Essay: Simulators used in neurosurgery and emergency evacuation events

1. Introduction
This report analyses and discusses the type of simulators that should be used in neurosurgery and in emergency evacuation events. The research was conducted in five areas: 1) the type of simulator, 2) the appropriateness of the simulator, 3) what will be measured and how, 4) how to maximise the quality of the data, 5) the limitations of the simulator.
The design for simulators in neurosurgery and emergency evacuation events should be based on two factors, fidelity and validity. Fidelity is the attribute determines how close the simulation reproduces the real world (Liu et al., 2009). Fidelity can be analysed further, physical fidelity and psychological. The physical fidelity of the simulation is dependent if it can replicate the real world and satisfy the input human senses. Psychological fidelity is the ability for the simulator to replicate cognitive factors (Munshi et al., 2015). The other attribute, validity, like fidelity, can be analysed further; internal validity and ecological validity as well as absolute and relative validity. Internal validity is the how a changing parameter (time) is compared to a controlled variable (layout of instruments) and ecological validity is the comparison of the data from the simulation to the real world. Finally, absolute and relative validity are the translatable measurements from the simulator to the real-world and the effects of the condition relative to the real work, respectively (Mullen et al., 2011). The importance of validity is the ability to transfer the training from the simulator to the real-world. The training will have an effect on the real-world situation, whether it is positive transfer or negative transfer of training (Liu et al., 2009).
For simulators in neurosurgery, the validity must allow for the transfer of training to the real-world. The ideology that a higher fidelity will provide a better transfer of training was disproven (Alessi, 1988). This has further been evaluated and disproven by other studies; therefore, the law of diminishing return is an issue when designing a simulation (Liu et al., 2009). However, if this is the case, then the need for high fidelity is less apparent and the cost of the simulation will be cheaper. Moreover, the cost is not just dependent of the fidelity it also depends on the types of patients, the objective of the simulation and the technology (Gaba, 2004).
When investigating human behaviour in an emergency evacuation
2. Scenario 1 — Electronics Company acting as supplier to hospitals
As discussed in the introduction the simulation should be based on the fidelity and validity, thus the training on the simulator must be able to translate well into a real world. The simulator used will be dependent on the objectives and the curriculum of the training. For this case, the simulation needs to ensure a positive, cost-effective transfer of training.
2.1. What type of simulator will be used?
The simulator needs will comprise of both visual fidelity and motion fidelity. For visual fidelity, an accurate geometric model is needed rather than the need for high graphics (Kockro, 2013; Gasco et al., 2013). During the training, the surgeon will be accustomed to the look and structure of the brain; therefore, the anatomical accuracy is more important than high graphics, thus keeping the cost considerably cheaper. Furthermore, the simulation will provide realistic visual feedback in real-time, with the added simulation of real-time tissue deformation (Paloc et al., 2001). There is, however, a need to specialise the type of imaging used for the simulation. There two environments that can be assessed, first order and second order. The use of first order will use standardised patient data, based on multilayers cadaver images; the second order will use patient-specific data and transform it into a 3D model (Robison et al., 2011). For this simulator, the use of first order environment is appropriate. Therefore, the simulation will be a 2D virtual reality (VR) stereovision with volumetric imaging from cadavers.
Motion fidelity is one of the harder areas to simulate. However, the need for haptic feedback and tactile response is needed to ensure a high success for transfer of training. The use of haptic feedback does not need to fully replicate the full motion in the 3D space or the deformation of the tissue. The simulation just needs to provide a realistic enough kinetic feedback that will convince the human stimuli that the users are in the same environment as the operating room (OR) (Robison et al., 2011).
The use of interchangeable tools for the simulation could provide a benefit to the user. However, the cost of this approach is not cheap. There are simulations where software and tools with 6 degrees of freedom which have been incorporated in the training. Due to the complexity of neurosurgery, the user needs to come to terms with the different tools that will be used in the real world; some of these tools include: bipolar forceps, micro-scissors and endoscopes (Choudhury et al., 2013).
