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. 2025 Nov 11;21(4):231–240. doi: 10.1097/SIH.0000000000000895

Competency-Based Simulation Training for Procedural Skills: A Systematic Review and Meta-analysis

Julia H Vermylen 1, Elaine R Cohen 1, David A Cook 1, William C McGaghie 1, S Barry Issenberg 1, Jason Arnold 1, Heather Ballard 1, Mohammad Bayoumi 1, Molly Beestrum 1, Remy Bremner 1, Scott Crawford 1, Noah Einstein 1, Candace Mannarino 1, Asit Misra 1, Tadaki M Tomita 1, Heidi Waldron 1, Frank Yanko 1, David O Kessler 1
PMCID: PMC13412366  PMID: 41217357

Summary Statement:

We conducted a systematic review of competency-based simulation education for medical procedural skills, encompassing a diverse range of healthcare domains and professions. Meta-analysis showed competency-based simulation is more effective than noncompetency-based simulation for skill outcomes (large effect). Outcomes are favorable, but small, for behaviors in practice and patient effects. Specific instructional design features examining different simulators and different types and sources of feedback were common themes identified among studies though no instructional design features were found to have significant impact on skill acquisition. This review also identified significant gaps in research including a lack of standardized terminology and reporting methods as well as a limited number of studies assessing the impact on behaviors in practice and patient effects. This study contributes to the ongoing evidence to support competency-based simulation training and highlights the need for further research to enhance the efficacy and efficiency of training of healthcare professionals.

Key Words: Simulation, deliberate practice, mastery learning, proficiency, proficiency-based training, competency, psychomotor skills

Demonstrating competency in procedural skills in a simulated setting before performing them on patients is increasingly viewed as an ethical imperative in medical education.1,2 However, limited teaching time, patient care priorities, financial disincentives, and education inertia grounded in the 19th century apprenticeship model of medical education makes it challenging to accomplish this for all requisite psychomotor skills.3 New approaches to medical education that include measurable learning objectives, powerful in situ and simulation learning experiences, rigorous assessment with feedback, and multiple opportunities for certification evaluation are now taking root that hold promise for improved clinician competence and professional accountability.4

Competency-based simulation is an outcome-based approach to medical education that focuses on tailoring simulation to students' demonstrated needs and holds promise for improved clinician competence and professional accountability.4,5 This endorsement aligns with the recommendations and policies of accrediting organizations, and highlights that merely allocating time for simulation in training programs is not sufficient to guarantee effective learning or skill acquisition. Competency-based simulation for procedural training is generally considered to be superior to other pedagogical simulation approaches yet few pre-licensure programs or postgraduate onboarding programs have rigorous methods to include competency-based simulation within their program. Additionally, the empirical evidence to support competency-based simulation over other types of simulation-based medical education has not recently been summarized.6 Consistently, systematic reviews of competency-based simulation training for procedural skills have demonstrated a spectrum of translational outcomes from the educational setting to patient outcomes and collateral health system effects, though frequently, systematic reviews did not compare competency-based simulation training programs to noncompetency-based simulation training programs.7–9

Additionally, there remain significant gaps in our understanding about the best instructional design features needed to produce procedural skill competency efficiently and effectively. Systematic reviews have not compared different design approaches within competency-based training (such as different competency threshold designs, simulator features, time on task, instructor experience, and the mode and timing of feedback) to understand the differential impacts on educational and patient outcomes.7–9 Synthesis of the evidence and specific head-to-head comparison of different approaches is necessary to help establish which competency-based simulation strategies are the best to drive future curricular approaches.

To address these gaps, we conducted a systematic review and synthesis of the literature to better understand (1) the impact of competency-based simulation in comparison to noncompetency-based simulation and (2) the impact of different design variables on learning effectiveness and efficiency.

METHODS

This study was planned, conducted and reported in adherence to preferred reporting items for systematic reviews and meta-analyses standards of quality for reporting meta-analysis.10

Study Identification

We systematically reviewed MEDLINE, Embase, Cochrane Library, CINAHL, PsycINFO,

Scopus, and Web of Science from inception to March 9, 2022 following a search strategy designed by 2 librarians with expertise in systematic reviews.11 This study was done in preparation for the Society for Simulation in Healthcare's 2023 research summit in January 2023. Articles were collected only until March due to time constraints to prepare initial results for the Summit.

