Health care education is confronted with many global challenges. Shorter hospital stays, specialization of care, higher patient safety measures, and shortage of clinical teachers all diminish the traditional opportunities for the training of health professions through direct patient contact [1,2]. Early health professions education is often dominated by theoretical presentations with insufficient connection to clinical practice [3]. The need to increase numbers and quality of the health workforce is especially visible in low-and-middle-income countries, where the need to scale up high-quality health education and introduce educational innovations is most pressing [4]. Therefore, the global medical education community is perpetually searching for methods that can be applied to improve the relevance, increase the spread, and accelerate the educational process for health professions [5].

Digital education (often referred to as e-learning) is “the act of teaching and learning by means of digital technologies” [6]. It encompasses a multitude of educational concepts, approaches, methods, and technologies. Digital health education comprises, for example, offline learning, mobile learning, serious games, or virtual reality environments. We have conducted this systematic review as part of a review series on digital health education [6-19] and focused it on the simulation modality called virtual patients.

Virtual patients are defined as interactive computer simulations of real-life clinical scenarios for the purpose of health professions training, education, or assessment [20]. This broad definition encompasses a variety of systems that use different technologies and address various learning needs [21]. The learner is cast into the role of a health care provider who makes decisions about the type and order of clinical information acquired, differential diagnosis, and management and follow-up of the patient. Virtual patients are hypothesized to primarily address learning needs in clinical reasoning [22,23]. However, an influence of the use of virtual patients on other educational outcomes has been reported in previous literature [21,24].

The educational use of virtual patients may be understood through experiential learning theory [25,26]. Following this theoretical model of action and reflection, virtual patients expose learners to simulated clinical experiences, providing mechanisms for information gathering and clinical decision making in a safe environment [27]. Exposing the learner to many simulated clinical scenarios supports learning diagnostic processes [28] while acquainting learners with a standardized set of clinical conditions common in the population, but rare or nonaccessible in highly specialized teaching hospitals [29].

Some concerns have been raised about educational use of virtual patients. Virtual patients should not replace but complement contact with real patients [27]. There are concerns around the use of virtual patients potentially resulting in less empathic learners [30]. The use of unfamiliar technology as part of virtual patients’ education can represent a barrier to learning, even for younger generations [31,32]. Virtual patients may also prove ineffective when technological objectives drive teaching instead of being motivated by learning needs [33].

This virtual patient simulation review has been preceded by several narrative reviews [22,34-37] and 2 systematic reviews with meta-analyses [38,39]. Our preliminary literature analysis showed that the number of studies including the term virtual patient or virtual patients has more than doubled on the MEDLINE database in comparison with available evidence provided in previous systematic reviews (February 2009 [38] and July 2010 [39]). Thus, our review will update the evidence base with studies not included in previous analyses.

The objective of this review was to evaluate the effectiveness of virtual patient simulation for delivering pre- and postregistration health care professions education using the following comparisons:

By traditional education, we mean all nondigital educational methods. This includes lectures, reading exercises, group discussion in classroom, and nondigital simulation as standardized patients or mannequin-based training. Virtual patient blended learning is the addition of virtual patients as a supplement to traditional education when the control intervention uses nondigital education methods only. Other types of digital education may include interventions such as video recordings, Web-based tutorials, or virtual classrooms.

We assessed the impact of virtual patient interventions on learners’ knowledge, skills, attitude, and satisfaction. Our secondary objective was to assess the cost-effectiveness, patient outcomes, and adverse effects of these interventions.

While conducting the review, we adhered to the Cochrane methodology [40], followed a published protocol [41], and presented results following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines [42].

We included randomized controlled trials (RCTs) and cluster RCTs (cRCTs). We excluded crossover trials because of the high likelihood of carryover effect.

Participants in the included studies had to be enrolled in a pre- or postregistration health-related education or training program (see glossary in Multimedia Appendix 1). This included students from disciplines such as medicine, dentistry, nursing and midwifery, medical diagnostic and treatment technology, physiotherapy and rehabilitation, and pharmacy.

