Altermatt FR, Corvetto MA

Language: Spanish
References: 17
Page: 101-105
PDF size: 243.69 Kb.

ABSTRACT

This article seeks to reflect and analyze how technology, through the use of sensors and artificial intelligence, can find patterns of expert performance that can help us in how to train the acquisition of procedural skills. Previous research has used the expert performance approach, described by Ericsson, to evaluate these patterns as performance indicators, as traits of expert performance as opposed to that of an inexperienced operator, with the objective of evaluating the progression of skill acquisition during learning.

REFERENCES

1. Ericsson KA, Whyte J 4th, Ward P. Expert performance in nursing: reviewing research on expertise in nursing within the framework of the expert-performance approach. ANS Adv Nurs Sci. 2007; 30 (1): E58-71.

2. Ericsson KA, Lehmann AC. Expert and exceptional performance: evidence of maximal adaptation to task constraints. Annu Rev Psychol. 1996; 47: 273-305.

3. Williams AM, Ericsson KA. Perceptual-cognitive expertise in sport: some considerations when applying the expert performance approach. Hum Mov Sci. 2005; 24 (3): 283-307.

4. Villagrán I, Moenne-Loccoz C, Aguilera V, García V, Reyes JT, Rodríguez S, et al. Biomechanical analysis of expert anesthesiologists and novice residents performing a simulated central venous access procedure. Plos One. 2021; 16 (4): e0250941.

5. Wallace WF. Roger Federer as Religious Experience - Tennis - The New York Times. The New York Times [Internet]. 2006. Available in: https://www.nytimes.com/2006/08/20/sports/playmagazine/20federer.html

6. Barach P, Berwick DM. Patient safety and the reliability of health care systems. Ann Intern Med. 2003; 138 (12): 997.

7. Ericsson K, Smith J. Prospects and limits of the empirical study of expertise: an introduction. In: oward a general theory of expertise: prospects and limits (pp 1-38) Cambridge University Press. Cambridge University Press; 1991. p. 1-38.

8. Matthew B. Weinger, David M. Gaba; Human factors engineering in patient safety. Anesthesiology. 2014; 120: 801-806.

9. Lira R, Salas-Morales J, Leiva L, Fuente R de la, Fuentes R, Delfino A, et al. Process-oriented feedback through process mining for surgical procedures in medical training: the ultrasound-guided central venous catheter placement case. Int J Environ Res Pu. 2019; 16 (11): 1877.

10. Causer J, Barach P, Williams AM. Expertise in medicine: using the expert performance approach to improve simulation training. Med Educ. 2014; 48 (2): 115-123.

11. Williams AM, Ericsson KA, Ward P, Eccles DW. Research on expertise in sport: implications for the military. Mil Psychol. 2008; 20 (Suppl. 1): S123-145.

12. McGaghie WC, Issenberg SB, Petrusa ER, Scalese RJ. Effect of practice on standardised learning outcomes in simulation-based medical education. Med Educ. 2006; 40 (8): 792-797.

13. Issenberg SB, Mcgaghie WC, Petrusa ER, Gordon DL, Scalese RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach. 2009; 27 (1): 10-28.

14. Corvetto MA, Pedemonte JC, Varas D, Fuentes C, Altermatt FR. Simulation-based training program with deliberate practice for ultrasound-guided jugular central venous catheter placement. Acta Anaesth Scand. 2017; 61 (9): 1184-1191.

15. Winkler-Schwartz A, Yilmaz R, Mirchi N, Bissonnette V, Ledwos N, Siyar S, et al. Machine learning identification of surgical and operative factors associated with surgical expertise in virtual reality simulation. JAMA Netw Open. 2019; 2 (8): e198363.

16. Shorten G. Artificial intelligence and training physicians to perform technical procedures. JAMA Netw Open. 2019; 2 (8): e198375.

17. Lam K, Chen J, Wang Z, Iqbal FM, Darzi A, Lo B, et al. Machine learning for technical skill assessment in surgery: a systematic review. Npj Digital Medicine. 2022; 5: 24.