We used an intentionally broad search strategy to capture the spectrum of competency-based simulation for procedural skill training (see Text, Supplemental Digital Content 1, http://links.lww.com/SIH/B209, which includes the full PUBMED search). We used search terms focused on the educational model (eg, competency, feedback, mastery learning, deliberate practice), simulation (simulation, augmented reality, cadaver, manikin), and health professions (eg, health personnel or medical-professional, physician, nurse, resident, etc.).

Study Eligibility

We included all original research studies that compared competency-based simulation training in psychomotor skills for health professionals versus another simulation training intervention. We required that the simulation modality include a psychomotor component. If the simulation involved augmented or virtual reality, it also had to include an element of psychomotor experience (eg, haptic feedback). To meet the broad definition of competency-based simulation training, the intervention had to include a baseline measure of skill performance, a simulation training intervention, and a clearly defined competency threshold. Actual achievement of competency was not required. We included both randomized and nonrandomized comparisons. We defined health professionals to include any provider or trainee who cared for human patients (ie, not veterinarians). Studies without English translations available in full-text were excluded at the full-text review step. There were no exclusions based on the date of publication.

Study Selection

All studies identified in the search were screened for inclusion using titles and abstracts via Abstrackr.12 Potentially eligible articles were then migrated into Covidence systematic review software,13 for full-text review to determine final inclusion. Articles were independently reviewed by 2 authors at each stage. Disagreements were passed onto a final round for additional review with the final decision adjudicated by a third reviewer.

Data Extraction

A data abstraction form was developed through iterative testing and revision. Pairs of authors independently abstracted data in duplicate from each included study into Excel (Microsoft, 16.90) and later met with a larger team to resolve any conflicts by consensus review.

We abstracted data on the study design (eg, randomization, learning curve), setting and location of study (simulation center or clinical practice setting), quantitative data on study participants (number enrolled, clinical background, training level), clinical skill domain, method of group allocation, type of competency instructional method (mastery learning, proficiency-based training, rapid cycle deliberate practice, or other), method of competency assessment, simulation modality (animal, manikin, task trainer, virtual or augmented reality), level of supervision, how assessment guided feedback, curricular integration, and consequences of not meeting competency threshold (eg, remediation, failure to graduate, none).

Methodologic quality was graded using the Medical Education Research Study Quality Instrument (MERSQI) and the Newcastle-Ottawa Scale-Education (NOS-E).14 These instruments are complementary tools for assessing the quality of medical education research and include assessment of study design, sampling, validity evidence, level of outcomes, controlling for confounding variables, and blinding.

For each study, we extracted up to 7 outcomes, organized according to Kirkpatrick's hierarchy as previously operationalized by Cook, namely, skill (in the simulation setting), behavior (in clinical practice), and patient effect.11,15 Outcomes within the simulation setting were further classified into categories of skill time (how long it took to perform a skill), nont.ime skill process (how well a task is completed for example a global rating scale), and skill product (results of an observable task such a knot integrity). Behaviors with patients were similarly classified into time and nontime processes; clinical “products” were considered patient effects.

Our main analysis compared competency-based versus noncompetency-based simulation training. To identify and explore variables contributing to the effectiveness and efficiency of competency-based simulation, we inductively analyzed articles for comparisons of different instructional designs (between-group differences in study exposures, whether planned or unplanned) and clustered these conceptually into research themes. We broke these design variables into the following 4 main categories: (1) instructional strategies such as the timing of feedback, type of feedback, or intensity of training, (2) simulation model variables such as studies comparing various types of simulators or cost of simulators, (3) learner engagement such as studies testing motivation or type of learner, and (4) assessment variables such as those assessing various minimum passing standards, validity constructs, or timing within a curriculum. Using this framework, we identified salient research themes of articles that compared similar design features.