This review focused on screen-based virtual patient simulations that form a computerized, dynamically unfolding representation of patient cases. A virtual patient simulation is introduced by a case description and might contain answers given by the patient, clinical data (eg, laboratory results, medical images), and descriptions of patients’ signs and symptoms. Only the representations of the patient as a whole were of interest, rather than studies that focused on single parts of the body. As a matter of a policy followed in the Digital Health Education Collaboration [6] and aiming at avoiding duplication of reviews, we deliberately excluded virtual patients in 3-dimensional (3D) virtual learning environments from this study. We judged that a higher level of immersion of learners in 3D virtual environments, connected with potential technical challenges (eg, difficulties in navigating such environments, lags because of increased computational time or limited internet bandwidth), was likely to influence the educational outcomes and therefore merited a separate analysis covered already by the virtual reality review [13] of this Digital Health Education Collaboration series. We also excluded those virtual patient interventions which require nonstandard equipment (eg, haptic devices, mannequins) or those virtual patients which are human controlled (eg, simulated email correspondence or chat room conversations). We excluded studies in which virtual patients were just a small part of the intervention and those in which the influence of virtual patients was not evaluated separately.

Furthermore, 2-arm RCTs comparing virtual patients with a control group not involved in any type of subject-related learning activity were not considered eligible as previous meta-analyses have already shown a large positive effect when virtual patients were compared with no intervention [38].

We decided to introduce to the review a comparison of virtual patients blended learning with traditional education as a consequence of the discussion in the community on the need to eliminate traditional types of learning activities to make space for virtual patients. For instance, Berman et al [29] noticed that the students’ subjective learning effect perceptions and satisfaction with integration were lower at universities that increased the workload of students by adding virtual patients without releasing time resources in the curriculum. As most of health professions education is conducted on campus, an integrated effect of virtual patients is possible. Blending virtual patients with traditional education is challenging and qualitatively different than a nonintervention control group comparison.

Eligible primary outcomes were students’ (1) knowledge, (2) skills, (3) attitudes, and (4) satisfaction—together representing clinical competencies measured post intervention with validated or nonvalidated instruments. Secondary outcomes were (1) economic cost and cost-effectiveness, (2) patient outcomes, and (3) observed adverse effects.

We searched the following 7 databases: MEDLINE (via Ovid), EMBASE (via Elsevier), The Cochrane Library (via Wiley), PsycINFO (via Ovid), Educational Resource Information Centre (ERIC; via Ovid), Cumulative Index to Nursing and Allied Health Literature (CINAHL; via EBSCO), and Web of Science Core Collection (via Thomson Reuters). We adapted the MEDLINE strategy and keywords presented in Multimedia Appendix 2 for use with each of the databases above. We searched databases from the year 1990 to September 20, 2018 to highlight recent developments and did not apply language restrictions. For all included studies, we searched references lists and conducted author and citation searches. We searched lists of references from other identified relevant systematic reviews while running our electronic searches.

Our searches yielded a total of 44,054 citations, and 51 studies with 4696 participants were included (Figure 1). Overall, 2 reports described results already included in the review [48,49].

All included studies were published in peer-reviewed journals. All included studies had an RCT design, with the exception of 3 cRCTs [50-52].

A total of 25 studies compared virtual patients with traditional education [53-77], 11 compared a blend of virtual patients and traditional education with traditional education [50,52,78-86], 5 studies compared virtual patients with different forms of digital health education [51,87-90], and 10 studies compared different types of virtual patient interventions [91-100].

The traditional education control group involved a reading assignment in 6 studies [59,60,62,67-69]; 4 studies each involving a lecture [63,66,72,77], group assignment [53,65,71,73], and mannequin-based training [58,64,70,76]; and 1 each involving standardized patients [74] and ward-based education [75]. In 5 studies, the intervention was a mix of different forms of traditional education (eg, lecture, small group assignment, and mannequin-based training) [54-57,61].

The digital education control group was in 2 studies—a Web tutorial or course [88,90] and video recording [87,89]—and in 1 study, a mix of traditional lectures and Web materials including video clips [51] was used.

Studies comparing different types of virtual patients contrasted narrative with problem-solving structure of virtual patients [91]; virtual patients with and without usability enhancements [94]; different forms of feedback in virtual patients [95,96]; worked with unworked versions of virtual patients [97]; differences between self-determined and mandatory access to virtual patients [98]; virtual patients collections in which all the cases were presented at once to those automatically activated spaced in time [99]; effects of virtual patient solving with virtual patient construction exercises [100]; linear versus branched design of virtual patients [92]; and finally the addition of representation scaffolding (see glossary in Multimedia Appendix 1) to virtual patients [93].

