Thursday, 5 May 2016

Programming People


Apologies for the delay in the (planned) fortnightly article, pesky minor inconveniences such as parenthood, and holding down a job in a foreign land have been tickling my procrastination bone.

And apologies in advance to all Charlies and Kims out there…

In this article we are going to begin to look at ways that simulators can be programmed.
Programmed you say? Well yes, I’m going to be using the word programming a *lot* in this article, and I make no apologies for doing so.
I am NOT a programmer. Real programmers use real programming languages, I’m just using the software as provided by the simulator manufacturer.
However, I’m using the software to generate a nested series of IF>THEN>ELSE loops and triggers in order to change the simulated patient’s condition.
The simplest example I can give is this, in the case of a patient struggling with an unspecified breathing problem
IF (Oxygen is applied) THEN (Raise Saturation) ELSE (Do nothing)
We can make this more sophisticated by asking how much oxygen is applied. By which method? And when in the scenario? But the principle remains the same.
IF (Intervention=True)
THEN (Alter the patient’s condition),
ELSE (Do nothing, or further deteriorate the patient)
Well, it looks like programming and smells like programming and…oh, delicious, this programming is beautifully cooked! It also works like programming, which is a bonus.
In my own simulation environment, where we don’t have the time, space or resources available to moulage or make up the patients and rooms, we need to try and introduce quality into our simulations in other ways. Programming the physiological responses of the simulator is one of these ways.
Much more on this later. Woo!

Traditionally and sadly currently, programming is seen as a substitute for medical knowledge and experience. I believe that when used effectively it can only augment and strengthen the delivery of medical simulation.
Operators; those that “drive” the simulators, respond verbally as the patient, and manage the physiological parameters of the patient via the software, fall veeeery broadly into two distinct camps.

  1. Experienced healthcare practitioners (HCP) that have “seen everything”
  2. Non medically qualified technicians/engineers/IT nerds/weirdos
And programming the simulator has itself traditionally taken one of the following forms
  1. Start parameters, lung and heart sounds etc. are set up for the beginning of the scenario and any changes or interventions are handled live “On the Fly”
  2. The patient moves from one hard wired state/set of parameters to another after a specific time, specific intervention or at the direction of the session leader. We’ll call this “Storyboarding”
\\lul-net\dfsLUL\Users\d\dip001\My Pictures\nurse-leather-strap-anesthesia.jpgLet’s call our experienced HCP Kim, for gender neutralities sake, and because I don’t know anyone called Kim. Kim has worked in emergency medicine and on the intensive care wards for many years, they have seen just about every simulatable situation there is. Kim trusts in their knowledge and experience, we trust in Kims knowledge and experience and many patients have trusted in Kims knowledge and experience. Kim prefers to drive the simulator “On the Fly”, because of the wealth of anecdotal knowledge available to them. Kim doesn’t like to Storyboard their simulations, they know what a Septic Shock looks like, and they have seen Septic Shock many times and are happy to trust themselves to manage the numbers and patient responses without relying on programming. After all, it’s received knowledge that we don’t know which interventions the participants are going to perform, or in which order.