Data Synthesis

We pooled results using random effects meta-analysis for all identified salient themes that had more than 5 studies. For each study outcome, a Cohen d standardized means difference (SMD) was calculated. We used the mean and standard deviation when possible; if not, the odds ratio or the results of statistical tests (P value, T statistic) were used. For 2-group pretest-posttest studies where the study reported both the postintervention outcome and a preintervention assessment outcome, the posttest means adjusted for pretest or adjusted statistical test results were used. If an article contained insufficient information to calculate an effect size, the study was excluded from the meta-analysis. When a given study reported >1 measurement for a given outcome, we selected 1 measurement prioritized by: 1) the primary outcome defined by the author or 2) the outcome measure deemed most clinically relevant as deemed by the author if not present by the reviewer or 3) global measure if present or 4) the mean of all reported measures within that category. When abstracting data from learning curves, the first time point was used as the pretest and the last reported time point as the posttest assessment. For studies reporting >1 type of outcome, we selected 1 outcome for a “best outcome” analysis, prioritized by: 1) the primary outcome as defined by the study; or when not defined or multiple primary outcomes stated, 2) the outcome category deemed the most clinically relevant.

To quantify the percentage of variability across studies not due to chance, we used I 2; values >50% indicate large inconsistency.16 We planned sensitivity analysis excluding nonrandomized studies and studies for which SMDs were approximated using imprecise P values. We conducted planned subgroup analyses based on total quality score (high vs low MERSQI and NOS-E) and selected instructional design features such as time on task for intervention compared with the control group.

We used SAS 9.4 (SAS Institute, Cary, NC) for all analyses. Statistical significance was defined by a 2-sided alpha of 0.05, and interpretations of clinical significance emphasized confidence intervals in relation to Cohen d effect size classifications (<0.2 = negligible, 0.2–0.49 = small, 0.5–0.79 = moderate, and >0.8 = large).17

RESULTS

We identified 48,638 articles of which 48 (0.1%) met the final criteria for inclusion (see Fig. 1).18–65

FIGURE 1.

FIGURE 1

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Trial flow diagram in the systematic review and meta-analysis on competency-based simulation training for procedural skills.

Study Characteristics

Characteristics of included studies are shown in Table 1. The 48 studies included 2058 participants with a median of 35 [interquartile range (IQR) = 20.5, 44.5] participants included in each study (see Table, Supplemental Digital Content 2, http://links.lww.com/SIH/B210, which provides further details for all included studies). Most studies included physicians in postgraduate training (n = 25, 52%) or medical students (n = 20, 42%). Some studies included more than 1 type of participant (eg, nurses and medical students). The most common clinical skill domains taught were minimally invasive surgery (n = 18, 38%), resuscitation skills (9, 19%), surgical knot tying or suturing (6, 13%), bedside procedures (4, 8%), and robotic surgery (4, 8%).

TABLE 1.

Characteristics of the Studies Included in the Systematic Review and Meta-analysis of Literature on Competency-Based Simulation Training for Procedural Skills

Study Characteristic Level No. Studies No. Participants
All studies 48 2058
Group allocation Nonrandomized 4 132
Randomized 44 1926
Study design Pretest-posttest 36 1661
Learning curve 8 299
Both 4 98
Participants* Medical students 20 937
Nurses 5 309
Physicians in postgraduate training 25 1033
Physicians in practice 1 123
Other 4 220
Clinical skill domain Bedside procedures 4 154
Endoscopy/colonoscopy 3 71
Resuscitation skills 9 493
Minimally invasive surgery 18 615
Open surgery 2 80
Robotic surgery 4 221
Surgical knot tying/suturing 6 257
Other 2 167
Location Practice setting 2 125
Simulation center 43 1763
More than 1 location 3 170
Simulation modality* Animal model 6 171
Manikin or task trainer 37 1704
Virtual or augmented reality 17 629
Instructional design Mastery learning 6 219
RCDP 6 298
Proficiency-based training 30 1271
Other 6 270
Method of competency assessment Automated measure 19 750
Combined checklist and global rating scale 11 429
Critical skill checklist 15 639
Global rating scale 1 70
One or more successful procedures 2 170
Level of supervision 1:1 learner to trainer ratio 17 731
1–5 learners to 1 trainer 6 212
5–10 learners to 1 trainer 1 30
10–15 learners to 1 trainer 1 42
>15 learners to 1 trainer 1 20
None (eg, automated or self) 13 597
Unknown/not reported 9 426
Baseline assessment for feedback No 25 1013
Yes 23 1045
Consequences of not meeting competency threshold None 20 901
Low-stakes (more training time) 27 1133
Medium (remediation scheduled) 1 24
Mandatory part of curriculum No 29 1355
Yes 12 446
Unknown/not reported 7 257
Primary outcome Skill: time 16 665
Skill: nontime process 11 492
Skill: product 14 609
Behavior 4 88
Patient effect 3 204
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*Indicates that in this field, studies may have fallen into more than 1 category and thus the subtotals added together will be more than 100%.