Furthermore, 41 studies had 2 study arms (see the first table in Multimedia Appendix 3), 7 studies had 3 arms [62,88,91,95-98], and 3 studies had 4 arms [63,65,67].

In total, 41 studies involved preregistered professionals (see the first table in Multimedia Appendix 3), with 8 studies focused on postregistered participants [68,69,74,76,78,90,94,97]; 2 studies involved both pre- and postregistered participants [59,87].

In 37 out of 51 studies, participants were from the field of medicine. The studies from fields other than medicine were as follows: 6 studies in nursing [58,64,70,73,78,80]; 2 in pharmacy [53,92]; and 1 each in physical therapy [61], osteopathic medicine [84], and dentistry [83]. In addition, 3 studies involved interprofessional education [74,76,90].

A total of 44 out of 51 studies were conducted in high-income countries; 19 were from the United States (see the first table in Multimedia Appendix 3); 5 from Germany [65,82,93,98,99]; 3 each from Australia [52,70,91] and Sweden [83,85,87]; 2 each from Canada [59,60], the Netherlands [86,88], and the United Kingdom [66,77]; and 1 study was conducted each in Belgium and Switzerland [92], Denmark [100], France [54], Hong Kong [51], Japan [67], Poland [50], Singapore [64], and Slovenia [71]. From the 7 studies conducted in low-and-middle-income countries, 3 were from China [63,73,80], 2 from Colombia [55,56], and 1 each from the Republic of South Africa [94] and Iran [75].

In Multimedia Appendix 4 we present the technical characteristics of virtual patient systems, topics of educational content presented, applied instructional design methods, setting of use, information on the validity of outcome measurement, and applied educational theories in the included studies. Multimedia Appendix 5 summarizes the reasons for excluding studies following a review of their full-text versions.

A total of 11 studies reported attitudinal outcomes (see the third table in Multimedia Appendix 1). The attitudes related to confidence, preparedness, comfort, self-efficacy, and perceived ability in topics such as history taking and clinical breast examination [79], diagnostic and management abilities [59], contrast reaction management and teamwork [76], ethical, legal, and communication issues [57,81], opioid therapy [90], cultural competence [84], procedural knowledge in pediatric basic life support [82], performing pharmacy triage [92], caring for distress disorders patients [74], and anxiety [77].

The effects of interventions on attitudinal outcomes are presented in the fourth table in Multimedia Appendix 3. Furthermore, 3 studies presented pooled scores on students‘ self-assessment. In the study by Lehmann et al [82], students felt more confident in their knowledge and skills on performing pediatric basic life support with additional access to virtual patients that supplemented their traditional course (P<.001). There were no significant differences in the remaining 2 studies focusing on communication-related self-efficacy [81] and attitudes related to opioid therapy [90].

In the study by Williams et al [77], more items related to self-assessment of competences (in dealing with ethical aspects and managing anxiety) were scored lower in the virtual patient group than in the traditional education groups. There were no differences in analyzed items related to attitudes in 4 studies [57,59,74,79]. In the study by Smith et al [84], the results regarding attitudes toward clinical cultural competence were presented separately for bilingual and English-speaking students, which makes it difficult to aggregate not knowing the number of bilingual students in each study group. However, the descriptive conclusion of the authors was that general cultural competence measures were the same for the virtual patient and control group. In 2 studies [76,92], the results were compared item-by-item and only within the groups (pre/posttest), not between the study groups.

In total, 17 studies measured satisfaction resulting from an intervention (see the fifth table in Multimedia Appendix 3). All outcomes in this category were measured by satisfaction questionnaires. Different facets of satisfaction were measured, which we classified in the following 5 dimensions: general impression (global score or willingness to recommend), comfort in use (learning style preference, engagement or motivation, positive climate or safety, and enjoyment or pleasure), integration in curriculum (time constraints, relevance, and level of difficulty), academic factors (feedback quality, structure, and clarity), and satisfaction with technical features (usability and information technology readiness).

