Cost-Effectiveness of Virtual Patient Integration in Dermatology Education: Evidence for Precision Education by Learner Expertise

Cost-Effectiveness of Virtual Patient Integration in Dermatology Education: Evidence for Precision Education by Learner Expertise | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cost-Effectiveness of Virtual Patient Integration in Dermatology Education: Evidence for Precision Education by Learner Expertise Chih-Tsung Hung, Sheng-Wen Liu, Shou-En Wu, Feng-Cheng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9467306/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Educational technology adoption accelerates despite limited evidence of incremental value within already-effective curricula. Pragmatic evaluations examining cost-effectiveness and differential benefits across learner populations with varying expertise remain scarce. Objectives To evaluate virtual patient integration into established flipped classroom curriculum, examining incremental educational outcomes, comprehensive cost-effectiveness, and systematic variation by training level. Methods Sequential implementation study at a tertiary teaching hospital in Taiwan (2022–2025). Flipped classroom (FC; 2022–2023, N = 131) was followed by FC with virtual patients (FC + VP; 2024–2025, N = 161). Primary outcomes: knowledge acquisition (Cohen's d, achievement rates ≥ 80%). Incremental cost-effectiveness ratios (ICERs) were calculated by training level. Results Both approaches achieved extremely large learning effects (FC: d = 2.27; FC + VP: d = 2.43; >98% achievement; between-group differences non-significant). FC + VP required 3.08-fold higher per-student investment ( $ 163 vs $ 53). Cost-effectiveness varied dramatically: undergraduates showed meaningful gains (Δd = + 0.29, ICER = $ 383/d), while postgraduates showed minimal benefit (Δd = + 0.02, ICER = $ 4,670/d)—a 12.2-fold difference. Germane cognitive load predicted learning progress (r = 0.521, R² = 27.1%); critical thinking did not (p = .421). Conclusions Near-universal competency is attainable through multiple instructional approaches. Virtual patient integration provides differential incremental value by learner expertise—meaningful for novices but negligible for advanced learners achieving comparable outcomes more efficiently. Findings support precision education frameworks targeting technology investments based on cost-effectiveness profiles rather than universal deployment. Virtual patients Flipped classroom Cost-effectiveness Expertise reversal Precision education Medical education 1 | INTRODUCTION Medical education faces increasing fiscal pressure as educational technology costs escalate while institutional budgets remain constrained [ 1 , 2 ]. Virtual patients—computer-based simulations of clinical scenarios—represent substantial investment for medical schools, with platform licensing, development, and maintenance costs often exceeding hundreds of thousands of dollars annually [ 3 , 4 ]. Despite enthusiastic adoption, evidence demonstrating incremental educational value when integrated into already-effective curricula remains limited, and comprehensive cost-effectiveness analyses are scarce [ 5 , 6 ]. This evidence gap impedes informed resource allocation decisions when medical education requires sustainable innovation strategies. Flipped classroom approaches, which shift content delivery outside class to dedicate in-class time to active learning, have demonstrated consistent effectiveness in medical education [ 7 , 8 ]. Recent meta-analyses confirm that flipped classrooms produce superior knowledge acquisition compared to traditional lecture-based instruction, with effect sizes ranging from moderate to large across diverse medical disciplines [ 9 – 14 ]. The pedagogical success of flipped classrooms raises a critical question: does adding expensive virtual patient technology to an already-effective flipped classroom provide sufficient incremental benefit to justify the substantial additional investment? Efficacy trials establishing that virtual patients can improve learning do not address whether they provide meaningful added value beyond simpler, more cost-efficient active learning approaches that may achieve comparable outcomes [ 15 – 19 ]. Cognitive load theory offers a framework for understanding potential differential benefits of enhanced educational scaffolding [ 20 , 21 ]. The theory predicts that instructional support requirements vary with learner expertise: novice learners benefit from guided instruction reducing cognitive load, while advanced learners with well-developed schemas may find additional guidance redundant—the expertise reversal effect [ 22 , 23 ]. This theoretical prediction suggests that virtual patient integration might provide differential value, with meaningful benefits for early learners lacking clinical experience but minimal gains for advanced learners who achieve comparable outcomes more efficiently. Despite robust theoretical grounding and experimental support,[ 23 – 25 ] the expertise reversal effect has not been systematically examined in authentic medical education contexts with cost-effectiveness analysis. The precision education concept—adapting instructional approaches to learner characteristics—has gained prominence as medical education adopts competency-based frameworks requiring individualized learning paths [ 26 – 29 ]. However, precision education implementations have focused primarily on pedagogical adaptation without incorporating economic considerations. Understanding not only which learners benefit from enhanced technology but also whether the incremental benefit justifies the incremental investment is essential for sustainable precision education implementation. This study addresses these gaps by conducting pragmatic evaluation of virtual patient integration into established flipped classroom curriculum in dermatology education. We examined three interrelated questions: (1) Does virtual patient integration provide incremental educational benefit when added to effective flipped classroom? (2) How does cost-effectiveness vary by learner expertise level (undergraduate medical students versus postgraduate residents)? (3) What mechanisms (germane cognitive load, critical thinking) mediate relationships between instructional approach and learning outcomes? By integrating educational effectiveness evaluation with comprehensive cost-effectiveness analysis stratified by training level, this study provides evidence to guide resource allocation decisions and inform precision education frameworks. 2 | METHODS 2.1 | Study design Sequential two-group design comparing flipped classroom (FC) versus FC enhanced with virtual patients (FC + VP) at Department of Dermatology, [institution blinded for review], Taiwan. FC implemented 2022–2023 (baseline), FC + VP 2024–2025. IRB approved (TSGHIRB No. C202105012). 2.2 | Participants During 2022–2025, 315 students enrolled: FC period 145 students (93 undergraduate [UGY], 52 postgraduate residents [PGY]); FC + VP period 170 students (84 UGY, 86 PGY). UGY: fifth-year clinical clerkship (2-week rotations). PGY: first- and second-year residents (4-week rotations). Analysis: FC N = 131 (85 UGY, 46 PGY, 90.3% completion), FC + VP N = 161 (79 UGY, 82 PGY, 94.7% completion), yielding N = 292 (92.7% completion, Supplementary Figure S1 ). 2.3 | Interventions Both targeted identical objectives: competent scabies diagnosis and management [ 30 ]. FC: pre-class video lectures (15 minutes), clinical atlas, readings, quiz; in-class (90 minutes): review, case discussions, Q&A. FC + VP: identical FC components plus one interactive virtual patient case (30 minutes total, Virti platform) featuring diverse contexts, interactive examination, diagnostic scaffolds, immediate feedback. All other components constant across periods to isolate virtual patient effects. 2.4 | Outcomes Primary: 10-item examination (clinical recognition, diagnostic approach, treatment, patient education). Measures: learning progress (post-pre), Cohen's d effect size, achievement rate (≥ 80%). Secondary (FC + VP only): critical thinking disposition (α = 0.933), cognitive load [ 31 ] (intrinsic α = 0.70, extraneous α = 0.87, germane α = 0.84). Cost-effectiveness: institutional costs (development, operations, technology, materials). Incremental cost-effectiveness ratios (ICERs): [Cost per student (FC + VP) − Cost per student (FC)] / [Cohen's d (FC + VP) − Cohen's d (FC)], computed overall and by training level. 