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 Table of Contents  
LETTER TO EDITOR
Year : 2018  |  Volume : 4  |  Issue : 3  |  Page : 303-305

Simulation-based medical education and effective staffing ratios


Lloyd A. Jacobs Interprofessional Immersive Simulation Center, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA

Date of Web Publication24-Dec-2018

Correspondence Address:
Dr. Thomas J Papadimos
Lloyd A. Jacobs Interprofesisonal and Immersive Simulation Center, Department of Anesthesiology, University of Toledo College of Medicine and Life Sciences, 3000 Arlington Avenue Toledo, OH, 43614
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAM.IJAM_1_18

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How to cite this article:
Chen Y, Pappada SM, Papadimos TJ. Simulation-based medical education and effective staffing ratios. Int J Acad Med 2018;4:303-5

How to cite this URL:
Chen Y, Pappada SM, Papadimos TJ. Simulation-based medical education and effective staffing ratios. Int J Acad Med [serial online] 2018 [cited 2019 Jan 18];4:303-5. Available from: http://www.ijam-web.org/text.asp?2018/4/3/303/248323



Simulation-based medical education (SBME) is a growing sector of medical education.[1] SBME is popular and effective, and nearly all institutions/organizations are engaged in its use.[2] One year ago, this journal published an symposium on SBME; however, in none of those papers was the issue of staffing addressed. The question that arises in these days of scarce academic resources is, “what is the correct ratio of simulation technicians to learners?” During the fall of 2017, we contacted 30 Society for Simulation in Healthcare-certified simulation centers around the country through a simple phone survey regarding their estimated number of learners and number of simulation technicians (ratio), of which only 12 could respond (40%) [Table 1]. The average number of simulation staff per center was 8, with a total average of 24,732 estimated learners per year, resulting in 3080 learners per simulation staff (we eliminated Loma Linda because of incomplete data). Some centers have a number of part-time simulation technicians or faculty that can run simulations, so this ratio may be difficult to apply across all institutions because of the complexity and variability of each institution's programs and training model, as well as the limited response to this survey. Each institution will have different needs, and the depth or fidelity of the scenarios used will vary considerably.[3],[4]
Table 1: Simulation center phone survey

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From these data, our simulation center, which does perform surgical and cadaveric simulations, and is a very high-fidelity center, seems to be understaffed. We propose that a center such as ours, which has 32,000 learners (a staff of four technicians or 1/8000 learners), should have a staff of 1/3000 learners at a minimum. Our institution requires 10.4 simulation technicians to keep up with this number of learners when compared to other efforts nationally. We defined simulation staff as anyone within their job description or with the capability to run clinical simulations. This does not include coordinators, tech support, and administration staff. Please also note that some of the higher learner volume centers have many faculty or focus mainly on standardized patient simulation, thereby effectively moving more learners through the scenarios with higher volume than those using manikins and surgical or cadaveric simulation (which require considerably more effort).[5] In other words, highly technologically advanced simulation laboratories/centers would require more information technology staff and as well as the same number, or even more, of simulation instructors. The creation and running of scenarios in high-fidelity simulation requires that the simulation technicians be more “tech savvy,” and this will not lessen the numbers needed. This becomes a cost issue to many institutions. This requirement for high-fidelity scenarios and increased numbers of support staff falls into the need to have health-care providers to be “operationally ready.” Operationally ready can be defined as being equipped with a sufficient level of preparedness to perform at a desirable level in real-world settings [Figure 1]a. Training should focus on establishing a desirable mix of cognitive, behavioral, and psychomotor skills. The appropriate mix is specific to roles and situations or scenarios. High-fidelity scenarios require workforce, especially in today's SBME environment where we are now actually trying to “measure it all.” Recently, a study examining the relationship between trauma volume and mortality suggested that an institution's operational readiness is associated with lower mortality because of high trauma volume.[6] This is a situation where high-fidelity SBME could play a major role. Training and re-training in simulation could better ready those in low-volume institutions, while making those in high-volume institutions even more keen in their skills.
Figure 1: Operational readiness. To be “operationally ready,” providers must be equipped with a (a) sufficient level of preparedness to perform at a desirable level in real-world settings; along with a (b) comprehensive measurement and assessment of approach to simulation-based medical education. There is a confluence between the learner level, the instructor level, learning the operational environment, and the pre- and posttraining levels

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This comprehensive measurement and assessment approach to SBME is important [Figure 1]b. There are four overlapping levels/segments to this approach. The learner level requires measures derived from the perspective of, or directly from, the trainee through physiological data and responses collected throughout participation in SBME or other educational activities, and through self-reports, survey data, etc. The instructor level includes measures acquired from observations made by an expert observer during SBME. The learning of the operational environment level requires data and measures collected directly from a training/simulation environment such as interactions with patient simulators and patient monitoring equipment. And finally, there is the pre- and posttraining level. Here, measures are obtained before and after training during preassessment tests, learner debriefing, posttraining assessment tests, measurements/evaluations of real-world on-the-job performance, and/or patient outcomes and results of care provided by learners/trainees.

Understaffing is a serious problem and may result in cancelled and/or unproductive sessions, thereby potentially affecting the competency of learners, creating consternation among institution leaders and department chairs, as well as learners. It also leads to lost revenues and dissatisfaction in simulation staff because of the longer work hours necessary to complete the mission.

The authors understand the weakness of this survey presented, but it is an initial effort to determine staffing needs in the SBME arena. We encourage our colleagues who are involved in SBME to create a schema of personnel staffing needs based on complexity and use through thorough cooperative, multi-institutional workforce studies in order to present health-care campus administrations with national (possibly international) efforts or investigations that are evidence based, thus creating a repository of evidence-based staffing models and standard that can be shared among institutions and acted upon in a coherent manner, educationally and economically.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
McGaghie WC, Issenberg SB, Cohen ER, Barsuk JH, Wayne DB. 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 2011;86:706-11.  Back to cited text no. 1
    
2.
Lyons R, Lazzara EH, Benishek LE, Zajac S, Gregory M, Sonesh SC, et al. Enhancing the effectiveness of team debriefings in medical simulation: More best practices. Jt Comm J Qual Patient Saf 2015;41:115-25.  Back to cited text no. 2
    
3.
Jenkins KD, Stroud JM, Bhandary SP, Lynem L, Choi M, Quick J, et al. High-fidelity anesthesia simulation in medical student education: Three fundamental and effective teaching scenarios. Int J Acad Med 2017;3:66-71.  Back to cited text no. 3
  [Full text]  
4.
Bhandary SP, Lipps JA, Ramadan ME, Jenkins KD, Stroud JM, Papadimos TJ. Scenario development strategies and process for simulation-based education in anesthesiology. Int J Acad Med 2017;3:72-7.  Back to cited text no. 4
  [Full text]  
5.
Maran NJ, Glavin RJ. Low- to high-fidelity simulation – A continuum of medical education? Med Educ 2003;37 Suppl 1:22-8.  Back to cited text no. 5
    
6.
Stawicki SP, Habeeb K, Martin ND, O'Mara MS, Cipolla J, Evans DC, et al. Aseven-center examination of the relationship between monthly volume and mortality in trauma: A hypothesis-generating study. Eur J Trauma Emerg Surg 2018;Jan 12 [Epub ahead of print].  Back to cited text no. 6
    


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