In 4 out of 17 studies evaluating the satisfaction of students receiving a virtual patient intervention, the result was presented as 1 aggregated score of several items. Furthermore, 3 of those studies compared different design variants of virtual patients. In the study by Friedman et al [96], the pedagogic format (menus, guided) resulted in higher satisfaction scores than the high-fidelity (free text, unguided) format (P<.01). There was no statistically significant difference between the virtual patients with and without usability enhancements [94] (P=.13) and solving versus constructing virtual patients [100] (P=.46). One study [58] presented comparison of virtual patients with mannequin-based training using a single score for student satisfaction and self-confidence in learning, showing no difference between the simulation modalities (P=.11).

In the remaining 13 out 17 studies, the survey responses were compared item-by-item. In 4 studies, the majority of the items indicated preference for the virtual patient intervention, in comparison with lecture [63], reading assignment [67], video-based learning [89], and Web tutorial [88]. In 7 studies, most items were indifferent between the groups [53,59,62,65,66,74,92]. In 1 study [76], most items (5 out of 6) in a satisfaction survey were better rated in the mannequin-based training than in the virtual patient group.

One study had cost-effectiveness as an outcome [58]. In 9 studies, statements were made regarding the cost of the intervention—either monetary or in development time [53,60,62,64-66,79,95]. Only 1 study provided numerical data on both types of intervention [95]. The comparison was qualitative in 3 studies [64,65,78]. In 5 studies, estimations of costs were made for the virtual patient group without contrasting it with the cost of the control intervention [53,60,62,66,79]. None of the included studies had patient outcomes or adverse effects as the main outcome measure. Even though none of the studies reported direct patient outcomes, in 2 studies, the participants were observed by raters while performing tasks on real patients as an outcome assessment [51,83]. In the study by Kumta et al [51], the score was included in more complex assessment (including MCQ tests and OSCE examination) and the patient-related outcome was not explicitly reported. In the study by Schittek Janda et al [83], first year students of dentistry were asked to perform history taking with real patients and were rated by the instructor. The patients’ perspective was, however, not considered. Even though none of the studies had adverse effects as the major outcome, 6 studies [53,55,67,70,84,88] reported findings related to noticed unexpected effects of the intervention.

None of the initially planned subgroup analyses explained the heterogeneity of the results.

Among many analyzed aspects, we looked into differences regarding the efficiency of learning with virtual patients between the health professions disciplines. Most of the located studies involved students of medicine as participants. For instance, when comparing virtual patients with traditional education in the domain of skills, out of the 12 outcomes included for subgroup analyses, only 2 were from other health profession disciplines than medicine (ie, studies from nursing [64,73]). When analyzing knowledge outcomes out of the 12 included studies, 4 were nonmedical but represented 3 very different disciplines, nursing [58,73], pharmacy [53], and physiotherapy [61]. The conducted subgroup analyses showed no significant differences between the subgroups and high heterogeneity.

While analyzing aspects of instructional design implemented in the virtual patient scenarios, we were able to locate a very balanced number of studies implementing the narrative and problem-solving designs [91] in the domain of knowledge outcomes (6 studies in each branch). Yet, the pooled results showed no difference (narrative: SMD=0.12, 95% CI −0.41 to 0.64, I²=85%, n=525 versus problem solving: SMD=0.11, 95% CI −0.17 to 0.38, I²=51%, n=520; subgroup differences P=.97). Interestingly, when looking into the domain of skills outcomes, all studies had either the problem-solving or unclear design (in 2 cases). This might be an indication that narrative (linear, branched) virtual patients are seen as being better suited for knowledge outcomes rather than skills.

Finally, we were unable to see any pattern in efficiency when analyzing the timing of feedback as being either during activity or post activity. However, in almost half of the studies, we were unable to decide, based on the description of the intervention, which model of feedback was implemented or whether the study had a mixed (during/post activity) mode of providing feedback.

To further explore the reasons for heterogeneity, we visualized the outcomes in the form of albatross plots of the knowledge and skills outcomes for virtual patients to traditional education comparisons. Figure 6 presents an albatross plot for knowledge and Figure 7 for skills outcomes. Comparisons of virtual patients to passive forms of learning (reading exercises and lectures) tended to display large positive effect sizes, whereas those comparing virtual patients to active learning (group discussion or mannequin-based learning) show small effects or even negative effects (left hand side in the Figures 6 and 7 and Multimedia Appendix 6).

Following the Cochrane methodology [40], we have assessed the risk of bias in all included studies. The results of the analysis are summarized in Figure 8.

