Evolving requirements to measure milestones and competencies at all phases of medical training1–6 signal a need to develop and validate new systems of assessment. Although the measurement of patient-related outcomes (i.e., provider behaviors and patient outcomes) in the workplace is desirable, such assessments are limited by costs, patient safety concerns, nonstandardized settings, and infrequent clinical events.7–9 Thus, educators must continue to rely on assessments completed in settings without direct patient contact.10,11 Although we caution against overrelying on such surrogate measures,12 establishing the necessary evidence base will permit educators and researchers to use these surrogates as the primary means of assessment during day-to-day practices. The use of patient-related outcomes then would be reserved for select situations, such as the later stages of training or the culmination of a program of research.7,8
Leaders in medical education have proposed that simulation-based assessments are essential to solving some of these challenges, given that they permit the testing of learners’ performance in safe and standardized environments.8,13,14 However, before an assessment tool is widely implemented, the validity evidence supporting both its intended use and the interpretation of its scores needs to be established.15 A recent systematic review of 417 studies of simulation-based assessments highlights notable limitations in the validity evidence supporting such tools.16 Two other reviews examined the association between simulation-based training and patient-related outcomes, but neither examined the role of simulation in assessment.17,18 To date, we are not aware of any review—for simulation-based assessments specifically or for assessments in general—evaluating the empirical evidence linking educational surrogates with corresponding assessments in the workplace. The purpose of the present study is to examine the association between scores from simulation-based and patient-related assessments and to outline the implications for current assessment practices.
According to guidelines, a proposed assessment tool is considered a valid surrogate if its scores correlate with the target outcome, and change in the proposed surrogate is associated with a corresponding change in the target outcome.19,20 Additional sources of validity evidence should also be sought to provide robust support for the surrogate measure.21–23 Moreover, the research itself should be rigorous and well reported. Hence, our detailed analysis of simulation-based surrogates required attention to broad sources of validity evidence as well as common reporting and methodological issues.
We conducted a systematic review to answer the following questions:
- What are the associations between technology-enhanced simulation-based outcomes and patient-related outcomes?
- What other sources of validity evidence have been reported for these outcomes?
- What is the quality of the methods and reporting in this body of research?
We planned, conducted, and reported on this review in adherence with PRISMA standards.24
Data sources and searches
We conducted our search in two stages. First, we examined all the studies identified in our earlier reviews of simulation-based training and assessment.16,25 For these, we searched Ovid MEDLINE, Ovid EMBASE, CINAHL, PsycINFO, ERIC, Web of Science, and Scopus using a search strategy previously reported in full,25 which was last updated on May 11, 2011. Second, we updated our search on February 26, 2013, searching Ovid MEDLINE, Ovid EMBASE, and Scopus using a revised strategy developed by a research librarian to focus on simulation-based assessments. Terms in both searches focused on the topic (e.g., simulat*), learner population (e.g., med*, nurs*, health occupations), and assessment (e.g., assess*, valid*); see Supplemental Digital Appendix 1 at https://links.lww.com/ACADMED/A246 for the full revised search strategy. We sought additional studies by examining the references from several published reviews of simulation-based assessments and training.26–38
We used broad inclusion criteria to identify original research studies published in any language that (a) assessed trainees both using technology-enhanced simulation and in the context of actual patient care, (b) involved health professionals at any stage of training or practice, and (c) reported evidence of the association between simulation-based scores and patient-related scores. We defined technology-enhanced simulation as “an educational tool or device with which the learner physically interacts to mimic an aspect of clinical care for the purpose of teaching or assessment.”25 We included technologies such as mannequins, virtual reality simulators, and part-task models, and excluded computer-based virtual patients and human standardized patients because they have been the topic of previous reviews.39,40 We included self-reported information regarding procedural success or complications but excluded self-assessments of confidence or subjective performance.
We worked independently, then in pairs, to screen titles, abstracts, and full-text articles for inclusion using the same criteria (see above) for both search strategies, with good agreement (intraclass correlation coefficient [ICC] 0.72 for the first search and 0.67 for the second). We resolved all conflicts by consensus.
