2010; 32: 65–70
Distributed simulation – Accessible immersive training ROGER KNEEBONE1, SONAL ARORA1, DOMINIC KING1, FERNANDO BELLO1, NICK SEVDALIS1, EVA KASSAB1, RAJ AGGARWAL1, ARA DARZI1 & DEBRA NESTEL1,2 1
Imperial College London, UK, 2Monash University, Victoria, Australia
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Abstract Distributed simulation (DS) is the concept of high-fidelity immersive simulation on-demand, made widely available wherever and whenever it is required. DS provides an easily transportable, self-contained ‘set’ for creating simulated environments within an inflatable enclosure, at a small fraction of the cost of dedicated, static simulation facilities. High-fidelity simulation is currently confined to a relatively small number of specialised centres. This is largely because full-immersion simulation is perceived to require static, dedicated and sophisticated equipment, supported by expert faculty. Alternatives are needed for healthcare professionals who cannot access such centres. We propose that elements of immersive simulations can be provided within a lightweight, low-cost and self-contained setting which is portable and can therefore be accessed by a wide range of clinicians. We will argue that mobile simulated environments can be taken to where they are needed, making simulation more widely available. We develop the notion that a simulation environment need not be a fixed, static resource, but rather a ‘container’ for a range of activities and performances, designed around the needs of individual users. We critically examine the potential of DS to widen access to an otherwise limited resource, putting flexible, ‘just in time’ training within reach of all clinicians. Finally, we frame DS as a ‘disruptive innovation’ with potential to radically alter the landscape of simulation-based training.
Background
Practice points
Simulation is a vital part of building a safer healthcare system and is one of the top 10 challenges for the health service in the next decade (Donaldson 2009). Simulation can address many limitations of traditional training imposed by a rapidly changing landscape of care (Ziv et al. 2003; Gaba 2004a; Issenberg & Scalese 2008) and has been shown to be educationally effective (Issenberg et al. 2005). Key drivers include dwindling opportunities for clinical experience (both at the bedside and in the interventional settings such as the operating theatre and the procedure suite); the explosion in new surgical techniques (requiring clinicians to learn new procedures throughout their career); and a growing awareness of patient safety as a central issue within clinical care, highlighting the unacceptability of learners ‘practising on patients’. These drivers affect clinical training across all disciplines and at every level of experience, but are especially evident in surgery. Surgeons in training require core skills in assisting, dissecting, camera-holding and dealing with unexpected problems such as bleeding, yet reductions in work hours are reducing operative experience to alarming levels (Kneebone & Aggarwal 2009). Earlier in training, medical students in the past were routinely exposed to the operating theatre, becoming acculturated to its practices through learning to scrub, gown and assist. Curricular changes make routine attendance much less common, generating a real need for novices to undergo systematic induction to the operating theatre environment.
. Simulation has the potential to address many of the challenges facing medical training. . Static simulation facilities have drawbacks including cost and availability. . Distributed simulation represents a disruptive innovation which provides an accessible, portable and inexpensive training environment.
Simulation has the potential to address many of these issues (Aggarwal et al. 2006), provided that an immersive environment can be designed which meets the requirements of effective education without jeopardising patient safety (Kneebone 2005). Learning from mistakes is regarded as a powerful educational experience (Ziv et al. 2005) and surgeons themselves value simulation-based training as an arena to make mistakes without causing harm (Arora et al. 2008). Unlike clinical care, where the patient is at the centre of the process and learning takes place as a by-product, simulation enables the learner to be at the centre of the educational process. Increasingly sophisticated simulators and simulations (a distinction we will return to later) now make it possible to rehearse and assess the complex set of competencies required for safe practice in an authentic environment which corresponds closely to the conditions of actual practice (Moorthy et al. 2003, 2005, 2006; Undre et al. 2007b; Arora & Sevdalis 2008; Koutantji et al. 2008).