Overall, we do not consider allocation bias as a significant issue in the review as most of the studies either described an adequate randomization method (17 of 51 studies) or even when the description was unclear (31 of 51), it was judged unlikely that the randomization was seriously flawed. Performance bias in comparisons with traditional education is an issue but at the same time is impossible to avoid in this type of research. The blinding of participants in virtual patient design comparisons is possible, but those studies are still relatively uncommon (n=10). The risk of assessor bias was avoided in many studies by using automated or formalized assessment instruments. Consequently, we assessed the risk as low in 42 of 51 studies. However, it is often unclear whether the instruments (eg, MCQ tests, assessment rubrics) were properly validated. We felt that in the majority of studies, attrition bias was within acceptable levels (low risk in 36 of 51 studies). This does not exclude volunteer bias, which is likely to be common, but its influence is difficult to estimate. As there is little tradition of publishing protocols in medical education research, it was problematic to assess selective reporting bias, but we judged the risk as low in 35 out of 51 studies. We were unable to reliably assess publication bias considering the high heterogeneity of studies. None of the cRCT studies considered in the statistical analysis had corrections for clustering, but we have decreased the number of participants in those studies using a method from the Cochrane Handbook to compensate for that. We present more details of the risk of bias analysis in Multimedia Appendix 7.

We rated down the quality of evidence for knowledge and skills outcomes in virtual patients to traditional education comparison because of the high heterogeneity of included studies and limitations in study design (lack of participant blinding, nonvalidated instruments, and potential volunteer bias). For attitudinal and satisfaction outcomes and for other types of comparisons, we additionally rated down the quality as the outcomes were presented as independent items in questionnaires that were not amenable to statistical analysis or the analyses contained just a handful of studies and the CIs were wide. Summary of findings table (GRADE) are presented in Multimedia Appendix 8.

The aim of this review was to evaluate the effectiveness of virtual patients in comparison with other existing educational methods.

There is low quality evidence that virtual patients are at least as effective as traditional education for knowledge outcome and more effective for skills outcomes. On the basis of the visual analysis of albatross plots, we may hypothesize that replacing passive forms of traditional education with virtual patients brings more benefit than replacing active learning methods. We collected positive evidence of effectiveness from both high-income and low-and-middle-income countries demonstrating the global applicability of virtual patients. Students were generally satisfied with the use of virtual patients, but we also located studies in our review where the use of virtual patients was connected with diminished confidence.

The strength of our systematic review is the broad perspective which shows the landscape of RCTs in the domain of virtual patients. Our systematic review updates the evidence on virtual patient effectiveness, which was last summarized in a meta-analysis almost a decade ago.

The limitation of our work is that the wide scope of the review does not allow nuances in the studies to be explored in detail. We were unable to make a firm assessment of publication bias. The high heterogeneity of the results leads to the conclusion that without further consideration of needs and implementation details, we cannot expect that the introduction of virtual patients will always lead to detectable positive outcomes. Evidence to determine the effective factors is sparse and represented by only 10 studies in our review, with very diverse research questions.

Our review is limited by the decision to exclude crossover design studies. However, this has been discussed in detail in the potential biases in the review process section in Multimedia Appendix 7. We excluded studies published before 1991 as we consider the technology available before the World Wide Web to be materially different from that currently available. Finally, we are limited by the sparse description of the interventions in some of the papers, which occasionally might have led to misclassification of the studies.

Extending the results of the meta-analysis by Cook et al [38] and in agreement with the one by Consorti et al [39], our review shows that virtual patients have an overall positive pooled effect when compared with some other types of traditional educational methods. Our observations regarding the influence of the type of outcome (knowledge/skills) and comparison (active/passive traditional learning) supplement the evidence in the previous reviews [38,39], which included studies until 2010. This time point divides the evidence collected in 2 parts: (1) time already covered by previous reviews (1991-2010) and (2) time not included in the previous reviews (2011-2018). It is interesting to note that, though the former timeframe spans over 20 years compared with 8 years in the latter, more studies were included from the latter period, 22 studies (until 2010) versus 29 studies (after 2010). This demonstrates increased interest in virtual patients and medical education in general. The research community around digital health education has long been criticized for publishing media-comparative studies [103,104]. Media-comparative research aims to make comparisons between different media formats such as paper, face-to-face, and digital education [104]. Both Friedman and Cook argue [103,104] that the limitations of this type of comparison boils down to the inability to produce an adequate control group as interventions are bound to be influenced by too many confounding factors to be generalizable. Even though there are still many media-comparative studies, the number of studies comparing different forms of digital education seem to increase: 3/22 (14%) until 2010 versus 11/29 (38%) after 2010. The number of studies in which students worked from home as an intervention has also increased; before 2011 there was just 1/16 (6%; in 6 studies it was unclear), whereas after 2011, it was 11/22 (50%; in 7 studies it was unclear) studies. However, this potentially raises concerns about how controlled the interventions and measures were, and thus the validity of the conclusions.

