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VR and AR Simulation Medical Training Reduces Medical Errors
Adam Dougherty, MD, MPH
Chief Medical Officer, SimX

Human error is an inevitable event in the practice of any trade, including healthcare and medicine. The errors made in medicine can have many negative effects for patients and healthcare providers. A study out of Johns Hopkins in 2016 found that medical errors accounted for over 250,000 deaths annually in the United States – making it the third leading cause of death in the country (Makary 2016). Research has shown that most medical errors occur during the administration and prescribing stages of healthcare (Durham 2008). Many errors can be reduced in frequency with training, education, and practice. This makes the use of medical simulation-based training a valuable tool.

Perhaps one of the first industries to demonstrate that simulation training can reduce error was the aviation industry. The first aviation simulator was purchased and implemented by the Army in the 1930s following some fatal accidents during training. This revolutionized the training of new pilots in terms of both safety and cost. In the 1960s, The National Aeronautics and Space Administration (NASA) further advanced the use of simulators for flight training, making the first fully digital simulators for the Apollo missions.  While these efforts were costly upfront, they eventually became a cost-effective way to improve the outcomes for safety and training.

The medical industry is another trade that uses simulation training to improve training and safety outcomes. The use of medical simulation has evolved greatly over time. Medical simulation has been practiced for centuries using things like anatomical models built out of clay (Rosen 2008). The first simulation mannequin made its debut in 1911. It was a primitive model, but it paved the way for the invention of computerized models later in the twentieth century. Fast forward to today and healthcare providers are now training with virtual reality (VR) and augmented reality (AR) simulations. The possibilities for the simulations that can be created with VR and AR are endless. Some of the options available for VR and AR simulation medical training include:

– Rehearsing and practicing surgery

– Exploring anatomy and studying the human body

– Practicing patient interactions and exams

– Rehearsing high-pressure and high-risk situations such as resuscitation

– Completing training such as advanced life support (ALS) or pediatric advanced life support (PALS)

Studies have also shown that the cost of VR and AR training can be less expensive when compared to mannequin simulation (Haerling 2018). This may partly be due to the fact that VR and AR eliminate the need for faculty and laboratory staff, physical facilities, durable equipment, consumable supplies, and other supplies and personnel to set the scene (Haerling 2018). Therefore, VR and AR simulation may represent a more cost-effective approach toward training healthcare professionals.

Virtual and augmented reality simulations make it possible for healthcare providers to practice rare and dangerous scenarios. These include emergency and critical care situations. This platform also allows healthcare providers to learn from the mistakes that they make without distressing an actual patient. It has been demonstrated that users can learn important skills during simulation, including teamwork, leadership, interpersonal communication, decision-making, prioritizing, and stress management (Flanagan 2004).

But what about the reduction of medical errors with simulation training? A meta-analysis of the relationship between medical error and simulation training has revealed that simulation can reduce medical error and prevent some risks related to medical treatment (Sarfati 2019). It has also been demonstrated that simulation-based learning leads to a reduction in the rate of medication errors by nurses working with critically ill patients (Ford 2010). Other studies have evaluated the impact of medical simulation training and shown that the skillset of the healthcare provider improved, thus reducing the potential for future medical errors. Examples of these findings include:

– Improvement of physician’s performance with mechanical ventilation and hemodynamic monitoring following simulation training (Havaldar,2020).

– Improvement of objective skills in practitioners working in emergency situations or surgical intensive care unit (Pascual 2011).

– Improved preparedness, comfort, and decreased anxiety among multidisciplinary resuscitation teams in the pediatric intensive cardiac unit (Alan, 2010).

– Safety improvements during labor and delivery following simulation training (Phipps 2012).

These improvements translate to decreased medical error and better outcomes for patients. The use of medical simulation can improve the quality of care delivered by healthcare providers, and virtual and augmented reality medical simulation may represent the future of training in healthcare. It is cost-effective and has great potential for creating a wide variety of training scenarios. One of the major goals of medical simulation training is to reduce the number of medical errors made by healthcare workers, and medical simulation training has a proven record of enhancing patient safety and reducing medical errors.