Most interventions took place only within the simulation center (43, 90%). Most studies used a manikin or task trainer (37, 77%), while 17 (35%) used virtual or augmented reality and 6 (13%) used an animal model.

The most common instructional design category was proficiency-based training (30, 63%) followed by mastery learning (6, 13%) and rapid cycle deliberate practice (6, 13%). Six (13%) of the included studies did not meet the criteria for mastery learning, proficiency-based training, or rapid cycle deliberate practice.

For assessment, 19 studies (40%) used an automated measure (such as the score from a virtual reality simulator), 15 (31%) used a checklist score, and 11 (23%) used a combined checklist and global rating scale. Twenty-three (48%) studies provided feedback to the learners following the baseline assessment and 28 (58%) of the studies required learners to do additional training or remediation if the competency threshold was not achieved at posttest assessment. Only 12 of the studies (25%) had curricular integration (ie, in which the simulation training was a required part of the curriculum).

The majority of studies had a skill outcome as the primary outcome (41, 85%). Four studies (8%) reported a behavior outcome and 3 (6%) reported a patient effect outcome.

Study Quality

Table 2 summarizes the methodologic quality (risk of bias) of the included studies. Nearly all studies were single institution studies (43, 90%) and most had ≥75% response rate (39, 81%). All included studies were a 2-group design. Most studies (44, 91.7%) used randomization to assign groups. Thirty-six (75%) were pretest-posttest design studies and 12 (25%) included a learning curve defined as 3 or more measurement time points. We found at least 1 outcome with objective data assessment in all 48 (100%) of studies. Validity evidence for assessment scores was reported for content in 45 (94%) studies, internal structure in 23 (48%) and relation to other variables in 34 (71%). Overall (MERSQI) study quality scores ranged from 11.5 to 16.5 [median = 14.5 (IQR = 13.5–15.5) of 18 possible]. Overall (NOS-E) study quality scores ranged from 4 to 6 [median = 5 (IQR = 4–5)].

TABLE 2.

Quality of Studies Included in the Systematic Review and Meta-analysis of Literature on Competency-Based Simulation Training for Procedural Skills

Scale Item Subscale (Points if Present) No. (%) Present
N = 48
MERSQI
Study design (maximum 3) One-group pre-post (1.5) 0
Observational 2-group (2) 4
Randomized 2-group (3) 44
Sampling: no. institutions (maximum 1.5) 1 (0.5) 43
2 (1) 0
>2 (1.5) 5
Sampling: response rate (maximum 1.5) <50% or not reported (0.5) 1
50%–74% (1) 8
≥75 (1.5) 39
Type of data: outcome assessment (maximum 3) Subjective (1) 0
Objective (3) 48
Validity evidence (maximum 3) Content (1) 45
Internal structure (1) 23
Relations to other variables (1) 34
Data analysis: appropriate (maximum 1) Appropriate (1) 48
Data analysis: sophistication (maximum 2) Descriptive (1) 3
Beyond descriptive analysis (2) 45
Highest outcome type (maximum 3) Knowledge, skills (1.5) 39
Behaviors (2) 3
Patient/health care outcomes (3) 6
Newcastle-Ottawa Scale (modified)*
Representativeness of sample Present (1) 35
Comparison group from same community Present (1) 48
Comparability of groups on the basis of the design and analysis (max. 2) Present (1) 16
Present (2) 32
Blinded outcome assessment Maximum (1) 30
Follow-up high Maximum (1) 40
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Research Themes