However, from day to day, and from session to session, Kim remembers a different Septic Shock on a different patient, in a different place with a different level of care, with different vocal responses and demeanor on behalf of the patient. These inconsistencies play a part in making the rest of the team unsure as to how the session is going to go, especially when it is a session that is likely to be repeated or even run in parallel, or when the other instructors aren’t familiar with Kim. It’s pretty much all down to Kim (but Kim likes this!) They may even feel threatened or slighted by the idea that their expertise isn’t being called upon if the simulation relies on programming (but they shouldn’t)
When the goal of the simulation part of the teaching session is to illicit some sort of Crew Resource Management based teamwork that can be discussed during a structured debrief, it can be an advantage to have some idea of how sick the patient is going to be that’s not based on the whim of the operator. Additionally, despite Kim’s wealth of experience, they are likely at some point to make a mistake. CRM debriefing a group of participants is challenging enough without then folding their arms and thinking, “Well, medically, that was bullshit, so why are we taking it seriously?”
Let’s introduce our Technical Wonk, and let’s call them something gender neutral and non-familiar like Charlie.
Charlie doesn’t trust in their medical knowledge and feels sure they are going to miss something if they drive “On the Fly”, Charlie, however has consulted patient cases and textbooks and has constructed a Storyboard in the programming that matches exactly that patient on that day in that textbook as treated at that time by that team.
Patient presents as sick, patient gets worse, the team works, patient gets better. That’s the plan and generally speaking it’s a sound one. The physiological parameters fit the disease state, the simulator can be programmed to have a deteriorating condition over time and at the click of a button, the patient improves to a point where we are satisfied that the learning goals of the session are met and nothing is missed. After all, it is documented medical fact which interventions should take place for a particular disease state, and in which order.
This case can be repeated with the same results across multiple dates, across parallel sessions with predictable consequences.
\\lul-net\dfsLUL\Users\d\dip001\My Pictures\Medical-Sim-Lab-Control-room-800x400.jpg
However, this approach assumes a lot, not least of which is that the participants come to a specific diagnosis and the correct treatment plan. As the physiology is “hard-wired” in obvious stages (Sick, deteriorating, recovering) it can leave participants feeling that they didn’t really participate in a way that affected the patients’ recovery directly, rather than the patient became better in some semi arbitrary manner after an amount of time had passed, or enough treatment boxes where ticked. Challenging enough to CRM debrief a team without participants crossing their arms and saying “That may as well have been numbers on a screen, and I don’t feel part of a team”
Clearly, I’m oversimplifying things to emphasize my own point of view (It’s my blog and I’m allowed to!) But we’ve all worked with individuals that remind us of Charlie or Kim and recognize and appreciate the strengths and weaknesses of their own approach to simulation even if they don’t.
And again, there are many more ways to program simulators and simulation, I’ve just taken two of the most recognizable examples which, handily, happen to be polar opposites of each other.
But they don’t need to be.
As outlined in a previous article, we run an absurd amount of simulation, across multiple and parallel sessions with huge cohorts of students. What we know is that the participants crave consistency, they talk to each other before and after and compare their experiences, even when we ask them not to, y’know?
It is UNFAIR for one group of participants to suffer a poorly executed simulation session when the poor execution is entirely avoidable and down to inbuilt inconsistencies or inflexible programming.
Our simulated patients can’t be based on Kims anecdotes, as there is only one Kim.
Our simulated patients can’t be That patient in That case study treated by That team as it’s not That team.
In each of our sessions it is always This patient in This room treated by This team (But also, This patient in That room treated by That team)
So how do we do that?
I’m splitting this article in two, it is long enough already.
So, in the next exciting installment of The Simulated Man I’ll be introducing the approach to programming that I’ve developed here at Akademiska University Hospital
In the meantime,
Stay simulated!