About Adam Dougherty

Adam Dougherty, MD, MPH is Chief Medical Officer of SimX, a comprehensive professional-grade VR medical simulation system used to train physicians, nurses, and other allied health professionals.


References

Allan CK, Thiagarajan RR, Beke D, Imprescia A, Kappus LJ, Garden A, Hayes G, Laussen PC, Bacha E, Weinstock PH. Simulation-based training delivered directly to the pediatric cardiac intensive care unit engenders preparedness, comfort, and decreased anxiety among multidisciplinary resuscitation teams. J Thorac Cardiovasc Surg. 2010 Sep;140(3):646-52. doi: 10.1016/j.jtcvs.2010.04.027. Epub 2010 Jun 8. PMID: 20570292.

Durham CF, Alden KR. Enhancing Patient Safety in Nursing Education Through Patient Simulation. In:Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Chapter 51. Available from: https://www.ncbi.nlm.nih.gov/books/NBK2628/

Flanagan B, Nestel D, Joseph M. Making patient safety the focus: crisis resource management in the undergraduate curriculum. Med Educ. 2004 Jan;38(1):56-66. doi: 10.1111/j.1365-2923.2004.01701.x. PMID: 14962027.

Ford DG, Seybert AL, Smithburger PL, Kobulinsky LR, Samosky JT, Kane-Gill SL. Impact of simulation-based learning on medication error rates in critically ill patients. Intensive Care Med. 2010 Sep;36(9):1526-31. doi: 10.1007/s00134-010-1860-2. Epub 2010 Mar 19. PMID: 20300731.

Haerling KA. Cost-Utility Analysis of Virtual and Mannequin-Based Simulation. Simul Healthc. 2018 Feb;13(1):33-40. doi: 10.1097/SIH.0000000000000280. PMID: 29373382.

Havaldar AA, Krishna B, Sampath S, Paramasivam SK. Simulation Training in Hemodynamic Monitoring and Mechanical Ventilation: An Assessment of Physician’s Performance. Indian J Crit Care Med. 2020 Jun;24(6):423-428. doi: 10.5005/jp-journals-10071-23458. PMID: 32863635; PMCID: PMC7435101.

Makary MA, Daniel M. Medical error-the third leading cause of death in the US. BMJ. 2016 May3;353:i2139. doi: 10.1136/bmj.i2139. PMID: 27143499.

Pascual JL, Holena DN, Vella MA, Palmieri J, Sicoutris C, Selvan B, Fox AD, Sarani B, Sims C, Williams NN, Schwab CW. Short simulation training improves objective skills in established advanced practitioners managing emergencies on the ward and surgical intensive care unit. J Trauma. 2011 Aug;71(2):330-7; discussion 337-8. doi: 10.1097/TA.0b013e31821f4721. PMID: 21825935.

Phipps MG, Lindquist DG, McConaughey E, O’Brien JA, Raker CA, Paglia MJ. Outcomes from a labor and delivery team training program with simulation component. Am J Obstet Gynecol. 2012 Jan;206(1):3-9. doi: 10.1016/j.ajog.2011.06.046. Epub 2011 Jun 21. PMID: 21840493.

Rosen KR. The history of medical simulation. J Crit Care. 2008 Jun;23(2):157-66. doi: 10.1016/j.jcrc.2007.12.004. PMID: 18538206.

Sarfati L, Ranchon F, Vantard N, Schwiertz V, Larbre V, Parat S, Faudel A, Rioufol C. Human-simulation-based learning to prevent medication error: A systematic review. J Eval Clin Pract. 2019 Feb;25(1):11-20. doi: 10.1111/jep.12883. Epub 2018 Jan 31. PMID: 29383867.


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