2.5 | Statistical analysis Independent samples t-tests compared baseline knowledge and post-intervention outcomes; paired samples t-tests assessed within-group changes. Chi-square tests compared achievement rates. Pearson correlations examined relationships between process measures and outcomes. Linear regression tested whether process variables predicted progress controlling for training level. α = .05 (two-tailed). Complete case analysis: 292/315 (92.7%) completed assessments; 23 (7.3%) with incomplete questionnaires excluded (FC: n = 14/145; FC + VP: n = 9/170). Completion rates were similar across training levels (UGY: 92.7%; PGY: 92.8%; χ²(1) = 0.001, p = .975). 2.6 | Use of artificial intelligence and statistical software Figure S1 (Participant Flow) was generated using Google Gemini (Gemini 1.5 Pro, Google LLC, Mountain View, CA) based on actual research data collected from this study. The AI tool was used solely for visual presentation and formatting of pre-existing data to enhance clarity and professional appearance; all underlying numerical data were verified against source records to ensure accuracy. Supplementary Figure S2 (Incremental Cost-Effectiveness Ratios by Training Level) was produced using R version 4.4 (R Foundation for Statistical Computing, Vienna, Austria) with the ggplot2 package [ 32 ]. All statistical analyses were performed using conventional statistical methods without AI assistance. No AI-generated content was used in the conception, design, data collection, analysis, interpretation, or writing of this manuscript. This use of AI tools complies with Springer Nature author guidelines. 3 | RESULTS 3.1 | Participant characteristics and baseline comparability Of 315 students (145 FC, 170 FC + VP), 292 (92.7%) completed assessments (Supplementary Figure S1 ). Final analysis: FC N = 131 (85 UGY, 46 PGY), FC + VP N = 161 (79 UGY, 82 PGY). Baseline knowledge was comparable between FC and FC + VP groups overall (55.42% vs 56.02%, t (289) = 0.31, p = .761) and within training levels (UGY: 55.06% vs 57.47%, t (161) = 0.92, p = .361; PGY: 56.09% vs 54.63%, t (125) = 0.46, p = .644), supporting baseline comparability (Table 1 ). Table 1 Baseline Characteristics and Knowledge Assessment Characteristic FC FC + VP p -value Overall N = 131 N = 161 Pre-test score, mean (SD) 55.42% (17.99) 56.02% (15.94) .761 Undergraduate Students (UGY) n 85 79 Pre-test score, mean (SD) 55.06% (18.49) 57.47% (14.80) .361 Postgraduate Residents (PGY) n 46 82 Pre-test score, mean (SD) 56.09% (17.19) 54.63% (16.94) .644 Note. FC = Flipped Classroom; FC + VP = Flipped Classroom with Virtual Patients; UGY = Undergraduate medical students (fifth-year clinical clerkship); PGY = Postgraduate year 1–2 residents; SD = Standard Deviation. p -values from independent samples t -tests. All p > .05, indicating no significant baseline differences between periods. 3.2 | Learning outcomes and educational effectiveness Both instructional approaches achieved extremely large learning effects with near-universal competency achievement, consistent with recent evidence on virtual reality and educational technology effectiveness in medical education [ 15 , 16 , 33 , 34 ]. In the FC group, undergraduate students demonstrated mean learning progress of 41.18 percentage points (Cohen's d = 2.23, 98.8% achieving ≥ 80% post-test mastery), while postgraduate residents showed mean progress of 40.43 percentage points ( d = 2.35, 97.8% achievement). In the FC + VP group, undergraduates progressed 37.22 percentage points ( d = 2.51, 100% achievement) and postgraduates progressed 40.24 percentage points ( d = 2.38, 97.6% achievement) (Table 2 ). All within-group pre-post comparisons reached strong statistical significance (all p < .001), reflecting substantial and robust learning gains across all conditions and training levels. Substantial ceiling effects were evident, with 73–85% of participants achieving perfect post-test scores, suggesting assessments captured learning to competency but potentially lacked discrimination at highest performance levels. Table 2 Learning Outcomes by Training Level Between-group comparisons revealed no statistically significant differences in post-test performance or achievement rates. Overall post-test scores were statistically equivalent (FC: 96.34% vs FC + VP: 94.78%, t (289) = 1.42, p = .157). Achievement rates exceeded 98% in both groups (FC: 98.5% vs FC + VP: 98.8%, χ²(1) = 0.10, p = .748). Within specific training levels, no significant differences emerged for any outcome measure (UGY post-test: t (161) = 1.08, p = .283; PGY post-test: t (125) = 0.94, p = .348; UGY achievement: χ²(1) = 1.00, p = 1.000; PGY achievement: χ²(1) = 0.01, p = 1.000). While FC + VP groups demonstrated numerically larger effect sizes compared to FC groups (overall: Δ d = + 0.16; UGY: Δ d = + 0.29; PGY: Δ d = + 0.02), these incremental differences did not reach conventional statistical significance thresholds, indicating educational equivalence between instructional approaches on primary knowledge acquisition outcomes (Table 2 ). Exploratory baseline knowledge stratification analysis suggested patterns of differential response warranting future investigation. Among learners entering with low baseline knowledge (≤ 40% pre-test scores), both approaches achieved remarkably large effect sizes (FC: d = 7.07, FC + VP: d = 6.93, Δ d = − 0.14), with FC achieving 95.9% mastery and FC + VP achieving 100% mastery, though this difference did not reach statistical significance (χ²(1) = 1.52, p = .466). Among learners with medium baseline knowledge (40–60% pre-test), both approaches achieved 100% mastery in FC and 97.6% in FC + VP (χ²(1) = 2.08, p = .692). Among learners with high baseline knowledge (> 60% pre-test), both approaches achieved 100% mastery rates. The inability to calculate meaningful effect sizes for medium and high baseline strata ( d = 0.00 due to ceiling effects) suggests potential ceiling constraints on observable differential effectiveness at higher baseline knowledge levels (Supplementary Table S1 ). 3.3 | Process measures: cognitive load and critical thinking Within the FC + VP group, germane cognitive load—representing learning-directed cognitive processing—showed moderate positive correlation with learning progress ( r = 0.521, p < .001), while critical thinking disposition showed moderate positive correlation in bivariate analysis ( r = 0.342, p = .010). Multiple linear regression controlling for training level and all process measures simultaneously confirmed that germane cognitive load was the only significant predictor of learning progress ( β = 3.96, SE = 1.62, p = .015), while critical thinking did not predict unique variance ( β = 0.98, SE = 1.15, p = .421), indicating that approximately 27.1% of variance in learning outcomes was attributable to cognitive load factors collectively ( R ² = .271; Supplementary Table S2). The modest explained variance aligns with cognitive load theory predictions while reflecting the multifactorial reality of learning outcomes in authentic educational contexts. 3.4 | Cost-effectiveness analysis Annual operational costs differed substantially between instructional approaches. The FC approach required $ 3,460 annually serving 66 students ( $ 53 per student), while the FC + VP approach required $ 13,108 annually serving 80 students ( $ 163 per student), representing a 3.08-fold higher per-student investment associated with virtual patient integration. Cost-effectiveness analysis revealed dramatic variation by training level (Supplementary Figure S2). For undergraduate medical students, virtual patient integration demonstrated favorable cost-effectiveness with an incremental cost-effectiveness ratio (ICER) of $ 383 per Cohen's d unit, reflecting meaningful effect size gains (Δ d = + 0.29) coupled with achievement of universal mastery (100% vs 98.8%). In striking contrast, for postgraduate residents the ICER was $ 4,670 per Cohen's d unit, reflecting minimal incremental benefit (Δ d = + 0.02) with no observable change in achievement rates (both groups 97.6–97.8%). This represents a 12.2-fold difference in cost-effectiveness between training levels, with virtual patient integration providing substantially superior value for novice learners compared to advanced learners who achieved comparable educational outcomes more efficiently through the less resource-intensive flipped classroom approach (Table 3 ). Table 3 Cost-Effectiveness Analysis by Training Level 4 | DISCUSSION This pragmatic evaluation reveals that both flipped classroom alone (FC) and flipped classroom with virtual patients (FC + VP) achieved extremely large learning effects (Cohen's d > 2.2) with near-universal competency achievement (> 98%), demonstrating educational equivalence on primary knowledge acquisition outcomes. However, comprehensive cost-effectiveness analysis revealed dramatic differential value by learner expertise: virtual patient integration provided meaningful incremental benefit for undergraduate students (Δ d = + 0.29, universal achievement, ICER = $ 383 per Cohen's d