Data abstraction and quality assessment
Independently and in pairs, we used a data abstraction form to extract information from the included studies on trainees, clinical topic, validity evidence, study quality, and measures of association. We distinguished validity evidence using Messick’s framework22—namely, content (ICC 0.60), response process (ICC 0.50), internal structure (ICC 0.73), relations with other variables (ICC 0.65), and consequences (ICC 1.0)—and further classified this evidence as favorable or unfavorable (i.e., evidence of validity or invalidity). For internal structure evidence, which often includes reliability metrics, we considered interrater reliability > 0.4 (“fair” per Fleiss and Cohen41) and internal consistency reliability > 0.7 as favorable. For relations with other variables, which typically involves correlations, we considered r ≥ 0.5 as favorable, r = 0.3–0.49 as weakly favorable, and r < 0.3 as negative, based on Cohen’s classification of these ranges as large, medium, and small/negligible, respectively.42
We used the Medical Education Research Study Quality Instrument (MERSQI)43 to grade overall study quality. We also looked at the unit of analysis (patient or trainee; ICC 0.59), presence/absence of a power analysis (ICC 1.0), number of independent analyses (ICC 0.94), reporting of patient demographics (ICC 0.51), blinding of assessment (ICC 0.69), time between simulation and patient assessment (ICC 0.73), assessment of behaviors through direct observation of specific encounters or rotation grades (ICC 0.90), collection of correlational data before or after training (ICC 0.91), and whether evaluating association was a study goal (ICC 0.94).
We classified simulation-based outcomes as time skills (time required to complete the task), process skills (measures of performance such as instructor ratings or minor errors), and product skills (quality of the final product, rate of completion, or major complication). We analogously classified outcomes assessed in the clinical context as time behaviors, provider behaviors (e.g., performance ratings or grades), and patient outcomes (e.g., procedural complications). When a simulation/patient correlation coefficient was not reported, we calculated one from other reported information (e.g., coefficient of determination [R2], P value, or t statistic) using standard methods.44 When necessary, we estimated correlation from linear regression slopes using the approach described by Peterson and Brown.45 For studies reporting insufficient information to calculate a correlation coefficient, we requested additional information from the authors. If more than one simulation or patient-related outcome was reported, we selected the association linking the most similar simulation and patient-related outcomes.
Data synthesis and analysis
We quantitatively pooled z-transformed correlation coefficients (Pearson r or Spearman rho) using random-effects meta-analysis, and then we transformed the pooled result back to the native format. We conducted separate meta-analyses for each outcome. We conducted planned subgroup analyses by topic, trainee, study quality (MERSQI score above or below median), named instrument, the timing of correlation (before or after training), and whether patient outcomes were derived from direct observation or rotation grades. The weighting for all meta-analyses was based on the number of trainees, not the number of patients. We explored possible publication bias using funnel plots and the Egger asymmetry test, although these methods are limited in the presence of high inconsistency.46
We quantified between-study inconsistency (heterogeneity) using the I2 statistic,47 which estimates the percentage of variability not due to chance. Values for I2 > 50% indicate large inconsistency. We used SAS 9.3 (SAS Institute, Cary, North Carolina) for all analyses. Statistical significance was defined by a two-sided alpha of 0.05.
From 11,628 potentially relevant articles, we identified 59 studies in which assessments included both simulation-based and patient-related outcomes. Of these, 29 reported data to determine the correlation between these outcomes. We attempted to contact the authors of the 30 studies lacking correlation information, successfully contacted 17, and received sufficient data from 4. Hence, 33 studies met our inclusion criteria. Figure 1 is a trial flow diagram showing our literature search and study selection process, and Appendix 1 reports key characteristics.
The 33 included studies enrolled 1,203 trainees (range 8–135, median 27).48–80 Most studies (n = 24) enrolled resident physicians. All of the clinical topics focused on procedural tasks such as surgery, anesthesiology, or endoscopy.
