The impact of surgical simulation on patient outcomes: a systematic review and meta-analysis

Abstract

The use of simulation in surgical training is ever growing. Evidence suggests such training may have beneficial clinically relevant effects. The objective of this research is to investigate the effects of surgical simulation training on clinically relevant patient outcomes by evaluating randomized controlled trials (RCT). PubMed was searched using PRISMA guidelines: "surgery" [All Fields] AND "simulation" [All Fields] AND "patient outcome" [All Fields]. Of 119 papers identified, 100 were excluded for various reasons. Meta-analyses were conducted using the inverse-variance random-effects method. Nineteen papers were reviewed using the CASP RCT Checklist. Sixteen studies looked at surgical training, two studies assessed patient-specific simulator practice, and one paper focused on warming-up on a simulator before performing surgery. Median study population size was 22 (range 3-73). Most articles reported outcome measures such as post-intervention Global Rating Scale (GRS) score and/or operative time. On average, the intervention group scored 0.42 (95% confidence interval 0.12 to 0.71, P = 0.005) points higher on a standardized GRS scale of 1-10. On average, the intervention group was 44% (1% to 87%, P = 0.04) faster than the control group. Four papers assessed the impact of simulation training on patient outcomes, with only one finding a significant effect. We found a significant effect of simulation training on operative performance as assessed by GRS, albeit a small one, as well as a significant reduction to operative time. However, there is to date scant evidence from RCTs to suggest a significant effect of surgical simulation training on patient outcomes.

Keywords: Education; Meta-analysis; Neurosurgery; Patient outcome; Simulation; Surgery; Systematic review.

Fig. 1

PRISMA flow diagram visualizing the literature search. A total of 119 papers were identified in our literature search. One hundred of these were excluded; the remaining 19 papers were assessed for eligibility and subsequently included in qualitative analysis. Of these, seven were eligible for inclusion in our meta-analysis of global rating scores and six in our analysis of operative time

Fig. 2

Flow chart visualizing the results of included studies, including the number of papers that demonstrated a clinical effect and/or effect on patient outcomes

Fig. 3

a Forest plot of the standardized mean difference of GRS scores between control and intervention groups, including tests for heterogeneity. b Forest plot of the standardized mean difference of GRS scores between control and intervention groups with outlier removed, including tests for heterogeneity

Fig. 4

a Forest plot of the standardized mean difference of operative time between control and intervention groups, including tests for heterogeneity. b Forest plot of the standardized mean difference of operative time between control and intervention groups with outlier removed, including tests for heterogeneity