unit) but negligible gains for postgraduate residents (Δ d = + 0.02, ICER = $ 4,670 per Cohen's d unit)—a striking 12.2-fold difference in cost-effectiveness (Supplementary Figure S2). Germane cognitive load showed moderate correlation with learning progress ( r = 0.521, R ² = 27.1% for cognitive load factors collectively), while critical thinking showed moderate bivariate correlation ( r = 0.342) but no independent predictive value in multivariate analysis. These findings collectively support precision education frameworks wherein technology investments are strategically targeted based on demonstrated cost-effectiveness profiles and learner expertise characteristics. The extremely large effect sizes in both groups align with meta-analytic evidence demonstrating flipped classroom superiority over traditional instruction across medical disciplines [ 7 – 12 , 14 ]. Near-universal achievement rates (> 98%) indicate that well-designed active learning environments can successfully bring learners to competency regardless of technological enhancement, with important implications for competency-based medical education [ 35 ]. The observed pattern—meaningful gains for undergraduates but minimal for postgraduates—is consistent with cognitive load theory's expertise reversal effect [ 22 , 24 ]. A recent meta-analysis of 60 studies confirms that low prior knowledge learners benefit from high-assistance instruction ( d = 0.505) while high prior knowledge learners benefit from low-assistance instruction ( d = − 0.428) [ 23 ]. For undergraduate students lacking dermatological clinical exposure, virtual patients provided structured scaffolding reducing extraneous load while promoting germane load through realistic case engagement. For postgraduate residents with developed clinical schemas from patient care rotations, virtual patient scaffolding likely introduced redundancy relative to internal knowledge structures, potentially increasing cognitive load—the hallmark of expertise reversal. The 12.2-fold ICER difference quantifies that targeted deployment is not merely pedagogically sound but economically essential for sustainable implementation [ 3 , 4 ]. Germane cognitive load's moderate correlation with learning progress ( r = 0.521, explaining approximately 27% of variance when considering all cognitive load factors collectively) aligns with cognitive load theory predictions,[ 20 , 21 , 31 ] while reflecting real-world educational complexity where multiple factors influence outcomes. Critical thinking's moderate bivariate correlation ( r = 0.342) but lack of independent predictive value ( β = 0.98, p = .421) in multivariate analysis suggests that general cognitive dispositions may not directly drive knowledge acquisition in focused interventions beyond their association with learning-directed cognitive processing, consistent with domain-specificity principles.[ 16 ] Several important limitations warrant consideration. First, sequential implementation raises concerns about temporal confounding and history bias. However, multiple factors mitigate this concern: baseline knowledge equivalence ( p = .761), consistent instructor and curriculum across periods, identical assessments, and stable institutional context. The extremely large effect sizes in both periods ( d > 2.2) suggest robust effectiveness independent of temporal factors. Moreover, the observed pattern—meaningful gains for undergraduates but minimal for postgraduates—aligns precisely with expertise reversal theory predictions [ 22 , 23 ] rather than reflecting secular trends, as temporal effects would be expected to influence both groups similarly. While randomized concurrent designs would provide stronger causal inference, the convergent evidence from baseline equivalence, theoretical alignment, and differential response patterns by expertise level supports validity of cost-effectiveness conclusions. Second, substantial ceiling effects present critical measurement limitations. Postgraduate residents achieved near-universal competency (FC: 97.8% vs FC + VP: 97.6%, p = 1.000) with minimal incremental effect from virtual patients (Δ d = + 0.02), while 77–85% attained perfect post-test scores. This strongly suggests the assessment tool lacked sufficient difficulty to discriminate learning gains at high performance levels. The apparent lack of incremental benefit for postgraduates should be interpreted cautiously—it may partially reflect assessment insensitivity rather than true absence of learning gains. Future research should employ assessments with greater difficulty and discrimination at high performance levels—incorporating complex clinical scenarios, atypical presentations, or management of complications—to detect potential subtle benefits current measures cannot capture. However, this measurement limitation paradoxically strengthens rather than weakens cost-effectiveness conclusions from a pragmatic perspective. Even assuming unmeasured incremental benefits exist for postgraduates, three converging factors support conclusions: (1) both approaches successfully achieved bringing learners to competency (> 97% achievement rates); (2) the 3.08-fold higher per-student investment ( $ 163 vs $ 53) represents substantial additional resource commitment; (3) any unmeasured benefits would necessarily be marginal improvements beyond already-near-perfect performance. The critical question becomes whether potential marginal improvements beyond near-universal competency achievement justify substantial additional investment, particularly when simpler approaches achieve comparable competency rates more efficiently. Third, this study focused on scabies management—a relatively circumscribed dermatological condition. Generalizability to more complex scenarios requires empirical verification. However, scabies was deliberately selected for several compelling reasons: it represents essential foundational knowledge encountered across all medical specialties; it requires integrating clinical recognition with diagnostic procedures; it demands authentic clinical reasoning including pattern recognition across varying presentations (classic to crusted), evidence-based treatment decision-making considering patient-specific factors, and patient education regarding transmission and compliance. Focusing on a single well-defined condition with identical learning objectives provided experimental control for rigorous cost-effectiveness comparison isolating virtual patient effects. The observed expertise reversal pattern reflects fundamental cognitive load principles likely to generalize across content domains. Nevertheless, future research should examine whether cost-effectiveness differentials persist across more complex dermatological conditions and other medical domains. Additional limitations include that process measures were assessed only in the FC + VP group, preventing direct mechanistic comparison between approaches; findings derive from one institution requiring cross-cultural and cross-institutional validation; and cost-effectiveness analysis captured direct educational costs without accounting for indirect costs (opportunity costs, long-term retention effects) or broader value considerations (learner satisfaction, clinical performance outcomes). These findings offer practical guidance for educational leaders. First, excellent outcomes are achievable through multiple pedagogical approaches; pedagogical principles matter more than technological sophistication for foundational knowledge acquisition. Second, technology investments should be targeted rather than universal. The 12.2-fold ICER difference indicates indiscriminate deployment is economically inefficient. Institutions should prioritize enhanced technologies for learners with demonstrated need while employing simpler approaches for advanced learners achieving comparable outcomes efficiently. Third, comprehensive evaluation frameworks integrating educational effectiveness with cost-effectiveness analysis are essential for informed decision-making,[ 3 , 36 , 37 ] as pedagogical equivalence may mask substantial economic disparities with important implications for sustainable resource allocation. In conclusion, this pragmatic evaluation demonstrates that while flipped classroom approaches achieve near-universal competency, the incremental value of virtual patient integration varies dramatically by learner expertise. Findings support precision education frameworks wherein technology investments are strategically targeted based on cost-effectiveness profiles rather than deployed universally. As medical education adopts competency-based models requiring individualized learning paths,[ 29 , 35 ] understanding the economic sustainability of pedagogical approaches becomes as essential as evaluating educational effectiveness, particularly as artificial intelligence and digital technologies continue to transform medical education delivery and create new opportunities for precision education implementation [ 38 – 41 ]. Declarations Acknowledgments We thank all students who participated in this study and the educational staff at the Department of Dermatology, Tri-Service General Hospital, for their support during curriculum implementation. Funding This study was supported by grants from Tri-Service General Hospital Research Foundation (TSGH-E-114224, TSGH-E-115231). The funding source had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflicts of Interest The authors declare no conflicts of interest. Ethical Approval This study was approved by the Institutional Review Board of Tri-Service General Hospital (TSGHIRB No. C202105012). All participants provided informed consent. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Data are not publicly available due to privacy and ethical restrictions. Authors' Contributions C.-T.H.: Conceptualization, methodology, investigation, data collection, formal analysis, writing – original draft, writing – review & editing. S.-W.L.: Methodology, data collection, writing – review & editing. S.-E.W.: Data collection, writing – review & editing. F.-C.L.: Conceptualization, supervision, writing – review & editing, corresponding author. All authors read and approved the final manuscript. Competing Interests The authors declare that they have no competing interests. Consent for Publication Not applicable. References Wartman SA, Combs CD. 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The effectiveness of virtual reality technology in student nurse education: a systematic review and meta-analysis. Nurse Educ Today. 2024;138:106189. https://doi.org/10.1016/j.nedt.2024.106189 . Sadek O, Baldwin F, Gray R, et al. Impact of virtual and augmented reality on quality of medical education during the COVID-19 pandemic: a systematic review. J Grad Med Educ. 2023;15(3):328–38. https://doi.org/10.4300/JGME-D-22-00594.1 . Tulshian P, Montgomery L, McCrory K, et al. National recommendations for implementation of competency-based medical education in family medicine. Fam Med. 2025;57(4):253–60. https://doi.org/10.22454/FamMed.2025.866091 . Dietze DT, Frimpter J. It is time to include cost-effectiveness in CME outcomes measurement. J CME. 2025;14(1):2565919. https://doi.org/10.1080/28338073.2025.2565919 . Walsh K. Cost effectiveness in medical education. London: Radcliffe Publishing; 2010. Gazquez-Garcia J, Sanchez-Bocanegra CL, Sevillano JL. AI in the health sector: systematic review of key skills for future health professionals. JMIR Med Educ. 2025;11(1):e58161. https://doi.org/10.2196/58161 . Huang S, Wen C, Bai X, et al. Exploring the application capability of ChatGPT as an instructor in skills education for dental medical students: randomized controlled trial. J Med Internet Res. 2025;27:e68538. https://doi.org/10.2196/68538 . Wartman SA, Combs CD. Reimagining medical education in the age of AI. AMA J Ethics. 2019;21(2):E146–152. https://doi.org/10.1001/amajethics.2019.146 . Car J, Ong QC, Erlikh Fox T, et al. The digital health competencies in medical education framework: an international consensus statement based on a delphi study. JAMA Netw Open. 2025;8(1):e2453131. https://doi.org/10.1001/jamanetworkopen.2024.53131 . Table 2 and 3 Table 2 and 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx TABLE23.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 25 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 20 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9467306","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":636708611,"identity":"1a85c79f-1112-4be8-b86f-433bb3b89e3d","order_by":0,"name":"Chih-Tsung Hung","email":"","orcid":"","institution":"National Defense Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chih-Tsung","middleName":"","lastName":"Hung","suffix":""},{"id":636708612,"identity":"def26fe6-e844-4867-ac7b-a88ce5556e38","order_by":1,"name":"Sheng-Wen Liu","email":"","orcid":"","institution":"National Defense Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sheng-Wen","middleName":"","lastName":"Liu","suffix":""},{"id":636708613,"identity":"5f7b2978-ee99-4d22-9fa1-2342c9140fce","order_by":2,"name":"Shou-En Wu","email":"","orcid":"","institution":"National Defense Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shou-En","middleName":"","lastName":"Wu","suffix":""},{"id":636708615,"identity":"87be82e7-c882-4c39-965b-1482fb5b696d","order_by":3,"name":"Feng-Cheng Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACPuYDDAcSftjI8YN4CQVEaGFjS2A88LEnzViyAaTFgDgtzAdnsB1O3HAAxCVOC/ODwzw8hxM3n1+d+OGBAYM8v9gBQlrYDA7zWKQbb7vxdrME0GGGM2cnENAi38MAtMVadtuNsxtAWhIMbhPSwsYD1MLGzLh5xtnNP4jWAvS+s+IG/t5txNrCZgAOZIkbvNssEgwkCPuFn4358QdwVPaf3XzzR4WNPL80AS0IIAFWKUGscrB9B0hRPQpGwSgYBSMJAAAQaUOYbeD7DQAAAABJRU5ErkJggg==","orcid":"","institution":"National Defense Medical University","correspondingAuthor":true,"prefix":"","firstName":"Feng-Cheng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-20 05:53:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9467306/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9467306/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109026662,"identity":"2cd78f5f-3693-4f63-9485-4613f38a5212","added_by":"auto","created_at":"2026-05-11 21:25:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":291982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9467306/v1/bfab5cd9-2ac2-44f6-a72b-3221aa09d4ea.pdf"},{"id":109026646,"identity":"1650291c-3efd-46cd-805e-22510ad4996c","added_by":"auto","created_at":"2026-05-11 21:25:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":214171,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9467306/v1/17c1b2257ae98e81bd3f1656.docx"},{"id":109026586,"identity":"52233ed1-ed3e-4619-b6b6-5612a5a52371","added_by":"auto","created_at":"2026-05-11 21:24:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18451,"visible":true,"origin":"","legend":"","description":"","filename":"TABLE23.docx","url":"https://assets-eu.researchsquare.com/files/rs-9467306/v1/7867efd9d5c141301e85e765.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cost-Effectiveness of Virtual Patient Integration in Dermatology Education: Evidence for Precision Education by Learner Expertise","fulltext":[{"header":"1 | INTRODUCTION","content":"\u003cp\u003eMedical education faces increasing fiscal pressure as educational technology costs escalate while institutional budgets remain constrained [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Virtual patients\u0026mdash;computer-based simulations of clinical scenarios\u0026mdash;represent substantial investment for medical schools, with platform licensing, development, and maintenance costs often exceeding hundreds of thousands of dollars annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite enthusiastic adoption, evidence demonstrating incremental educational value when integrated into already-effective curricula remains limited, and comprehensive cost-effectiveness analyses are scarce [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This evidence gap impedes informed resource allocation decisions when medical education requires sustainable innovation strategies.\u003c/p\u003e \u003cp\u003eFlipped classroom approaches, which shift content delivery outside class to dedicate in-class time to active learning, have demonstrated consistent effectiveness in medical education [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent meta-analyses confirm that flipped classrooms produce superior knowledge acquisition compared to traditional lecture-based instruction, with effect sizes ranging from moderate to large across diverse medical disciplines [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pedagogical success of flipped classrooms raises a critical question: does adding expensive virtual patient technology to an already-effective flipped classroom provide sufficient incremental benefit to justify the substantial additional investment? Efficacy trials establishing that virtual patients can improve learning do not address whether they provide meaningful added value beyond simpler, more cost-efficient active learning approaches that may achieve comparable outcomes [\u003cspan additionalcitationids=\"CR16 CR17 CR18\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCognitive load theory offers a framework for understanding potential differential benefits of enhanced educational scaffolding [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The theory predicts that instructional support requirements vary with learner expertise: novice learners benefit from guided instruction reducing cognitive load, while advanced learners with well-developed schemas may find additional guidance redundant\u0026mdash;the expertise reversal effect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This theoretical prediction suggests that virtual patient integration might provide differential value, with meaningful benefits for early learners lacking clinical experience but minimal gains for advanced learners who achieve comparable outcomes more efficiently. Despite robust theoretical grounding and experimental support,[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] the expertise reversal effect has not been systematically examined in authentic medical education contexts with cost-effectiveness analysis.