Most studies (n = 27) measured provider behaviors with real patients—namely, procedural ratings by instructors (n = 18),48,49,51–53,55,56,59–62,64,65,67,68,74,77,79 grades on clinical rotations (n = 8),57,58,66,69–72,75 and automated motion analysis (n = 1).50 Seven studies reported time behaviors,50,53,56,64,76,78,79 and five studies reported direct patient outcomes—namely, procedural success (n = 2),63,80 evaluation of a final product (n = 2),54,73 and major complications (n = 1).79 Simulation-based assessments of process skills (n = 24) included checklists (n = 9),49,52,62,63,70–72,74,75 global rating scales (n = 6),51,55,57,61,65,77 simulator-specific scores (n = 4),58,59,67,68 motion analysis (n = 2),50,64 and a visual analogue scale (n = 1).56 Seven studies measured time skills.48,53,56,64,76,78,79 Six studies measured product skills—namely, faculty ratings (n = 2),60,69 global ratings of a dental preparation (n = 2),54,73 procedural success (n = 1),80 number of attempts (n = 1),66 and major complications (n = 1).79 Six studies analyzed correlations between outcomes that we considered conceptually misaligned. Specifically, product skills were correlated with provider behaviors (n = 3),60,66,69 time skills were correlated with provider behaviors (n = 2),48,79 and process skills were correlated with patient outcomes (n = 1).63 Twelve studies reported the time delay between assessments, with a median of 75 days (range 0–180). Two studies reported nontechnical skills60,72; one of these studies also reported a technical skill, and we included that in our meta-analysis.60 A human rater assessed all provider behaviors, three patient outcomes,54,73,79 and two time behaviors.76,78 Two patient outcomes were self-reported by trainees.63,80 The method of measuring time behaviors was not reported explicitly in five studies.50,53,56,64,79
Associations between simulation-based and patient-related outcomes
For the 27 studies reporting a correlation with provider behaviors, the pooled correlation was 0.51 (95% confidence interval [CI], 0.38 to 0.62; P < .0001); see Panel A in Figure 2. On the basis of Cohen’s classification,42 we considered this a large correlation. However, between-study inconsistency was large, with I2 = 79%. For the 7 studies reporting a correlation with time behaviors, the pooled correlation was 0.44 (95% CI, 0.15 to 0.66; P = .0001), a medium correlation, with large inconsistency (I2 = 58%); see Panel B in Figure 2. For the 5 studies reporting a correlation with direct effects on patients, the pooled correlation was 0.24 (95% CI, −0.02 to 0.47; P = .05), a small correlation, with large inconsistency (67%); see Panel C in Figure 2. Neither funnel plots nor the Egger asymmetry test suggested publication bias for any analysis.
Subgroup analyses for provider behaviors (see Figure 3) demonstrated that the pooled correlation was highest for physicians in practice and higher for postgraduate trainees than medical students. Correlations also were higher for direct observations of specific clinical encounters than for rotation grades reflecting general impressions, and higher for assessments conducted before training than those conducted after training. The pooled correlation was large (> 0.68) for the three instruments that were used as the simulation-based outcome in more than one study: the Objective Structured Assessment of Technical Skill (OSATS),51,55,57 the Global Operative Assessment of Laparoscopic Skills (GOALS),61,65 and the Fundamentals of Laparoscopic Skills (FLS; or its predecessor the McGill Inanimate System for Training and Evaluation of Laparoscopic Skill).58,59,68
As noted above, using a measure as a surrogate requires evidence of correlation with the target outcome and also evidence that the surrogate changes when the target outcome changes. Whereas all studies reported the first criterion, only one study reported the second.80 This study assessed ventriculostomy cannulation success in patients and in an augmented-reality simulator. Whereas both outcomes improved with training, posttraining simulation success approached 100% in all participants, resulting in restriction of range that in turn attenuated the correlation. As such, pretraining correlation was greater (r = 0.76) than posttraining correlation (r = 0.34).
