Simulate, or not to simulate?

Autori: 
Poggioli M, Rondi V, Ingrassia PL, Bazurro S, Brunetti I
Rivista / Volume: 
ICU Management & Practice
Pagine: 
18 (1); 64-66
Pubblicato il: 
01/01/2018
Tipologia pubblicazione: 
ARTICOLI NON PEER REVIEWED
Abstract: 

A brief discussion about the importance and the state of the art of simulation in anaesthesia and intensive care medicine.

Training in simulation plays a key role in complex systems such as aviation and the nuclear industry, to investigate predictable errors that lead to adverse outcomes. The advancement made by aviation integrating simulation in training over the past years is relevant, whereas in medicine simulation remains marginal, but is now rising in use. In medicine we are asking if simulation really works and if there is a place for it in medical training. Perhaps the answer is yes.

In the last few years, the use of mannequin-based simulation has become a mainstay in physician education in particular through the Basic Life Support (BLS), the Advanced Life Support (ALS) or the Advanced Trauma Life Support (ATLS) (Miyasaka et al. 2015). The American College of Critical Care Medicine recommended the use of simulation to enhance resident training in critical care (Dorman et al. 2004). Furthermore, the Institute of Medicine report To Err is Human suggested simulation training to reduce preventable errors (Kohn et al. 2000)..

In the USA about 98,000 deaths per year are due to medical errors, more than vehicle accidents, cancer or AIDS (Kohn et al. 2000). In Canada, around 7.5% of hospital admissions will result in an adverse event (Naik and Brien 2013).

So, what is our answer to this evidence?

Can we be sure that we are educating students and trainees in the most effective way possible? On the contrary, Dudeck et al. affirmed that our programmes are not always able to identify underperforming residents and that the lack of evaluating documentation leads to undefined level of competence. Too often, current trainees are assessed using poorly and non-standardised metrics (Levine and Shorten 2016; Dudeck et al. 2015).