Friday, 15 April 2016

Split simulated personality: Parallell simulations

Simulated People are People too!
Here at Akademiska KTC, we deliver upwards of 400 hours of simulation every term, to put that in laymans terms, thats a HELL OF A LOT! IT’s MADNESS!
We like to keep the group size as small as manageable, and find that maybe 6 or 8 is the maximum we can take and still deliver a debriefing that facilitates an advanced depth of reflection and a focus on Crew Resource Management.
“Thanks” to the enormous and increasing demand for Medical Simulation, we find ourselves running often two, sometimes three and on occasion FOUR simulation sessions simultaneously.
So that’s more repeat sessions, repeated more frequently.
In an ideal world we would like all our students/customers/stakeholders (insert your own description here) to have had a similar learning experience, and have reached their teaching goals regardless of which session they have attended.
How do we go about attempting that?
Four simultaneous sessions, in four different rooms. Importantly, it is THE SAME PATIENT experiencing the SAME SCENARIO in EVERY ROOM.
Much of what the students perceive during a simulation comes from the operator/voice of the patient. Therefore, there is a responsibility for the simulation team to make sure as much as possible that the broadcast information is correct, or at least not misleading.
Operating the simulator, and interacting with the students as the patient is a criminally underappreciated art form. Yes, I absolutely would say that, and I absolutely believe it to be true. So important that it merits its own article, so stay tuned for some serious soapboxing in the coming weeks.
Repeating the same case across four sim sessions is simply a matter of programming/storyboarding the process, right? As long as the numbers show the same thing at the same time then the students should be led along the right lines, and everyone kind of stumbles over the finish line together.
Well, that is assuming that each group of students does the same thing at the same time in every room, and that the patients disease state is non dynamic and everything follows a linear progression.
In the immortal words of trained simulator instructor ICE-T, “Shit ain’t like dat”
The students should be free to deliver whichever sort of intervention they feel to be relevant to the patients disease state as they themselves interpret it, however, and this is a BIG however…
A liter of fluid delivered to the patient in Room A should have roundabout the SAME effect as a liter of fluid delivered to the same patient in Room B and 5l/min of administered oxygen should have the same effect on Patient A as it does on Patient B, C and D and NOT just what the operator *thinks* is right based on their clinical experience. Yes the operator may have seen patients in this disease state, and yes, their experience is in no way to be discounted but to ensure parity of experience for our students, anecdotal evidence is not enough. *Much* more on numbers, trends and science on another occasion.
Feelings, nothing more than feelings
Patients are, of course more than a set of standard responses coupled to a set of trends in their physiology.
When we run standardized patient cases (using real people made up as patients) we often employ professional trained actors to convey the emotion of being a patient in a disease state. So why are we neglecting this important aspect of patient care when we run patient cases using a patient simulator? Patient simulators can be hard to communicate and empathize with due to how they look and their lack of mobility. It is often left to the simulator operator to interpret and convey the patients mental state how they see fit. Had a bad day? Grumpy patient maybe.
Fine I guess for one off simulations, but let’s remember, THE SAME PATIENT in the SAME SCENARIO in EVERY ROOM should mean the SAME EMOTIONAL STATE for every instance.
Realism is as important as repeatability, in every sim session we run.
A recent post from the excellent SimGhosts blog (Hail! My brothers and sisters in arms!) Shared a feature not commonly found on Sim Scenario templates. It’s taken from a journal article regarding integrating actors into a simulation program and is mainly aimed at the SP world.
The original looks like this
It can be agreed in advance with the Sim Team what sort of state the patient is in when encountered, and the appropriate numbers ringed, the sim operator then has a better idea of how to deliver their lines and frame their responses.
So far so good.
I’ve developed this strategy further and would like to share my suggestions.
Firstly, nine categories of patient feelings are maybe too many when considering all the other info the simulator operator is dealing with. I’ve gone for six.
Secondly, the info is presented in the form of a simple table, I’ve gone for something a little more intuitive
Thirdly, what happens as the scenario develops? Does the patient become calmer? Angrier?
My version looks like this
It’s a simple spider diagram easily created using Microsoft Excel (I say easily, we work with Office Online and it’s as *easy* as pushing water uphill with a fork)
I actually took the ideal from marriage-destroying football management simulation Football Manager where it is used as a way of comparing players attributes.
Firstly, six categories of patient feelings. Simpler, broader, less open to interpretation.
Secondly, a spider diagram allows for an at-a-glance overview of a patients emotional state. More coloured area equals higher emotions, more drama and overacting! (Hopefully not)
Thirdly, two shades of colour. Why? The darker shape represents the patients INITIAL emotional state, and the lighter shape represents the patients potential emotional state as the case develops. Overlapping trends don’t need to be hidden under each other with some clever formatting of lines and transparency.
In this medical case, the patient exhibits the symptoms of ongoing sepsis/septic shock.
Emotionally, we can see the patient is mostly stressed and increasingly confused as the scenario develops.
A patient in a diabetic coma, or heavily intoxicated might present with a 0 or a 1 in all the categories, but become extremely confused or even angry when awake.

We have started using this approach with some success during the running of multiple sessions. The success is purely based on anecdote as I can’t think of a way to measure parity of teaching across multiple scenario sessions without using the time travel machine that I am going to have invented last week tomorrow.
That being said, using this device could be argued toward contributing towards parity of experience for all our students, and that feels important to us.
Coupled to this, we are looking to program the parallel patients physiological responses to interventions in a novel way.
We are not looking to standardize the sessions, but we are looking to ensure that we can standardize our delivery without sacrificing the creativity and spontaneous nature of Medical Simulation.

Next up on thesimulatedman
Programming People : Pros, Cons and tricks
See you then, stay simulated!

Friday, 1 April 2016

*Published* Advances in Physiology Education *In Press*

Simulation as Science 

In my simulation career, I've been lucky enough to work with some very talented and knowledgeable people, and in work environments where our combined talents and knowledge where given the opportunity to flourish and grow.

During my time at Bristol University I was part of a team developing a novel approach to medical science based simulation, and we continue to be proud of the steps that we made.

I'm not going to go too much into the details now, but sure to bang on about it at length in good time.

However, I'm very proud to say that Dr Richard Helyer and my humble self have had a short communication on the subject accepted for publication in a forthcoming issue of Advances in Physiology Education.