Eleven articles (23%) compared competency-based simulation versus noncompetency-based simulation. Additionally, we empirically identified the following 4 themes of instructional design variables the articles assessed: (1) instructional strategies, (2) simulation models, (3) learner engagement, and (4) assessment models (see Table 3 for a complete list of themes). Thirty-seven (77%) articles compared different instructional strategies for competency-based simulation training, including 7 articles comparing types of feedback (automated vs nonautomated) and 7 articles comparing sources of feedback (self-directed vs instructor-guided). Ten (48%) articles focused on comparing the simulation model. Some studies had active comparators across multiple instructional design features. For example, we classified a study that used a low-fidelity model used at home without an instructor present compared with a high-fidelity model used in a simulation center with an instructor present as comparing different instructional strategies (type of feedback and source of feedback) and different simulation models (type, cost, and fidelity).

TABLE 3.

Instructional Design Variables for Studies Included in the Systematic Review and Meta-analysis of Literature on Competency-Based Simulation Training for Procedural Skills

Major Variable Subdomain No. Studies
Instructional strategy CONCEPTUAL APPROACH (mastery, proficiency)
TASK COMPLEXITY (adaptive difficulty, progressive skills)
INTENSITY (dose, frequency, strength of deliberate practice)
TIMING (distribution intervals, proximity to clinical)
INSTRUCTOR (self, peer, expertise, trained)
FEEDBACK (source, intensity, timing, automation)
COMPETENCY THRESHOLD (source, integration, stakes)
COGNITIVE ADJUNCTS (mental rehearsal, environment)
SUSTAINABILITY (integration, mandates, consequences)		 37
Simulation model TYPE (animal, manikin, virtual, hybrid, partial/whole task)
COST (value, sustainability)
FIDELITY (physical, psychological, haptic, gamification)
ALIGNMENT (tailored to learner, skill, stakes, setting)		 10
Learner engagement TYPE (learner level, individual vs teams, interprofessional)
STAKES (voluntary, mandatory, licensure, privileges)
MOTIVATION (intrinsic/extrinsic factors, incentives, context)
EFFICIENCY (time, cost, resource, trade-offs)		 3
Assessment DEFINED OUTCOMES WITH MINIMUM PASSING STANDARD (derivation, evidence level, relevance)
SOURCE (expert, peer, system, self)
VALIDITY (data driven, reliable data, task germane, clinical transfer)
TIMING (immediate, post, delayed, throughout)
CONTINUITY (retention built in, used throughout curriculum)		 2
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Quantitative Synthesis

Comparison of Competency to noncompetency Simulation Training

Eleven articles reported 12 studies comparing a competency-based simulation to a noncompetency-based simulation (1 article reported 2 studies).18 Study characteristics for each study are shown in Figure 2 and 3. We report first a pooled analysis using the best outcome from each study (Fig. 2) and then a pooled analysis for each Kirkpatrick level separately (Fig. 3).

FIGURE 2.

FIGURE 2

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Forest plot for the best outcome for competency versus noncompetency meta-analysis on competency-based simulation training for procedural skills.

FIGURE 3.

FIGURE 3

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Forest plot of all outcomes for the competency versus noncompetency meta-analysis on competency-based simulation training for procedural skills.

Meta-analysis of the best outcome for each of these 12 studies shows improved performance among participants trained in a competency-based simulation when compared with a noncompetency-based simulation (pooled SMD = 0.67, 95% CI = 0.27 to 1.07, P < 0.001) as shown in Figure 2. This represents a moderate effect size.

All but 2 studies favored competency-based simulation training. Among the 2 articles that did not favor competency, the first article was a booster training after an initial competency-based simulation training and the second was a study that showed poor transfer of skills for both arms of the study.