\u003c/p\u003e \u003cp\u003eThe precision education concept\u0026mdash;adapting instructional approaches to learner characteristics\u0026mdash;has gained prominence as medical education adopts competency-based frameworks requiring individualized learning paths [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, precision education implementations have focused primarily on pedagogical adaptation without incorporating economic considerations. Understanding not only which learners benefit from enhanced technology but also whether the incremental benefit justifies the incremental investment is essential for sustainable precision education implementation.\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by conducting pragmatic evaluation of virtual patient integration into established flipped classroom curriculum in dermatology education. We examined three interrelated questions: (1) Does virtual patient integration provide incremental educational benefit when added to effective flipped classroom? (2) How does cost-effectiveness vary by learner expertise level (undergraduate medical students versus postgraduate residents)? (3) What mechanisms (germane cognitive load, critical thinking) mediate relationships between instructional approach and learning outcomes? By integrating educational effectiveness evaluation with comprehensive cost-effectiveness analysis stratified by training level, this study provides evidence to guide resource allocation decisions and inform precision education frameworks.\u003c/p\u003e"},{"header":"2 | METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 | Study design\u003c/h2\u003e \u003cp\u003e Sequential two-group design comparing flipped classroom (FC) versus FC enhanced with virtual patients (FC\u0026thinsp;+\u0026thinsp;VP) at Department of Dermatology, [institution blinded for review], Taiwan. FC implemented 2022\u0026ndash;2023 (baseline), FC\u0026thinsp;+\u0026thinsp;VP 2024\u0026ndash;2025. IRB approved (TSGHIRB No. C202105012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 | Participants\u003c/h2\u003e \u003cp\u003eDuring 2022\u0026ndash;2025, 315 students enrolled: FC period 145 students (93 undergraduate [UGY], 52 postgraduate residents [PGY]); FC\u0026thinsp;+\u0026thinsp;VP period 170 students (84 UGY, 86 PGY). UGY: fifth-year clinical clerkship (2-week rotations). PGY: first- and second-year residents (4-week rotations). Analysis: FC N\u0026thinsp;=\u0026thinsp;131 (85 UGY, 46 PGY, 90.3% completion), FC\u0026thinsp;+\u0026thinsp;VP N\u0026thinsp;=\u0026thinsp;161 (79 UGY, 82 PGY, 94.7% completion), yielding N\u0026thinsp;=\u0026thinsp;292 (92.7% completion, Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 | Interventions\u003c/h2\u003e \u003cp\u003eBoth targeted identical objectives: competent scabies diagnosis and management [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. FC: pre-class video lectures (15 minutes), clinical atlas, readings, quiz; in-class (90 minutes): review, case discussions, Q\u0026amp;A. FC\u0026thinsp;+\u0026thinsp;VP: identical FC components plus one interactive virtual patient case (30 minutes total, Virti platform) featuring diverse contexts, interactive examination, diagnostic scaffolds, immediate feedback. All other components constant across periods to isolate virtual patient effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 | Outcomes\u003c/h2\u003e \u003cp\u003ePrimary: 10-item examination (clinical recognition, diagnostic approach, treatment, patient education). Measures: learning progress (post-pre), Cohen's d effect size, achievement rate (\u0026ge;\u0026thinsp;80%). Secondary (FC\u0026thinsp;+\u0026thinsp;VP only): critical thinking disposition (α\u0026thinsp;=\u0026thinsp;0.933), cognitive load [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] (intrinsic α\u0026thinsp;=\u0026thinsp;0.70, extraneous α\u0026thinsp;=\u0026thinsp;0.87, germane α\u0026thinsp;=\u0026thinsp;0.84). Cost-effectiveness: institutional costs (development, operations, technology, materials). Incremental cost-effectiveness ratios (ICERs): [Cost per student (FC\u0026thinsp;+\u0026thinsp;VP) \u0026minus; Cost per student (FC)] / [Cohen's d (FC\u0026thinsp;+\u0026thinsp;VP) \u0026minus; Cohen's d (FC)], computed overall and by training level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 | Statistical analysis\u003c/h2\u003e \u003cp\u003eIndependent samples t-tests compared baseline knowledge and post-intervention outcomes; paired samples t-tests assessed within-group changes. Chi-square tests compared achievement rates. Pearson correlations examined relationships between process measures and outcomes. Linear regression tested whether process variables predicted progress controlling for training level. α\u0026thinsp;=\u0026thinsp;.05 (two-tailed). Complete case analysis: 292/315 (92.7%) completed assessments; 23 (7.3%) with incomplete questionnaires excluded (FC: n\u0026thinsp;=\u0026thinsp;14/145; FC\u0026thinsp;+\u0026thinsp;VP: n\u0026thinsp;=\u0026thinsp;9/170). Completion rates were similar across training levels (UGY: 92.7%; PGY: 92.8%; χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.001, p = .975).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 | Use of artificial intelligence and statistical software\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (Participant Flow) was generated using Google Gemini (Gemini 1.5 Pro, Google LLC, Mountain View, CA) based on actual research data collected from this study. The AI tool was used solely for visual presentation and formatting of pre-existing data to enhance clarity and professional appearance; all underlying numerical data were verified against source records to ensure accuracy. Supplementary Figure S2 (Incremental Cost-Effectiveness Ratios by Training Level) was produced using R version 4.4 (R Foundation for Statistical Computing, Vienna, Austria) with the ggplot2 package [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All statistical analyses were performed using conventional statistical methods without AI assistance. No AI-generated content was used in the conception, design, data collection, analysis, interpretation, or writing of this manuscript. This use of AI tools complies with Springer Nature author guidelines.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 | RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 | Participant characteristics and baseline comparability\u003c/h2\u003e \u003cp\u003eOf 315 students (145 FC, 170 FC\u0026thinsp;+\u0026thinsp;VP), 292 (92.7%) completed assessments (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Final analysis: FC \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;131 (85 UGY, 46 PGY), FC\u0026thinsp;+\u0026thinsp;VP \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;161 (79 UGY, 82 PGY). Baseline knowledge was comparable between FC and FC\u0026thinsp;+\u0026thinsp;VP groups overall (55.42% vs 56.02%, \u003cem\u003et\u003c/em\u003e(289)\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e = .761) and within training levels (UGY: 55.06% vs 57.47%, \u003cem\u003et\u003c/em\u003e(161)\u0026thinsp;=\u0026thinsp;0.92, \u003cem\u003ep\u003c/em\u003e = .361; PGY: 56.09% vs 54.63%, \u003cem\u003et\u003c/em\u003e(125)\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e = .644), supporting baseline comparability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics and Knowledge Assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFC\u0026thinsp;+\u0026thinsp;VP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-test score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.42% (17.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.02% (15.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUndergraduate Students (UGY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-test score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.06% (18.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.47% (14.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePostgraduate Residents (PGY)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-test score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.09% (17.