I am happy to present a draft gratefully recieved.

Progress in the utilisation of high-fidelity simulation in basic-science education

Richard J Helyer* PhD & Peter J Dickens**

*School of Physiology, Pharmacology & Neuroscience, University of Bristol. Bristol, BS8 1TD, UK
**Akademiska University Hospital, Uppsala, Sweden

High-fidelity simulators with responsive, functional physiological models are typically used to teach clinical skills and clinical emergencies. This teaching is usually delivered to cohorts other than those in early years of undergraduate courses or those studying the basic-sciences that underpin medicine such as physiology and pharmacology.  It is now some 15 years since seminal papers by Euliano and others (Euliano, 2000, 2001; Zvara et al., 2001) first described the utility of using human patient simulators (HPS) to teach key physiological principles, 10 years since adoption of HPS (CAE, Canada) at the University of Bristol into basic-science teaching to early-years undergraduates, and some 5 years since the Bristol approach was summarised by Harris et al. (2011). 

In Bristol over the past 10 years we have continued to develop simulation as a core part of the curriculum embedded alongside traditional lecture, tutorial and practical class teaching (Harris et al., 2011). We currently use HPS to teach seven separate scenarios in physiology and pharmacology across three basic-science and three professional programmes including medicine. Over 1000 students per year receive some form of simulation teaching in their first two undergraduate years. Final year basic-science students are also able to select ‘laboratory’ projects using simulators to explore in-depth aspects of integrated human physiology that would otherwise be impossible eg. altitude and descent to depth as similarly reported by Hyatt & Hurst (2010).

Despite these exciting innovations, high-fidelity simulators with a functional physiology are still under-utilised in basic-science teaching, with only  few reports in the literature (Hyatt & Hurst, 2010; Gabi et al, 2013).  In fact, the converse is probably true in that they are more typically utilised in teaching basic skills that do  not require high-fidelity models – the ‘fidelity trap’ (Lampotang, 2008). Further, there may be a misconception as to what is actually being taught with simulation.  Teaching that demonstrates changes in heart rate and blood pressure during bleeding to nursing students, although clearly valuable, is far removed from using simulated physiological data to effectively demonstrate the action of Starling’s law during haemorrhage. The latter is an example of high-fidelity teaching aimed at complex principles that students may find difficult. The potential for using simulators in this type of teaching was first shown by Euliano et al. (1997) & Euliano (2000, 2001) and further developed at Bristol (reviewed by Harris et al. 2011) and a small number of locations elsewhere (including Waite et al., 2011; )

The question remains as to why high-fidelity simulation still remains under-utilised in teaching basic-sciences despite this potential and the increased adoption among teaching hospitals and university departments. A number of factors may be involved. First, developing physiologically accurate scenarios can be difficult and time consuming. Scenarios should be validated against published human data (Lloyd et al., 2008; Harris et al., 2011), which itself may be scarce, and the model modified in order to improve fidelity. Second, there are few simulators with an effective, integrated physiological model that produces data required for full exploration of physiological principles, and these are expensive in terms of basic cost and servicing. Other less expensive, commonly adopted simulators may fall short in terms of integration of even the most basic cardio-respiratory responses. Third, faculty may be wary of using simulator models versus traditional teaching or non-integrated computer simulations which may produce accurate, but limited data in terms of homeostatic integration with other systems, eg. an isolated heart model. In Bristol, concerns by faculty around the fidelity of pharmacolological models of HPS vs stand-alone computer simulations for calculating dose-responses and drug interactions, have hampered wider adoption. This is despite the attraction of being able to demonstrate effects across systems. Finally the complexity of scenario creation may dissuade even the keenest developer. It is very easy to produce a simple model of say, blood loss that can be demonstrated at a superficial level. It is very hard to develop one where all relevant physiological variables closely match published human data. 

Matching data produced with the literature is an example of the highly accurate, validated approach taken in Bristol. To add a further level of fidelity in terms of simulating homeostatic interactions, we adopted a ‘dogma’ that our scenarios should be exclusively ‘model-driven’. In theory, this means that layers of changes and perturbations can be applied over the primary scenario. For example, in demonstrating the response to low inspired O2, rather than simply setting controllable variables to simulate the response, data were entirely based on the actual response of the simulator via its ‘lung’ and in real-time. To do this, the basis must be a reasonably accurate model with responses that can be fine-tuned by applying gains and factors to variables, rather than overrides. And certainly without simply presenting static data to students, for example when blood-gases are requested. This though, produces a further level of complexity.