Correspondence: Dr Roger Kneebone, Reader in Surgical Education, Department of Biosurgery and Surgical Technology, 10th Floor, QEQM, St Mary’s Hospital, Paddington, London, W2 1NY, UK. Tel: þ44(0)20 3312 1310; fax: þ44(0)20 3312 6950; email:
[email protected] ISSN 0142–159X print/ISSN 1466–187X online/10/010065–6 ß 2010 Informa Healthcare Ltd. DOI: 10.3109/01421590903419749
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Yet for all these benefits, static high-fidelity simulation has drawbacks. Most obvious are the practical ones of cost and availability. To date, high-fidelity simulation has usually taken place in dedicated centres, addressing the needs of ‘high-end users’ in established surgical teams. Such centres are expensive to establish and run and are a scarce resource. Although many such simulation centres run at full capacity, economic and organisational pressures typically prevent them from being routinely used within the curriculum for residents, undergraduate medical students, nurses and others at earlier stages of clinical learning. The number of clinicians who receive regular training in dedicated centres is relatively small, rendering simulation’s uptake amongst key potential markets low (Arora et al. 2009). There are other drawbacks too. Because the locations of simulation centres are fixed, anyone wishing to train in them has to travel. For obvious practical reasons, it is difficult for a whole team to attend a distant simulator centre, reducing realism still further. Once there, the conditions within a simulation centre may not mirror those in participants’ home institutions, raising concerns that the centres’ agendas take precedence over learners’ individual needs. Thus some simulations may be perceived as taking place in their own universe, disconnected from the daily practice of those who come there to learn. Of course there are exceptions. We acknowledge the crucial importance of such centres. Much seminal work has taken place in these environments, establishing the value of simulation-based training, especially for team working and handling complex situations (Gaba et al. 2001; Gaba 2004b; Undre et al. 2007a). Yet the stark reality is that such facilities are not universally accessible. Indeed in many countries they are either not available at all, or confined to a small number of specialist units, thereby excluding a large group of professionals who could potentially benefit.
The challenge of contextualisation An obvious alternative is low and medium fidelity simulators, which require less elaborate facilities. These are in widespread use, mostly for learning specific procedural skills. However, there is a danger of engendering a task-focused approach which privileges psychomotor and dexterity skills over the wider aspects of clinical performance. Indeed much more than technical skill is required for safe surgical practice, with high-profile studies, highlighting that it is often the failure of clinical judgment, teamwork, leadership, communication and professional skills that leads to adverse events (Calland et al. 2002/2006; Vincent et al. 2001, 2004; Undre et al. 2009). An unhelpful preoccupation with simulator technology can cloud the picture. Elsewhere we have argued for a contextualised approach emphasising simulations (whole clinical encounters) rather than simulators (specific items of equipment) (Kneebone et al. 2006a, 2007). Such simulations can ‘activate’ a wide range of responses in clinicians, developing a holistic approach and discouraging a task-focused emphasis (Kneebone et al. 2006b). Work by our group has used real people (actors) to play the part of patients within procedural simulations ( patient focused simulation) and thereby ensuring that human interaction underpins any clinical procedure 66
(Kneebone et al. 2002a). In our experience, much is gained by positioning simulation-based training of invasive procedures within an authentic clinical setting (Kneebone et al. 2002b; Donaldson 2009). So how can such contextualisation be achieved? One possibility is the provision of ‘in-situ’ simulations within a true clinical context, placing a mannikin, simulated patient or procedural simulation in an actual ward or operating theatre (Kneebone et al. 2005; LeBlanc 2008; Miller et al. 2008; Rall et al. 2008; Nunnink et al. 2009). This concept is attractive, especially as it allows individuals and professional teams to train in their own environment (Kneebone et al. 2002c). However, such simulations rely on unused capacity and ‘down time’ within clinical areas. Within current health systems, certainly in the UK’s National Health Service (NHS), pressures on bed occupancy are so high that such simulations would be impossible to schedule reliably. If high-fidelity simulation could be of widespread benefit within contemporary surgical training, how might such simulation be made available to all who need it? The idea of ‘portable’ or mobile simulations is gaining currency. Such approaches usually provide mannikin-based training in mobile facilities (Paige et al. 2009). Although such simulations may provide some sense of context by taking place close to participants’ clinical environment, they may not respond to the educational needs of a given clinical team. And like immersive simulations in static centres, penetration of the total healthcare workforce by in situ and current portable simulations remains low. Another possibility is to develop self-contained simulated environments which can be provided alongside clinical space and where ‘just-in-time’ (Spencer 2003) or situation-related training can take place. This would break down the conceptual wall between simulation and real-world practice, replacing it with a permeable membrane which allows learners to link simulation-based training with actual clinical issues (Kneebone et al. 2004). Such an environment would act as a ‘container’ for contextualised simulation, locating each procedure within an authentic setting of place and people.