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.63% (16.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e FC\u0026thinsp;=\u0026thinsp;Flipped Classroom; FC\u0026thinsp;+\u0026thinsp;VP\u0026thinsp;=\u0026thinsp;Flipped Classroom with Virtual Patients; UGY\u0026thinsp;=\u0026thinsp;Undergraduate medical students (fifth-year clinical clerkship); PGY\u0026thinsp;=\u0026thinsp;Postgraduate year 1\u0026ndash;2 residents; SD\u0026thinsp;=\u0026thinsp;Standard Deviation. \u003cem\u003ep\u003c/em\u003e-values from independent samples \u003cem\u003et\u003c/em\u003e-tests. All \u003cem\u003ep\u003c/em\u003e \u0026gt; .05, indicating no significant baseline differences between periods.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 | Learning outcomes and educational effectiveness\u003c/h2\u003e \u003cp\u003eBoth instructional approaches achieved extremely large learning effects with near-universal competency achievement, consistent with recent evidence on virtual reality and educational technology effectiveness in medical education [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In the FC group, undergraduate students demonstrated mean learning progress of 41.18 percentage points (Cohen's \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.23, 98.8% achieving\u0026thinsp;\u0026ge;\u0026thinsp;80% post-test mastery), while postgraduate residents showed mean progress of 40.43 percentage points (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.35, 97.8% achievement). In the FC\u0026thinsp;+\u0026thinsp;VP group, undergraduates progressed 37.22 percentage points (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.51, 100% achievement) and postgraduates progressed 40.24 percentage points (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.38, 97.6% achievement) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All within-group pre-post comparisons reached strong statistical significance (all \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), reflecting substantial and robust learning gains across all conditions and training levels. Substantial ceiling effects were evident, with 73\u0026ndash;85% of participants achieving perfect post-test scores, suggesting assessments captured learning to competency but potentially lacked discrimination at highest performance levels.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLearning Outcomes by Training Level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBetween-group comparisons revealed no statistically significant differences in post-test performance or achievement rates. Overall post-test scores were statistically equivalent (FC: 96.34% vs FC\u0026thinsp;+\u0026thinsp;VP: 94.78%, \u003cem\u003et\u003c/em\u003e(289)\u0026thinsp;=\u0026thinsp;1.42, \u003cem\u003ep\u003c/em\u003e = .157). Achievement rates exceeded 98% in both groups (FC: 98.5% vs FC\u0026thinsp;+\u0026thinsp;VP: 98.8%, χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e = .748). Within specific training levels, no significant differences emerged for any outcome measure (UGY post-test: \u003cem\u003et\u003c/em\u003e(161)\u0026thinsp;=\u0026thinsp;1.08, \u003cem\u003ep\u003c/em\u003e = .283; PGY post-test: \u003cem\u003et\u003c/em\u003e(125)\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003ep\u003c/em\u003e = .348; UGY achievement: χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;1.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000; PGY achievement: χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000). While FC\u0026thinsp;+\u0026thinsp;VP groups demonstrated numerically larger effect sizes compared to FC groups (overall: Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.16; UGY: Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29; PGY: Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.02), these incremental differences did not reach conventional statistical significance thresholds, indicating educational equivalence between instructional approaches on primary knowledge acquisition outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExploratory baseline knowledge stratification analysis suggested patterns of differential response warranting future investigation. Among learners entering with low baseline knowledge (\u0026le;\u0026thinsp;40% pre-test scores), both approaches achieved remarkably large effect sizes (FC: \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.07, FC\u0026thinsp;+\u0026thinsp;VP: \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.93, Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.14), with FC achieving 95.9% mastery and FC\u0026thinsp;+\u0026thinsp;VP achieving 100% mastery, though this difference did not reach statistical significance (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;1.52, \u003cem\u003ep\u003c/em\u003e = .466). Among learners with medium baseline knowledge (40\u0026ndash;60% pre-test), both approaches achieved 100% mastery in FC and 97.6% in FC\u0026thinsp;+\u0026thinsp;VP (χ\u0026sup2;(1)\u0026thinsp;=\u0026thinsp;2.08, \u003cem\u003ep\u003c/em\u003e = .692). Among learners with high baseline knowledge (\u0026gt;\u0026thinsp;60% pre-test), both approaches achieved 100% mastery rates. The inability to calculate meaningful effect sizes for medium and high baseline strata (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00 due to ceiling effects) suggests potential ceiling constraints on observable differential effectiveness at higher baseline knowledge levels (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 | Process measures: cognitive load and critical thinking\u003c/h2\u003e \u003cp\u003eWithin the FC\u0026thinsp;+\u0026thinsp;VP group, germane cognitive load\u0026mdash;representing learning-directed cognitive processing\u0026mdash;showed moderate positive correlation with learning progress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.521, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), while critical thinking disposition showed moderate positive correlation in bivariate analysis (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342, \u003cem\u003ep\u003c/em\u003e = .010). Multiple linear regression controlling for training level and all process measures simultaneously confirmed that germane cognitive load was the only significant predictor of learning progress (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.96, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.62, \u003cem\u003ep\u003c/em\u003e = .015), while critical thinking did not predict unique variance (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.15, \u003cem\u003ep\u003c/em\u003e = .421), indicating that approximately 27.1% of variance in learning outcomes was attributable to cognitive load factors collectively (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = .271; Supplementary Table S2). The modest explained variance aligns with cognitive load theory predictions while reflecting the multifactorial reality of learning outcomes in authentic educational contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 | Cost-effectiveness analysis\u003c/h2\u003e \u003cp\u003eAnnual operational costs differed substantially between instructional approaches. The FC approach required \u003cspan\u003e$\u003c/span\u003e3,460 annually serving 66 students (\u003cspan\u003e$\u003c/span\u003e53 per student), while the FC\u0026thinsp;+\u0026thinsp;VP approach required \u003cspan\u003e$\u003c/span\u003e13,108 annually serving 80 students (\u003cspan\u003e$\u003c/span\u003e163 per student), representing a 3.08-fold higher per-student investment associated with virtual patient integration.