Appendix 1 reports the sources of validity evidence collected for each study and whether we judged it as favorable or unfavorable. All 33 studies reported a statistical association between two variables. Eleven studies explored additional relations with other variables by evaluating how scores varied by participants’ training level.
For simulation-based outcomes, 13 studies reported internal structure evidence, 12 reported content evidence, 2 reported response process evidence (e.g., comparison of on-site and off-site raters), and 1 reported consequences (rigorous standard-setting method). For patient-related outcomes, 10 studies reported content evidence, 9 reported internal structure evidence, and 1 reported response process evidence (unfavorable: key elements not visible on video). None reported evidence of consequences.
Methodological and reporting quality
We summarize study quality in Supplemental Digital Table 1 available at https://links.lww.com/ACADMED/A246. Of the 33 studies, 10 failed to report the number of learners providing data, and 18 failed to report the number of patients contributing data. Among the 15 studies reporting the number of patients, the average number of patients per trainee ranged from 1 to 5.9, with a median of 1. Only 2 studies reported demographic information on patients. The average MERSQI score was 13.4 (standard deviation, 1.4) from a maximum possible of 18.
Three studies calculated the correlation coefficient using more than one simulation-based data point per trainee (i.e., an inappropriate unit of analysis), and 1 of these studies also used more than one patient-related data point per trainee. Seven studies did not report sufficient information to determine the unit of analysis.
Seventeen studies listed the correlation between simulation-based and patient-related outcomes as the primary study objective, 3 listed it as a secondary objective, 1 listed it as an objective without prioritization, and 12 did not mention it as an objective. Only 3 studies reported a power analysis for a calculation involving a patient-related outcome.
Twenty-five studies reported multiple correlation coefficients, yet only 2 identified one analysis as a primary study objective—hence, most left it to the reader to prioritize among multiple reported analyses.
Our synthesis of 33 studies suggests that properly developed and validated simulation-based assessments can supplement and potentially replace measures of provider behaviors and patient outcomes for select procedural skills. We found that pooled correlations with simulation outcomes were, on average, large for provider behaviors, medium for time behaviors, and small for patient outcomes. Although between-study inconsistency was high, all but two of the individual coefficients were positive, and most were of medium magnitude or higher. Subgroup analyses indicated stronger correlations for participants with greater experience (i.e., practicing physicians > resident physicians), for direct observations of performance, and for assessments conducted before training.
However, available evidence provides only limited support for specific instruments. We identified large pooled correlations (r ≥ 0.68) and generally favorable validity evidence for three commonly used procedural skills assessment instruments: OSATS, FLS, and GOALS. All other instruments appeared only once in our review.
Although most simulation-based assessments demonstrated favorable correlations with provider behaviors and patient outcomes, just one study80 reported evidence indicating how changes in the simulation-based outcome corresponded with changes in the patient-related outcome, an important element in the chain of evidence supporting the use of such measures.19 Moreover, we found relatively sparse validity evidence beyond the correlation data. Thus, prior to using any simulation-based assessment tool, we encourage educators to carefully review all available validity evidence to verify that the evidence supports the intended use in their local curriculum.15
We intentionally included studies reflecting a variety of technology-enhanced simulation modalities, learner populations, clinical topics, and assessment methods. Although this variation likely contributed to the high between-study inconsistency, including more studies ensured a larger sample size and increased statistical power, thus expanding the applicability of our findings. We could not include 26 studies reporting both simulation-based and patient-related outcomes because authors did not report data linking these outcomes. We successfully contacted 57% of these authors, but most did not supply the needed information. We chose not to include standardized patient simulation, which may explain the dominance of procedural skills assessments.
Although our judgments regarding validity evidence (favorable/unfavorable) were grounded in accepted standards, we recognize that validity evidence and validation are far more nuanced than our simple classification scheme and that other schemes could be justified. Our classifications serve the present purpose of identifying broad strengths and weaknesses in this evidence base but may be insufficient to appropriately evaluate the validity of a specific instrument’s scores for a specific application.