Finally, the need for teamwork will be incorporated into the simulation. The use of teamwork at the simulation stage improves the professional attributes of the surgeon (Toader, 2015). The simulation will have the capability of the allowing team members to view the screen which the surgeon can also see. This would help improve communication between the surgeons and improve overall efficiency.
2.2. Why is the simulator appropriate?
In the case for neurosurgery, it is important to replicate the scenarios as closely to the real-world in the simulator. The advantage of a 2D VR simulator compared to mannequins or hybrid versions, is that they provide objective performance in real-time during training. Furthermore, it gives the students a chance to practice on whole procedures or part of a procedure. This type of simulator also will allow for the students to improve psychomotor skills, with the added possibly to adjust the difficulty in real-time. A further advantage of the 2D VR simulator is that the procedures are repeatable, thus allowing the surgeons to enhance their skills and letting them become automated (Schmidt-Panos & Scerbo, 2009). However, cognitive loading can be a problem with surgeons, with a chance that the learning process will take longer and their performance will be poor. This could make the results obtained from the simulator moot (Gasco et al., 2013). Although haptic feedback is still in its early stages of development. It provides the best scenario for transfer of training. The involvement of the team during the simulation will also allow for direct feedback either from the lead surgeon in the room or other team members, increasing the rate of improvements to their learning (Toader, 2015).
2.3. What will be measured and how?
In order to obtain data from the simulation, several parameters will be measured. One of the main metrics to measure is the time to complete the task. In the past, this has been the most cost-effective parameter to measure. After the surgeon has been brief on the type of surgery to the brain, the simulation is started as well as the timer. Another metric to measure is the hand movement when completing a task. To measure the movement the pitch, roll and yaw will be recorded on the computer (Wayne Overby & Watson, 2014). Furthermore, eye-tracking, combined with hand movement, will measure whether the surgeon can anticipate the next step in the procedure. This will be completed with a set of eye tracking goggles (Schmitt et al., 2012). However, it is difficult to measure everything being inputted by the user, some of which could be due to human limitations such as cognitive loading and behavioural problems with either themselves or the simulation (Liu et al., 2009). Therefore, the use of subjective means needs to be assessed. The problem is that the user’s surgical skill is subjective and, therefore, evaluating it requires objective measurements (Paloc et al., 2001). Finally, the use of a questionnaire would be used before and after the simulation. The questions will be based on the user’s experience both spatial and engagement, as well what they learnt from the simulations and difficulties experienced whilst using the simulator.
2.4. How will you maximise the quality of any data generated by the simulator?
The best way to maximise the data obtained for the psychomotor skills is to set a gold standard by expert neurosurgeons. Although skill is still a subjective measure, the standard will allow surgeons in training to be compared to the experts, thus working from a benchmark (Choudhury et al., 2013).
As discussed in the previous sections, the main objective of the simulation is to develop the surgeon’s psychomotor skills. However, this should not be the only objective for the simulation. In order to improve the validity of the simulation, there will be the requirement of presence. At this time, there are no know neurosurgery simulators. The requirement for presence is dependent on the involvement and the immersion. Presence is the belief that the user is situated in one domain, whilst being in another (Witmer & Singer, 1998). Involvement for this simulation is the haptic feedback, where immersion is the environment the surgeon is exposed to during the training.
For this simulation, the room will set up exactly like the OR. Before the surgeon has entered the room, the other team members are dressed in the accustomed attire, similar to that of a real operation. Furthermore, the lead surgeon will carry out the same conversation before and after the operation as they would when operating on a person. The purpose of this is to remove the ideology that the surgeon is simply playing a video game. This method is a cost-effective way to allow the surgeon to believe it is real and will take the simulator more seriously.
3. Scenario 2 — University research department
3.1. What type of simulator will be used?
3.2. Why is the simulator appropriate?
3.3. What will be measured and how?
3.4. How will you maximise the quality of any data generated by the simulator?
4. Discussion and Conclusions
4.1. What limitations do you envisage in the use of this simulator? — Scenario 1
One of the many limitations see with this simulator is the lack of tactile feedback. At the moment, recreating the mechanical and elastic properties of the tissue is a problem for simulators. (Kockro, 2013; Robison et al., 2011). Another limitation which can be envisaged is ergonomic set up compared to the OR. Unfamiliar layout could cause problems with the transfer of training.