The question remains even for the teaching of basic science in some detail,  is this level of model-driven fidelity required? Are even physiologists effectively using simulation caught in the ‘fidelity-trap’. Has the ‘fidelity-trap’ hampered wider utilisation of simulation in basic-science teaching? It is evidently far more practical to produce accurate data by applying overrides and ‘fixes’ to models to produce data at valid values in terms of the literature, and as importantly, what students might expect to see in a textbook. This approach is also repeatable, as data will be identical for each session – in the model-driven HPS equipped with a lung, respiratory data in particular vary from run to run and drift over time. Further, setting variables to fixed values avoids having to work within a complex model with feedback loops where changing one parameter will have knock-on consequences on another. In other words inconvenient homeostatic algorithms can be circumvented . Finally, we could ask why use a simulator at all? This question is beyond the scope of the current discussion!

The future for high-fidelity simulation in basic-science education may be in finding a middle-way. Some lower-cost simulators without the ability of the HPS to effectively exchange gases or operate with a ventilator utilise similar physiological models (it should be noted that not all do, eg. presentation of blood gas data, so careful choice of mid-range platform is required) . In fact, a mixed-approach to producing teaching scenarios with some data produced by model-driven aspects of the scenario, and others determined by over-rides, can produce data where a dogmatic, purely model-driven approach fails. An example is demonstration of the classic alveolar gas equation derived by Fenn, Otis and Rahn that shows the relationship between O2 and CO2 (learning opportunities described by Curran-Everett, 2006). An accurate demonstration of this equation is not possible using a CAE HPS with a lung. However, using the HPS software-model alone, or with a manikin that does not have a lung, extremely accurate results can be obtained compared to published human data (Helyer & Lloyd, 2009). 

Unfortunately, a final area of consideration remains for even the keenest adopter that remains a prevailing question- does using simulation improve learning outcomes? Here there is no clear evidence in the basic-sciences. There is little doubt that simulation in the broadest sense is an effective tool in improving learning and outcomes in medical education (McGaghie et al., 2011). This is probably most apparent in disciplines assessed via achievement of skills and day-one competencies. In other areas, the relatively scarce evidence centres around improving confidence of students or in preferential learning methods (eg. Harris et al, 2011) rather than in measurable improvements in examination results. This is not limited to simulation as assessing impact on learning in terms of measurable outcomes is notoriously difficult. We may take some solice by consider whether this is really an issue in a climate where student satisfaction and learning-method preference seems to be becoming a prevailing driver.

We conclude that high-fidelity simulation in basic science education remains an under-developed resource with considerable potential. By careful matching of hardware and software to teaching and learning objectives, it remains a potentially highly-effective tool.


McGaghie, WC, Issenberg, SB Cohen, ER, Barsuk, JH & Wayne, DB (2011). Does Simulation-based Medical Education with Deliberate Practice Yield Better Results than Traditional Clinical Education? A Meta-Analytic Comparative Review of the Evidence. Acad Med. 86: 706–711.

Helyer, R & Lloyd, E (2009). The response to hypoxia: a refined Human Patient Simulator (HPS) model to demonstrate high altitude physiology. Proc Physiol Soc 15

Lloyd, E, Helyer, R, Dickens, P & Harris, JR (2008). Use of a high fidelity Human Patient Simulator to demonstrate the control of ventilation
Proc Physiol Soc 11

Curran-Everett, D (2006). A classic learning opportunity from Fenn, Rahn, and Otis (1946): the alveolar gas equation. Adv Physiol Educ. 30: 58-62.

Harris, J, Helyer, R & Lloyd, E (2011). Using high-fidelity human patient simulators to
teach physiology. Med Educ. 45: 1131–1162.

Hyatt, JP & Hurst, SA. (2010). Novel undergraduate physiology laboratory using a human patient simulator. Med Educ. 44:523

Zvara, DA, Olympio, MA & MacGregor, DA (2001). Teaching cardiovascular physiology using patient simulation. Acad Med. 76:534.

Waite, GN, Hughes EF, Geig, RW & Duong, T (2013). Human patient simulation to teach medical physiology concepts: A model evolved during eight years.
J. Teaching & Learning with Tech 2: 79-89.

Euliano, TY (2000). Teaching respiratory physiology: clinical correlation with a human patient simulator. J Clin Monit Comput. 16:465-70.