What is distributed simulation? This article presents distributed simulation (DS) as a potential solution. We use the term to describe accessible, portable and self-contained simulated environments, which can be used for teaching and assessment. DS sets out to strike a balance between the realism of the clinical setting and the functionality of the simulation centre. Our concept is that simulation should provide an approach to learning which is generally accessible, which can become part of the normal range of educational facilities and which can be tailored to the needs of individual groups. The adjective ‘distributed’ resonates with the terminology of operational research, where distributed systems technology (i.e. linking of computer systems over networks) enables models to be coupled and interoperate during a simulation run (Boer et al. 2009). The term also links to ‘distributed cognition’ and other applications (Hutchins 1996).
Distributed simulation
In DS we identify key elements of real clinical settings and, through a process of ‘selective abstraction’ based on observation within real settings, present these elements within a lightweight, inexpensive and portable environment that can be easily transported and set up at any hospital or learning institution, providing a ‘container’ within which clinical scenarios can be created and replayed (Figure 1). Again this resonates with the literature on operational research, where conceptual modelling underpins the selection of essential elements of a real world system to be included in the underlying for representation in simulation (Wang & Brooks 2007; Onggo 2009).
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Central to DS is a process of ‘active design’. Key elements of the clinical environment are first identified through close observation of clinical practice by a team of design engineers. This is followed by ‘selective abstraction’ of essential components, using design principles to capture and recreate the essence of each. This process ensures that conditions for perceived realism are aligned with practical constraints and educational requirements. The intention is not to reproduce every aspect of a clinical setting, but only those components which are necessary to achieve a sense of realism. In providing such a ‘minimalist’ approach, some compromise is clearly necessary. An acceptable balance must provide key cues, but without the full panoply of an operating theatre, ward or other complex setting. The emphasis is placed on simulation function rather than structure. From this theoretical perspective, the specifications of DS are: . a self-contained immersive environment which can be closed off from its surroundings, allowing any available
space to be converted into a convincing ‘clinical’ setting for the duration of the simulation minimum necessary cues (visual, auditory and kinaesthetic) to recreate a realistic ‘clinical’ environment (including clinical equipment and sounds) key affordances of static simulation centres (for observing, recording, playback and debriefing) in a simple, userfriendly format practical, lightweight and easily transportable components which can be erected quickly by a minimal team the flexibility to recreate a range of clinical settings according to individual requirements, using appropriate physical or hybrid simulations without the need for complex and costly mannikins (Figure 2) minimal cost
Creating the DS environment Using these principles of active design, we have explored the DS concept in several domains including operating theatre, clinic/trauma room and intensive care unit settings. Key elements are as follows: . A self-contained and enclosable space is provided by an inflatable structure which can be easily erected. Inflation by a muted electric pump takes 3 min, resulting in a structure with a footprint of 5 m 4 m and a height of 2 m. When collapsed, the inflatable folds into a bag in the size of a family tent. . A tripod-mounted portable operating lamp, recreating many of the features of inbuilt surgical illumination (circular, multiple bright lights, adjustable position) but made of lightweight moulded plastic and using low-voltage LEDs. A video camera and microphone are built into the central handle.
Figure 1. The DS operating room.
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. Pull-up banners with high-resolution photographs of clinical equipment (anaesthetic machine, equipment trolley) provide representations of key components of a clinical space.
Figure 2.
. Lightweight speakers hidden within the inflatable structure allow clinical sounds (heart monitor, ventilator, ambient clinical noise) to be played. The monitor beep frequency can be controlled from the system computer.
A Hybrid DS using advanced prosthetics.
Figure 3. A simulated operation demonstrated at a recent conference. 68
Distributed simulation
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. Lightweight cameras offer a flexible configuration for recording details of procedures and an overall view of team activity. Recording and playback are controlled via a simple customised interface on a remotely controlled laptop computer which is used for debriefing and feedback. The environment can be set up by two people within an hour, and the key components can be transported in the boot of a small car. The total cost of providing a DS setting is a small percentage of that of most fully equipped static installations. Preliminary work has demonstrated high-perceived value of DS. We are currently exploring its potential and limitations across a range of clinical applications and conducting formal validation studies. The outcomes of this research lie beyond the scope of this concept article. The concept is already giving rise to considerable professional and public interest, as evidenced by the recent NHS Healthcare Innovation Expo (2009) and presentations at the UK and overseas conferences (Kneebone 2009) (Figure 3).