We acknowledge that all surrogate outcomes have potential limitations, including noncausal associations, nonuniform response to change, and incomplete representation of the task.12,20 Researchers must address these limitations systematically before educators can confidently use simulation-based assessments to replace workplace-based assessments.
Study strengths include the rigor of our search, abstraction, and analysis process. We found no evidence to suggest publication bias, although that cannot be excluded.
Integration with other research
Recent systematic reviews have summarized the prevalence of validity evidence for simulation-based assessments broadly16 and clarified the specific data elements contributing to each evidence source.22 We extend these findings by detailing the magnitude and quality of the associations between simulation-based outcomes and patient-related outcomes. Our focus on assessment also complements other recent reviews examining the benefits of simulation-based training on patient-related outcomes.17,18
Our review, which included 33 studies focused on procedural tasks, complements a review of patient-related assessments focused on direct observation of nonprocedural behaviors.81 This predominance of procedural tasks is consistent with the findings of our recent systematic reviews of simulation-based assessments,16,17 but it contrasts with those of our reviews of simulation-based training that identified more than 200 studies addressing nonprocedural tasks.25,82,83 The preferential study of procedural tasks when evaluating simulation-based assessments and when measuring patient outcomes may not be related solely to the educational modality (i.e., technology-enhanced simulation), and it might reflect biased topic selection. Alternatively, it could reflect challenges in conducting workplace-based assessments of nonprocedural tasks or indicate that other approaches (e.g., standardized patients)39 have met this need.
Implications for practice and research
A number of factors likely contribute to our finding that the correlations between simulation-based assessments and provider behaviors are highest for practicing physicians and lowest for trainees. First, just 12 studies included either medical students or practicing physicians, and a larger sample may yield different results. Second, we suggest that practicing physicians’ performance is more consistent across contexts and that the higher variability of trainees’ performance may attenuate the correlation. Third, trainees’ workplace-based assessments may be influenced by variables absent from simulated settings that further reduce the correlation, such as stress, worry about patient harm, and cues or assistance from supervisors. No matter the explanation, the weak correlations among trainees suggest a role for dual assessment approaches: Simulation-based assessments might be most appropriate early in training (thus conserving clinical time and resources and protecting patients from potential harm), with workplace-based assessments used in later stages. By contrast, the stronger correlations for physicians suggest that simulation-based assessments may be sufficient for low- or moderate-stakes contexts.
During this review and in our discussions with authors, we identified several methodological concerns related to research evaluating the links between simulation-based assessments and provider behaviors and patient outcomes. First, because training generally reduces between-subject variability, the restricted range of posttraining scores will often cause pretraining correlation to be higher than posttraining correlation (as we confirmed in subgroup analyses and in the one study reporting pre- and posttraining analyses). Second, “a correlate does not a surrogate make.”20 Research seeking to validate surrogate outcomes should consider not only the baseline correlation but also whether scores from the two assessments change in parallel as the construct of measurement (e.g., trainee performance) evolves with training or over time. These two points together illustrate the potential confounding effect of training when establishing a relationship between simulation-based and patient-related assessments. Third, the precision of the assessment improves with multiple repetitions, but so does the participants’ performance, as is evidenced by the several studies that showed score improvement over multiple repetitions of an assessment task (data not reported). Thus, educators must consider the intended inference when deciding which data point(s) from a repeating activity to score. Fourth, we noted wide variation in sample size and suspect that many studies were underpowered to detect reliable estimates of correlation. Investigators could use our pooled correlations to estimate power when designing future studies.
In conclusion, correlation alone does not establish validity. Although most of the studies in our sample demonstrated supportive evidence of relations with other variables, we found substantial gaps among all other sources of validity evidence. We encourage researchers to seek a broad variety of evidence22 when evaluating the validity of both simulation-based and workplace-based assessments.
Acknowledgments: The authors wish to thank Stanley J. Hamstra, PhD (Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada), and Jason H. Szostek, MD, and Amy T. Wang, MD (Department of Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota), for their assistance in the initial literature search. They received no compensation for their contributions.
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