\u003c/p\u003e \u003cp\u003eCost-effectiveness analysis revealed dramatic variation by training level (Supplementary Figure S2). For undergraduate medical students, virtual patient integration demonstrated favorable cost-effectiveness with an incremental cost-effectiveness ratio (ICER) of \u003cspan\u003e$\u003c/span\u003e383 per Cohen's \u003cem\u003ed\u003c/em\u003e unit, reflecting meaningful effect size gains (Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29) coupled with achievement of universal mastery (100% vs 98.8%). In striking contrast, for postgraduate residents the ICER was \u003cspan\u003e$\u003c/span\u003e4,670 per Cohen's \u003cem\u003ed\u003c/em\u003e unit, reflecting minimal incremental benefit (Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.02) with no observable change in achievement rates (both groups 97.6\u0026ndash;97.8%). This represents a 12.2-fold difference in cost-effectiveness between training levels, with virtual patient integration providing substantially superior value for novice learners compared to advanced learners who achieved comparable educational outcomes more efficiently through the less resource-intensive flipped classroom approach (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCost-Effectiveness Analysis by Training Level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eThis pragmatic evaluation reveals that both flipped classroom alone (FC) and flipped classroom with virtual patients (FC\u0026thinsp;+\u0026thinsp;VP) achieved extremely large learning effects (Cohen's \u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.2) with near-universal competency achievement (\u0026gt;\u0026thinsp;98%), demonstrating educational equivalence on primary knowledge acquisition outcomes. However, comprehensive cost-effectiveness analysis revealed dramatic differential value by learner expertise: virtual patient integration provided meaningful incremental benefit for undergraduate students (Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29, universal achievement, ICER = \u003cspan\u003e$\u003c/span\u003e383 per Cohen's \u003cem\u003ed\u003c/em\u003e unit) but negligible gains for postgraduate residents (Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.02, ICER = \u003cspan\u003e$\u003c/span\u003e4,670 per Cohen's \u003cem\u003ed\u003c/em\u003e unit)\u0026mdash;a striking 12.2-fold difference in cost-effectiveness (Supplementary Figure S2). Germane cognitive load showed moderate correlation with learning progress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.521, \u003cem\u003eR\u003c/em\u003e\u0026sup2; = 27.1% for cognitive load factors collectively), while critical thinking showed moderate bivariate correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342) but no independent predictive value in multivariate analysis. These findings collectively support precision education frameworks wherein technology investments are strategically targeted based on demonstrated cost-effectiveness profiles and learner expertise characteristics.\u003c/p\u003e \u003cp\u003eThe extremely large effect sizes in both groups align with meta-analytic evidence demonstrating flipped classroom superiority over traditional instruction across medical disciplines [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Near-universal achievement rates (\u0026gt;\u0026thinsp;98%) indicate that well-designed active learning environments can successfully bring learners to competency regardless of technological enhancement, with important implications for competency-based medical education [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed pattern\u0026mdash;meaningful gains for undergraduates but minimal for postgraduates\u0026mdash;is consistent with cognitive load theory's expertise reversal effect [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A recent meta-analysis of 60 studies confirms that low prior knowledge learners benefit from high-assistance instruction (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.505) while high prior knowledge learners benefit from low-assistance instruction (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.428) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For undergraduate students lacking dermatological clinical exposure, virtual patients provided structured scaffolding reducing extraneous load while promoting germane load through realistic case engagement. For postgraduate residents with developed clinical schemas from patient care rotations, virtual patient scaffolding likely introduced redundancy relative to internal knowledge structures, potentially increasing cognitive load\u0026mdash;the hallmark of expertise reversal. The 12.2-fold ICER difference quantifies that targeted deployment is not merely pedagogically sound but economically essential for sustainable implementation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGermane cognitive load's moderate correlation with learning progress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.521, explaining approximately 27% of variance when considering all cognitive load factors collectively) aligns with cognitive load theory predictions,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] while reflecting real-world educational complexity where multiple factors influence outcomes. Critical thinking's moderate bivariate correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.342) but lack of independent predictive value (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003ep\u003c/em\u003e = .421) in multivariate analysis suggests that general cognitive dispositions may not directly drive knowledge acquisition in focused interventions beyond their association with learning-directed cognitive processing, consistent with domain-specificity principles.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eSeveral important limitations warrant consideration. First, sequential implementation raises concerns about temporal confounding and history bias. However, multiple factors mitigate this concern: baseline knowledge equivalence (\u003cem\u003ep\u003c/em\u003e = .761), consistent instructor and curriculum across periods, identical assessments, and stable institutional context. The extremely large effect sizes in both periods (\u003cem\u003ed\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2.2) suggest robust effectiveness independent of temporal factors. Moreover, the observed pattern\u0026mdash;meaningful gains for undergraduates but minimal for postgraduates\u0026mdash;aligns precisely with expertise reversal theory predictions [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] rather than reflecting secular trends, as temporal effects would be expected to influence both groups similarly. While randomized concurrent designs would provide stronger causal inference, the convergent evidence from baseline equivalence, theoretical alignment, and differential response patterns by expertise level supports validity of cost-effectiveness conclusions.\u003c/p\u003e \u003cp\u003eSecond, substantial ceiling effects present critical measurement limitations. Postgraduate residents achieved near-universal competency (FC: 97.8% vs FC\u0026thinsp;+\u0026thinsp;VP: 97.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.000) with minimal incremental effect from virtual patients (Δ\u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.02), while 77\u0026ndash;85% attained perfect post-test scores. This strongly suggests the assessment tool lacked sufficient difficulty to discriminate learning gains at high performance levels. The apparent lack of incremental benefit for postgraduates should be interpreted cautiously\u0026mdash;it may partially reflect assessment insensitivity rather than true absence of learning gains. Future research should employ assessments with greater difficulty and discrimination at high performance levels\u0026mdash;incorporating complex clinical scenarios, atypical presentations, or management of complications\u0026mdash;to detect potential subtle benefits current measures cannot capture.\u003c/p\u003e \u003cp\u003eHowever, this measurement limitation paradoxically strengthens rather than weakens cost-effectiveness conclusions from a pragmatic perspective. Even assuming unmeasured incremental benefits exist for postgraduates, three converging factors support conclusions: (1) both approaches successfully achieved bringing learners to competency (\u0026gt;\u0026thinsp;97% achievement rates); (2) the 3.08-fold higher per-student investment (\u003cspan\u003e$\u003c/span\u003e163 vs \u003cspan\u003e$\u003c/span\u003e53) represents substantial additional resource commitment; (3) any unmeasured benefits would necessarily be marginal improvements beyond already-near-perfect performance. The critical question becomes whether potential marginal improvements beyond near-universal competency achievement justify substantial additional investment, particularly when simpler approaches achieve comparable competency rates more efficiently.\u003c/p\u003e \u003cp\u003eThird, this study focused on scabies management\u0026mdash;a relatively circumscribed dermatological condition. Generalizability to more complex scenarios requires empirical verification. However, scabies was deliberately selected for several compelling reasons: it represents essential foundational knowledge encountered across all medical specialties; it requires integrating clinical recognition with diagnostic procedures; it demands authentic clinical reasoning including pattern recognition across varying presentations (classic to crusted), evidence-based treatment decision-making considering patient-specific factors, and patient education regarding transmission and compliance. Focusing on a single well-defined condition with identical learning objectives provided experimental control for rigorous cost-effectiveness comparison isolating virtual patient effects. The observed expertise reversal pattern reflects fundamental cognitive load principles likely to generalize across content domains. Nevertheless, future research should examine whether cost-effectiveness differentials persist across more complex dermatological conditions and other medical domains.