Inconsistency (heterogeneity) was high between the studies (I 2 = 77%) with effect sizes ranging from −0.47 to 2.06. We explored this inconsistency by conducting a subgroup analysis to determine whether time in training influenced outcomes. To do this, we conducted a post hoc analysis in which we calculated the ratio of time spent by the competency-based vs noncompetency-based group. For the 6 studies with a time ratio <1 (shorter time for competency-based simulation), the pooled SMD was 1.00 (95% CI = 0.53 to 1.7, P < 0.001) indicating a large effect favoring the competency-based group. For the 2 studies with time ratio = 1 (identical training time), the pooled SMD was 0.67 (95% CI = −0.06 to 1.340, P = 0.07) indicating a moderate but not statistically significant effect favoring the competency-based group. For the 4 studies with time ratio >1 (competency-based group longer), the pooled SMD was 0.18 (−0.67 to 1.01, P = 0.68). Of note, 2 of these 4 studies were the studies (discussed above) that showed no change from pretest to posttest for either intervention.

We also explored the impact of study quality (risk of bias) by conducting sensitivity analysis excluding nonrandomized studies, those with imprecise SMD, and subgroup analysis contrasting studies with high versus low MERSQI and NOS-E values. Analysis restricted to randomized studies (n = 10) showed a pooled SMD of 0.56 (95% CI = 0.14 to 0.97, P = 0.009). Sensitivity analysis excluding 1 study with an imprecise SMD found virtually identical results to the main analysis with a pooled SMD 0.66 (95% CI = 0.21 to 1.10, P = 0.004). We defined high MERSQI values as those above the median of the group (15.5) and those with high NOS-E values as those equal to or above 4.14 Within high-quality studies (n = 6), the pooled SMD was 0.47 (95% CI = −0.19 to 1.12, P = 0.17).

We report the pooled effect size for each Kirkpatrick outcome level in Figure 3. Skill outcomes measured in the simulation lab had large effect sizes: for (1) skill nontime pooled analysis SMD = 0.79 (95% CI = 0.22 to 1.36, P = 0.007) and (2) skill time pooled analysis SMD = 1.07 (95% CI = 0.81 to 1.33, P < 0.001). The effects for higher-level Kirkpatrick outcomes, however, were small: (1) nontime behavior pooled analysis SMD = 0.46 (95% CI = −0.34 to 1.23, P = 0.26) and (2) patient effect pooled analysis SMD = 0.21 (95% CI = −0.32 to 0.75, P = 0.43).

Comparison of Other Instructional Approaches

We conducted a meta-analysis for all themes with more than 5 articles within the same subdomain. Two additional subdomains of the instructional design theme had sufficient articles to conduct a pooled analysis. The first subgroup of 7 studies (15%) compared competency-based simulations using automated feedback compared with simulations with nonautomated feedback. Figure 4 shows the meta-analysis for the best outcome for the automated feedback group compared with the nonautomated feedback group with a pooled SMD of 0.11 (95% CI = −0.31 to 0.52, P = 0.62), a negligible effect favoring automated feedback. Inconsistency was high between studies (I 2 = 79%) with effect sizes ranging from −1.38 to 1.07. Given the small number of studies, high inconsistencies, and negligible effect found, further sensitivity analysis was not conducted.

FIGURE 4.

FIGURE 4

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Forest plot of all outcomes for the automated feedback versus nonautomated feedback meta-analysis on competency-based simulation training for procedural skills.

The second subgroup of 7 studies (15%) compared the source of feedback (specifically self-regulated feedback versus instructor-guided feedback). Figure 5 shows the meta-analysis for the best outcome with a pooled SMD of 0.10 (95% CI = −0.33 to 0.53, P = 0.64). Inconsistency was again high between studies (I 2 = 80) with effect sizes ranging from −1.38 to 1.07. Likewise, this pooled effect size was negligible and not statistically significant. Given the small number of studies, high inconsistencies, and negligible effect found, further sensitivity analysis was not conducted.

FIGURE 5.

FIGURE 5

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Forest plot of all outcomes for the instructor-guided feedback versus self-regulated feedback meta-analysis on competency-based simulation training for procedural skills.

DISCUSSION

In this systematic review and meta-analysis, we examined the effectiveness and efficiency of competency-based simulation training for procedural skills among health professions learners. Overall, we found strong evidence to support that competency-based simulation consistently outperformed noncompetency approaches in promoting acquisition of skills in a simulation setting. The impact of competency-based simulation was particularly pronounced in outcomes measured within controlled simulation environments; however, effects for clinical outcomes (physician behaviors and patient effects) were not statistically significant. This suggests that competency-based approaches are highly effective in developing psychomotor procedural skills in a simulation setting; however, the translation of these skills to clinical practice, although still positive, is less pronounced.