Our observation that virtual patient simulations predominantly effect skills rather than knowledge outcomes can be interpreted as an indication that for lower levels of Bloom's taxonomy [105], (remember, understands) there is little added value of introducing virtual patients when compared with traditional methods of education. Virtual patients can have greater impact when applied where knowledge is combined with skills and applied in problem solving, and when direct patient contact is not yet possible. We found little evidence to support the use of virtual patients at higher levels of the taxonomy. We also warn against using our result in justifying diminished hours of bedside teaching as this was investigated in just 1 study [75] and did not show positive outcomes. Consequently, virtual patients can be said to be a modality for learning in which learners actively use and train their clinical reasoning and critical thinking abilities before bedside learning, as was previously suggested in their critical literature review by Cook and Triola [22].

The perceptions of students toward studying with virtual patients are generally positive. However, some exceptions can be noted. In 1 study [77], students were less confident in their skills when compared with facilitated group discussion and lecture. This is in contrast with no observable differences or even better performance in the virtual patient group when considering the objective outcomes in those studies. This could be explained by disbelief in the effectiveness of the new computer-based methods of learning or anxiety of losing direct patient contact.

The results of our subgroup analysis, though inconsistent, encourage the introduction of more active forms of education. Yet, we note that the range from active to passive learning forms a continuum, and the decision on how to classify each intervention is hampered by sparse descriptions in the reports. Nevertheless, questioning the utility of passive learning is not a new finding and is observed elsewhere, for instance, in the literature on the flipped-classroom learning approach [106]. As the effects of comparing virtual patients with other forms of active learning were small and we could not detect any other variables explaining the heterogeneity, it seems reasonable to individually consider other factors such as cost of use, time flexibility, personnel shortage, and availability in different settings (eg, students’ homes or locations remote from academic centers) when determining which methods to use.

The need for more guidance within virtual patient simulations is apparent in studies differing by instructional methods where narrative virtual patient design was better than more autonomous problem-oriented designs [91]. Feedback given by humans at distance in a virtual patient system was better than an animated backstory in increasing empathy [95], whereas more active constructing virtual patients with more time on a task but no feedback had no more positive result on the outcomes than learning from a virtual patient scenario [100]. This reminds us that presenting realistic patient scenarios with a great degree of freedom cannot be an excuse for neglecting guidance in relation to learning objectives [107,108].

We join the plea of Friedman [103] and Cook [104] to abandon media-comparative research as it is difficult to interpret and we instead encourage greater focus on exploring the utility of different design variants of virtual patient simulations. The current knowledge on the influence of these factors is sparse. A carefully planned study backed up in sound educational theory should provide many valuable research opportunities. However, sufficiently powered samples are needed, as the effects are likely to be small. The second consideration pertains to the need to use previously validated measurement tools that are well-aligned with the learning objectives. Comparisons of outcomes in tools on an item-by-item basis is methodologically questionable and makes the aggregations of results difficult in systematic reviews. We also call for more studies in other health professions disciplines than medicine, as our subgroup analysis showed that evidence of virtual patient effectiveness in such programs as nursing, physiotherapy, or pharmacy is underrepresented. Investigations into patient outcomes and cost-effectiveness of virtual patients are not yet explored directly and form a key avenue for future efforts.

Low to modest and mixed evidence suggests that when compared with traditional education, virtual patients can more effectively improve skills, and at least as effectively improve knowledge outcomes as traditional education. Education with virtual patients provides an active form of learning that is beneficial for clinical reasoning skills. Implementations vary and are likely to be broad across pre- and postregistration education, although current studies do not provide clear guidance on when to use virtual patients. We recommend further research be focused on exploring the utility of different design variants of virtual patients.