Euliano, TY, Caton, D, van Meurs, W & Good, ML (1997) Modeling obstetric cardiovascular physiology on a full-scale patient simulator. J Clin Monit. 13(5):293-7. 

Euliano, TY (2000). Small group teaching: clinical correlation with a human patient simulator. Adv Physiol Educ. (2001) 25:36-43.

Lampotang (2008) in Manual of Simulation in Healthcare ed. Riley, R. Oxford University Press.


Richard Helyer, PhD. School of Physiology, Pharmacology & Neuroscience, University of Bristol, Bristol BS8 1TD, UK;

What is Simulation?

What is Simulation?

That's a mighty mighty big question, too big for a blog post, or a website, or even a shelf of books to answer concisely. Best if we are a bit more specific to begin with.

What is Medical Simulation? is possibly a more realistic subject to cover in a blog post. Possibly? I doubt it, but lets keep it simple and we will be okay. Relax, you are amongst friends.

But even so, there are groups of renowned and learned experts in the field who devote much of their time to defining simulation, what it is, and what it absolutely is not, and the setting the boundaries for the sub categories of simulation, and refining their definitions, and naming and renaming the categories...

Let's just leave them to it, shall we?

Here goes...

Any simulation can be argued to be a model of a situation, whether it is a computer model of the stretching of polymers, or an improvised  role play between a group of people, or a life threatening CPR situation, or a model of the economy of a housing association etc etc etc

As the situation becomes more and more complex, so does the model, and the most important component in a model of a real life situation is not what is contained, but what is left out. What is real or real life identical, and what needs to be simulated?

Lets take an example from Medical Simulation to explain my ramblings

At my place of work, we recreate acute emergency medical situations, with effective communication being the learning goal, so what do we need for that practically speaking?

Hospital ward environment? Yes (we are based in a hospital), so no need to simulate that
Up to date, functional medical equipment? Yes (Insert own joke about state funded hospital here)
An interprofessional group featuring all the work categories? Absolutely yes
A handsome, knowledgeable simulator technician? Not a necessity, but it does help...
Real, relevant case studies? Yes
An endless supply of willing patients just waiting for us to use their illnesses and diseases for teaching purposes?....Well. No. Obviously not.

So that is the bit we simulate, often using a computer driven manikin for the participants to react to in real time, with a debriefing session afterwards.

So we are building a model of an emergency medical situation using a mixture of components that are either real, actual and identical to those used and/or using components that are simulated.

In this case, it is the patient that is simulated, and allowances are made for the fact that they don't move much, and they talk through a speaker, and they find it hard to display physical side effects and symptoms. However, the session is manned by staff skilled in facilitating teaching using simulated patient manikins.

It is VITALLY important that the simulated part of our model is not the weakest link in the chain.

Another example would be disaster/evacuation/zombie apocalypse simulation. Real buildings/environment. Real staff. Real emergency procedures to follow. Real cops/fire brigade. The zombies/poison gas/godzilla attack? Thankfully simulated. But real enough for people to feel stressed, and to react professionally.

And the point of simulation? Because it is certainly NOT a silver bullet when it comes to teaching Well, there goes another bookshelf of books but simply speaking it is this. We can recreate disease conditions, stress conditions, human feelings, triggers for alarms etc etc in such a way that the results speak to the idea that


And let us leave it at that for now.


Wednesday, 14 October 2015

Welcome to The Simulated Man - Introductions

Welcome to The Simulated Man, a blog about Medical Simulation. 

That's me on the left

In publishing The Simulated Man I am hoping to develop a network of like-minded simulation specialists and enthusiasts, provoke debate, explore ideas and raise awareness of what is still quite a specialised educational tool.

I'm Pete Dickens, that's me in the photo, just putting the finishing touches to a medical simulation in one of the operating theatres at Enköping hospital.

I've had a long and interesting journey to get here, both geographically and personally, but more on that in another post.

Behind me on the operating table lies Simon Karlsson, our patient. Sometimes Simon becomes Simona and sometimes the more gender neutral Kim. 

Does it and should it make a difference that our simulated patient has a name? A gender? If so,why? and is it significant? More on that another time...

Simon is inhabiting a Laerdal 3G patient simulator, because that's the one we chose to get the best out of the patient case we were running (and more on simulator choice in another post, so stick around!)

Okay, introductions over, there's already a lot to discuss.

Ambitiously, for our next post, I'm going to be putting an answer to the question...

What is simulation?

See you next time,