An interesting finding was that less time in competency-based training compared with noncompetency-based training had a large positive effect size, but equal training time and more training time had smaller effect sizes. This variable finding is likely due to the small number of studies included in each analysis. Additionally, 50% of the studies included in the subanalysis that had more time in training in the competency arm than the noncompetency arm were studies that had no improved skill acquisition in their primary outcome measure. Because of this, these were likely ineffective simulations that impacted this analysis. This variable finding speaks to the fact that time on task alone does not guarantee adequate skill acquisition and instead simulations that focus on specific outcome-based assessments should be used to promote efficient, effective and improved skill acquisition.

Frequent research themes explored included comparisons of different simulators and different types and sources for delivering feedback to learners. The differences in feedback delivery approaches were negligible in magnitude and not statistically significant. The equivocal results from the secondary analyses are chiefly due to the small number of studies in each category and uneven research methods. The limited number of articles examining these, and other subdomains of instructional strategies, highlights the need for further research identifying and optimizing the active components of effective competency-based simulation.

Integration With Prior Literature

Our systematic review and meta-analysis affirm the results of prior reviews on this subject, establishing the efficacy of competency-based simulation training in medical education.7–9 Our findings also support evidence from 1 prior review showing proficiency-based training improved patient outcomes and aligns with early evidence from another review bolstering evidence to support competency-based simulation training.7,8

A distinct aspect of our review, compared with previous systematic reviews on this topic (and simulation-based education in general), is an exclusive focus on comparative efficacy studies incorporating simulation-based training in both the control and intervention arms.7–9,66 This contrasts with earlier reviews that included control groups without any simulation intervention. By focusing on studies where simulation is an integral component of all training interventions, we eliminated confounding variables associated with nonsimulation-based control groups.

Limitations

This review has several limitations. First, it does not include articles published after March 2022 or those without full-text English translations. While some recent publications may be missing from this analysis, we believe it still contributes to the current synthetic evidence. The review includes more than 40,000 articles and includes unique head-to-head comparative efficacy studies not found in other published systemic reviews. Additionally, many included studies lacked detailed exploration into the specific design elements that could be contributing to the impact on procedural skill acquisition. Additionally, although a broad range of outcomes were assessed in this review, a minority of studies assessed downstream clinical outcomes thereby limiting applicability to improving patient care.

Several studies contained methodological weaknesses that compromised the strength and integrity of their findings. In addition, a wide range of effects with considerable variability was observed, posing challenges for interpreting aggregated data. We did not systematically track selective reporting of results, largely because authors were inconsistent in documenting planned versus actual analyses. This represents a limitation and may introduce reporting bias. While we attempted to account for some methodological variables through subgroup analysis, many studies involved multiple changing features within the same study making it difficult to isolate specific instructional design features for synthesis.

Finally, involvement of many reviewers in our research team introduces the potential for unwanted variation in article screening and selection. The potential for bias or inconsistency in study selection and data extraction remains a consideration. Although training for all reviewers was conducted and all conflicts were resolved by the primary authors (J.H.V. and D.O.K.), we did not measure interrater agreement among team members.

Implications

Our results show there is substantial evidence to support the use of competency-based simulation to train health professional procedural skills. These findings have implications for the future of procedural skills training for health professionals, particularly in advocating for the adoption of competency-based simulation training methods. This endorsement aligns with the recommendations and policies of accrediting organizations, and highlights that merely allocating time for simulation in training programs is not sufficient to guarantee effective learning or skill acquisition. Rather, strategies focused on targeted, outcome-based training that emphasizes abilities and promote learner-centered development produces rigorous skill acquisition in an efficient and effective manner. Such strategies are designed to maximize the impact of simulation-based training, as evidenced by the work of Ericsson and Pool,67 along with McGaghie et al,68,69 who have contributed to understanding of how deliberate practice and well-structured simulation sessions can enhance development of procedural skill expertise.