Furthermore, validity will be an issue with the simulator. In terms of real-time feedback, any lag or delay will reduce the face validity of the simulation. This is dependent on the computational speeds to progress the relevant algorithms. Moreover, there needs to be a gold standard set by expert neurosurgeons in order gain a high validly for the simulator (Choudhury et al., 2013).
Finally, a problem with the simulator in regards of the patient, surgeon relationship; becomes dehumanised. This is due to the surgeon developing their psychomotor skills, so that they become automated, but they spend less time talking to the patient. This could increase the stress of the patient and effect overall performance (Schmidt-Panos & Scerbo, 2009).
4.2. What limitations do you envisage in the use of this simulator? — Scenario 2
4.3. What are the key differences between the two simulators?
• Cost benefit
5. References
Alessi, S.M., 1988. Fidelity in the design of instructional simulators. J. Comp.-Based Instruction, 15(2), pp.40-47.
Choudhury, N., Gélinas-Phaneuf, N., Delorme, S. & Del Maestro, R., 2013. Fundamentals of Neurosurgery: Virtual Reality Tasks for Training and Evaluation of Technical Skills. World Neurosurgery, 80(5), pp.e9-e19.
Gaba, D.M., 2004. The future vision of simulation in health care. Qual Saf Health Care, 13(1), pp.i2-i10.
Gasco, J. et al., 2013. A Novel Virtual Reality Simulation for Hemostasis in a Brain Surgical Cavity: Perceived Utility for Visuomotor Skills in Current and Aspiring Neurosurgery Residents. World Neurosurgery, 80(6), pp.732-37.
Kockro, R.A., 2013. Neurosurgery Simulators — Beyond the Experiment. World Neurosurgery, 80(5), pp.e101-02.
Liu, D., Macchiarella, N.D. & Vincenzi, D.A., 2009. Simulation Fidelity. In Human Factors in Simulation and Training. pp.61 – 73.
Mullen, N., Charlton, J., Devlin, A. & Bédard, M., 2011. Chapter 13: Simulator Validity: Behaviors Observed on the Simulator and on the Road. In Handbook of Driving Simulation for Engineering, Medicine, and Psychology. CRC Press; 1 edition (April 25, 2011).
Munshi, F., Lababidi, H. & Alyousef, S., 2015. Low- versus high-fidelity simulations in teaching and assessing clinical skills. Journal of Taibah University Medical Sciences, 10(1), pp.12-15.
Paloc, C., Kitney, R.I., Bello, F. & Darzi, A., 2001. Virtual reality surgical training and assessment system. International Congress Series, 1230(1), pp.210-17.
Robison, R.A., Liu, C.Y. & Apuzzo, M.L.J., 2011. Man, Mind, and Machine: The Past and Future of Virtual Reality Simulation in Neurologic Surgery. World Neurosurgery, 76(5), pp.419-30.
Schmidt-Panos, E.A. & Scerbo, M.W., 2009. Simulation Advances in Medical Training: Laparoscopic Skill Acquisition. In Human Factors in Simulation and Training. pp.371 – 385.
Schmitt, P.J., Agarwal, N. & Prestigiacomo, C.J., 2012. From Planes to Brains: Parallels Between Military Development of Virtual Reality Environments and Virtua Neurological Surgery. World Neurosurgery, 78(3/4), pp.214-19.
Toader, W., 2015. Clinical simulations for learning medical skills: a work based approach to simulators. Procedia – Social and Behavioral Sciences, 197(1), pp.2443-48.
Wayne Overby, D. & Watson, R.A., 2014. Hand motion patterns of Fundamentals of Laproscopic Sugery certified and noncertified surgeons. The American Joeournal of Surgery, 207(2), pp.226-30.
Witmer, B.G. & Singer, M.J., 1998. Measuring presence in virtual reality environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), pp.225-40.