Conclusions DS offers a possible solution to some of the most pressing limitations of immersive high-fidelity simulation, namely access and expense. By making lightweight, portable yet self-contained facilities, widely available at low cost, DS addresses a constituency of healthcare professionals who would otherwise not be using contextualised simulation at all. Although DS cannot provide all the facilities of static centres (e.g. a dedicated control room), our initial investigations suggest that many of those elements can be created to an acceptable level, providing ‘good enough’ simulation which allows effective training for procedural skills in a team setting. Crucially, the environment is physically self-contained, creating a customised clinical universe which meets the educational needs of its users while remaining insulated from the distractions of surrounding activity. In addition, DS offers potential advantages over static facilities, obviating the need for dedicated space and storage. Furthermore, temporarily setting up simulation facilities alongside existing clinical space allows professional teams to work together without having to take time out to travel to distant centres. In Christensen’s terms, we are addressing the needs of ‘non-consumers’, developing a disruptive innovation which achieves many (although not all) of the goals of traditional simulation but at a far lower cost (Christensen et al. 2007, 2008). Like Christensen, we see great positive potential for disruptive technology within education. Our intention in this article is to promote debate on the place of simulation in healthcare education. Restricted access has been one of the key brakes on the widespread uptake of immersive simulation. Innovative, low-cost solutions will be essential if simulation is to realise its potential in supporting the education of a multiprofessional workforce in a changing healthcare landscape.
Acknowledgements The authors wish to acknowledge the invaluable contribution made by Cian Plumbe, Matt Harrison (both of Studiohead) and Max Campbell in providing design expertise during the development of DS. The authors thank the BUPA Foundation and the London Deanery Simulation and Technology-enhanced Learning Initiative (SteLI) for funding this research. Declaration of interest: Dr Kneebone reports owning shares in Medical Skills Ltd, which provides training in procedural skills using multimedia CD-ROMs and models for simple clinical procedures. The other authors report no conflicts of interest. The authors alone are responsible for the content and writing of the article and all contributed to the research and the work involved in the preparation of this manuscript.
Notes on contributors ROGER KNEEBONE trained first as a general surgeon and then as a general practitioner. In 2003 he joined Imperial College London, where his research focuses on simulation and the contextualisation of clinical learning, using innovative hybrids of models and simulated patients. Roger directs Imperial’s Masters in Education (MEd) in Surgical Education. SONAL ARORA is a general surgery trainee and clinical research fellow, currently completing a PhD in simulation-based training of non-technical skills for surgeons. Sonal is interested in assessing and training safety-related skills in operating theatre teams. She is currently exploring simulation as a training tool, alongside other training modules. DOMINIC KING is a specialty registrar in General Surgery in London and is undertaking a PhD in Behavioural Economics and Health Policy at Imperial College London. He has a Masters in Surgical Education from Imperial College London and maintains a significant interest in undergraduate and postgraduate teaching and education research FERNANDO BELLO is a senior lecturer in Surgical Graphics and Computing. His research interests include modelling and simulation, medical virtual environments and haptic interaction. His work spans across technology and education, including development of patient specific simulation and exploring the integration of computer-based simulation and context via patient-focused simulation. NICK SEVDALIS is an experimental psychologist, currently a lecturer in patient safety in Imperial College London. Nick leads a research team that is carrying out research in real and simulated clinical settings, with a focus on non-technical skills (communication, team working, leadership) in surgical teams, and decision-making in physicians and patients. EVA KASSAB holds an MSc in Cognitive and Decision Sciences and is currently a research psychologist in Imperial College London. Eva is interested in assessing non-technical skills in surgical teams in real and simulated procedures. She is currently working on developing further the DS environment and assessment tools. RAJ AGGARWAL is a specialist registrar in General Surgery in London and an academic clinical lecturer in Surgery at Imperial College London. His research interests include the validation of simulation training and training curriculum development for surgeons. ARA DARZI holds the Chair of Surgery at Imperial College London and is an honorary consultant surgeon at St Mary’s Hospital, London. His main clinical and academic interests lie in minimally invasive therapy and educational research. His teams were awarded the 2001 Queen’s Anniversary Prize for Excellence in Higher and Further Education. DEBRA NESTEL is a professor of Medical Education at Gippsland Medical School, Monash University and consultant to Imperial College London. Her research interests are in clinical communication, simulated based education, especially simulated patient methodology and programme evaluation.
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