\u003c/p\u003e \u003cp\u003eAdditional limitations include that process measures were assessed only in the FC\u0026thinsp;+\u0026thinsp;VP group, preventing direct mechanistic comparison between approaches; findings derive from one institution requiring cross-cultural and cross-institutional validation; and cost-effectiveness analysis captured direct educational costs without accounting for indirect costs (opportunity costs, long-term retention effects) or broader value considerations (learner satisfaction, clinical performance outcomes).\u003c/p\u003e \u003cp\u003eThese findings offer practical guidance for educational leaders. First, excellent outcomes are achievable through multiple pedagogical approaches; pedagogical principles matter more than technological sophistication for foundational knowledge acquisition. Second, technology investments should be targeted rather than universal. The 12.2-fold ICER difference indicates indiscriminate deployment is economically inefficient. Institutions should prioritize enhanced technologies for learners with demonstrated need while employing simpler approaches for advanced learners achieving comparable outcomes efficiently. Third, comprehensive evaluation frameworks integrating educational effectiveness with cost-effectiveness analysis are essential for informed decision-making,[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] as pedagogical equivalence may mask substantial economic disparities with important implications for sustainable resource allocation.\u003c/p\u003e \u003cp\u003eIn conclusion, this pragmatic evaluation demonstrates that while flipped classroom approaches achieve near-universal competency, the incremental value of virtual patient integration varies dramatically by learner expertise. Findings support precision education frameworks wherein technology investments are strategically targeted based on cost-effectiveness profiles rather than deployed universally. As medical education adopts competency-based models requiring individualized learning paths,[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] understanding the economic sustainability of pedagogical approaches becomes as essential as evaluating educational effectiveness, particularly as artificial intelligence and digital technologies continue to transform medical education delivery and create new opportunities for precision education implementation [\u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgments\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all students who participated in this study and the educational staff at the Department of Dermatology, Tri-Service General Hospital, for their support during curriculum implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from Tri-Service General Hospital Research Foundation (TSGH-E-114224, TSGH-E-115231). The funding source had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eConflicts of Interest\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eEthical Approval\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board of Tri-Service General Hospital (TSGHIRB No. C202105012). All participants provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. Data are not publicly available due to privacy and ethical restrictions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.-T.H.: Conceptualization, methodology, investigation, data collection, formal analysis, writing \u0026ndash; original draft, writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;S.-W.L.: Methodology, data collection, writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;S.-E.W.: Data collection, writing \u0026ndash; review \u0026amp; editing.\u003cbr\u003e\u0026nbsp;F.-C.L.: Conceptualization, supervision, writing \u0026ndash; review \u0026amp; editing, corresponding author.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWartman SA, Combs CD. 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JAMA Netw Open. 2025;8(1):e2453131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2024.53131\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2024.53131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2 and 3","content":"\u003cp\u003eTable 2 and 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Virtual patients, Flipped classroom, Cost-effectiveness, Expertise reversal, Precision education, Medical education","lastPublishedDoi":"10.21203/rs.3.rs-9467306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9467306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEducational technology adoption accelerates despite limited evidence of incremental value within already-effective curricula. Pragmatic evaluations examining cost-effectiveness and differential benefits across learner populations with varying expertise remain scarce.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo evaluate virtual patient integration into established flipped classroom curriculum, examining incremental educational outcomes, comprehensive cost-effectiveness, and systematic variation by training level.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSequential implementation study at a tertiary teaching hospital in Taiwan (2022\u0026ndash;2025). Flipped classroom (FC; 2022\u0026ndash;2023, N\u0026thinsp;=\u0026thinsp;131) was followed by FC with virtual patients (FC\u0026thinsp;+\u0026thinsp;VP; 2024\u0026ndash;2025, N\u0026thinsp;=\u0026thinsp;161). Primary outcomes: knowledge acquisition (Cohen's d, achievement rates\u0026thinsp;\u0026ge;\u0026thinsp;80%). Incremental cost-effectiveness ratios (ICERs) were calculated by training level.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth approaches achieved extremely large learning effects (FC: d\u0026thinsp;=\u0026thinsp;2.27; FC\u0026thinsp;+\u0026thinsp;VP: d\u0026thinsp;=\u0026thinsp;2.43; \u0026gt;98% achievement; between-group differences non-significant). FC\u0026thinsp;+\u0026thinsp;VP required 3.08-fold higher per-student investment (\u003cspan\u003e$\u003c/span\u003e163 vs \u003cspan\u003e$\u003c/span\u003e53). Cost-effectiveness varied dramatically: undergraduates showed meaningful gains (Δd\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.29, ICER = \u003cspan\u003e$\u003c/span\u003e383/d), while postgraduates showed minimal benefit (Δd\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.02, ICER = \u003cspan\u003e$\u003c/span\u003e4,670/d)\u0026mdash;a 12.2-fold difference. Germane cognitive load predicted learning progress (r\u0026thinsp;=\u0026thinsp;0.521, R\u0026sup2; = 27.1%); critical thinking did not (p = .421).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eNear-universal competency is attainable through multiple instructional approaches. Virtual patient integration provides differential incremental value by learner expertise\u0026mdash;meaningful for novices but negligible for advanced learners achieving comparable outcomes more efficiently. Findings support precision education frameworks targeting technology investments based on cost-effectiveness profiles rather than universal deployment.\u003c/p\u003e","manuscriptTitle":"Cost-Effectiveness of Virtual Patient Integration in Dermatology Education: Evidence for Precision Education by Learner Expertise","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 21:23:56","doi":"10.21203/rs.3.rs-9467306/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-15T14:45:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110692781155701334990164810743114395050","date":"2026-05-04T15:05:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T08:34:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-25T20:32:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T00:50:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-23T00:49:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-04-20T05:42:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9e94b0f-3536-472a-ae85-9762533ffc15","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-15T14:45:56+00:00","index":50,"fulltext":""},{"type":"reviewerAgreed","content":"110692781155701334990164810743114395050","date":"2026-05-04T15:05:48+00:00","index":35,"fulltext":""},{"type":"reviewersInvited","content":"15","date":"2026-05-04T08:34:18+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T21:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 21:23:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9467306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9467306","identity":"rs-9467306","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}