through use of integrated gaming and simulation models that allow staff to train across multiple levels and even across multiple organisations to be exposed to the same scenario.

The Simulation Industry Association of Australia (SIAA) exists to advance the research, development, and use of simulation technologies and practices in Australian industry, academia and government. A key activity of the Association is to host SimTecT – the annual Simulation Technology and Training Conference.

At SimTecT 2008, David Urquhart, Macquarie Graduate School of Management (Sydney) and Associate Professor Debbie Richards, Macquarie University (Sydney) demonstrated that the Monte Carlo simulation is an effective way to value a bundle of Human Resource Management (HRM) policies. According to their paper, “the field of HRM addresses issues such as staff costs, performance and turnover – all of which have a basis in uncertainty”. Urquhart and Richards used the GoldSim Monte Carlo simulation software to underpin the project. See www.goldsim.com for more information on this software.

This project served to demonstrate that simulation can be used to model business management scenarios and has the advantage of not interrupting operations. Importantly the modelling demonstrated that simulation software can make it easier for business people to understand and experiment with simulation models.

Many organisations currently reacting to the Global Economic Crisis could be utilising simulation techniques to model the consequences of change. Both economic and social outcomes could be tested. Indeed simulation could also play a role in training those involved in the fall-out from decisions effecting large numbers of people. Just how many senior executives have been trained appropriately to react to angry staff, a media pack and the politician looking for a head to roll?

Approach to simulation

There doesn’t appear to be a prescription for the perfect approach to developing and conducting a simulation. The SIAA website (www.siaa.asn.au) contains a vast amount of material to reference when considering using this training tool.

Simulation should be approached on the basis of:

Planning

The scope of developing and conducting a simulation can be a substantial project – even for a low fidelity approach. The design of course, the resources needed, the tasks which need to be identified and coordinated and the quality assurance all contain significant effort.

Investment

The total effort required to develop and conduct a simulation can be a significant cost. The business case to support this investment must be sound to in order to secure resources needed.

Resources

The human resources, administration systems, simulation technology, infrastructure, facilities and related costs with utilisation of each can add up. Such an investment can require high-level stakeholder support.

Support

In any decision-making process there will be a variety of stakeholders involved. Critical stakeholders are the decision makers, essential stakeholders are those who influence critical stakeholders and those who remain are less important (but not necessarily useless). It is vital that critical and influential stakeholders are ‘on-side’.

The planning, total investment and resources needed means a simulation will need to be supported by those whose opinions count. It is therefore important to undertake careful stakeholder analysis to ensure you gain the right level of support needed.

Conclusion

Simulation is now recognised as a high effective training tool. By default, the exercise must simulate reality; therefore, immersion by participants is critical. Examples presented in this paper highlight the results that can be achieved when simulations are properly planned, investment is made, resources are used effectively and support is gained.

The use of simulation in the Military, Law Enforcement and Healthcare sectors has paved the way for other private and public sector organisations to make greater use of this training tool in its workforce development programmes.

 

[1] Request For Tender (ASSH/001/07), issued by Queensland Health on behalf of the Australian Society for Simulation in Healthcare (ASSH)

Read more: space.newscientist.com Two black holes merge quickly in this simulation of a close encounter between three black holes (Courtesy of M Campanelli/L Carlos/Y Zlochower/HP Bischof; background image: NASA/ESA/Hubble Heritage/STScI/AURA)

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Early action, especially rapid rollout of vaccines, is extremely effective in reducing the attack rate of the H1N1 influenza virus, according to a simulation model of a pandemic outbreak.

View full post on ScienceDaily: Mathematical Modeling News

The precise conditions inside a white dwarf star in the hours leading up to its explosive end as a Type Ia supernova are one of the mysteries confronting astrophysicists studying these massive stellar explosions. Now astrophysicists and mathematicians have created the first full-star simulation of the hours preceding the largest thermonuclear explosions in the universe.

View full post on ScienceDaily: Mathematical Modeling News