We found high variability in the quality of reporting that likely contributed to the heterogeneity among study findings as missing information made analysis difficult. Future research must focus on standardizing terminology and reporting methods across studies to improve research synthesis. Existing frameworks such as the Reporting Mastery Education Research in Medicine guidelines could provide a useful starting point for researchers to better identify the impact of various educational variables on skill acquisition in simulation-based training.70 The use of consistent reporting would also aid in future synthetic research to identify the key ingredients necessary for skill acquisition.

Our review found a paucity of head-to-head studies of simulation approaches. Future research must focus on comparative efficacy studies to determine which instructional design features are necessary for achieving competency of procedural skills most efficiently and effectively. We recommend using the 4 themes of instructional design variables (instructional strategies, simulation models, learner engagement, and assessment models) as well as the identified subdomains presented in Table 3 as a framework to develop further comparative efficacy studies to better elucidate the best instruction design features needed to produce procedural skill competency efficiently and effectively.

Additionally, despite strong evidence supporting the efficacy of competency-based simulation to enhance procedural skills in controlled environments, we found that evidence is scarce to demonstrate that these skills transfer to clinical settings and improve patient care outcomes. This deficit highlights a critical gap in the current body of literature, identifying a need for studies designed to evaluate the longitudinal effects of simulation training on clinical practice and patient care quality. Addressing this challenge is not straightforward and cannot be achieved through isolated studies but requires a thematic, sustained, and cumulative research effort that bridges education and health services research.70 Such an approach would involve longitudinal studies that follow learners from the simulation environment into clinical practice, evaluating how simulation-acquired skills translate to real-world patient care scenarios. We recommend a call for a cohesive research agenda that spans the immediate education outcomes of simulation training, focusing on the ultimate goal of enhancing patient care quality and safety.

Addressing these gaps will meaningfully contribute to a better understanding of competency-based simulation training and will also help elucidate methods of implementing simulation to cultivate clinical skills among healthcare professionals. This direction for future research and practice holds promise for improving the quality of care delivered to patients via more skilled and competent healthcare professionals.

Supplementary Material

sih-21-231-s001.docx (16.7KB, docx)
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sih-21-231-s002.docx (42.3KB, docx)
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ACKNOWLEDGMENTS

The authors thank the 2023 Society for Simulation in Healthcare Research Summit participants who reflected on our results and provided valuable insights. The authors also thank Kristyn Gadlage for administrative support throughout the review process and Selah Kessler for help with Abstrackr.

Expert panel is as follows:

• Kathy Boutis, MD

• Tim Draycott, MD

• Kirsty Freeman, PhD, MHPE, FSSH

• S. Barry Issenberg, MD, FSSH

• Michelle Kelly, PhD, MN, BSc, RN

• David Kessler, MD, MSc, FSSH

• William McGaghie, PhD

• Neal Seymour, MD

• Rebecca Szabo, MClinEd, MMBS

• Julia Vermylen, MD, MPH

Review Team:

• Asit Misra, MD, MSMEd, CHSE, FSSH

• Barry Issenberg, MD

• Beverley Robin, MD

• Candace Mannarino, MD, MS

• David Kessler, MD, MSc, FSSH

• Denise Findlay, MBBS, MEd, MET

• Diane Dennis, PhD, GradDip, BSc

• Elaine Cohen, MEd

• Frank Yanko, BS

• Heather Ballard, MD, MS

• Heidi Waldron, MBChB, MHPEd,

• Jason Arnold, MD

• Julia Vermylen, MD, MPH

• Kaitlyn Vitale, MD

• Katherine Salada, MD

• Mohammad Bayoumi, MD, FRCPCH

• Molly Beestrum, MLIS

• Monica Gope, MBBS, MClinEd

• Nicole Paprocki, MD

• Noah Einstein, MD

• Rachel Kasimer, MD

• Remy Bremner, MD

• Scott Crawford, MD, FSSH

• Tadaki Tomita, MD, RPVI, FACS

• Timothy Rowe, MD

• William McGaghie, PhD

Footnotes

ORCID: Julia H. Vermylen, 0000-0002-1674-0738

The authors declare no conflict of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s Web site (www.simulationinhealthcare.com).

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