Digital Education Strategies in Anaesthesiology: A Systematic Review and SWOT Analysis

doi:10.21203/rs.3.rs-7476309/v1

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Digital Education Strategies in Anaesthesiology: A Systematic Review and SWOT Analysis

https://doi.org/10.21203/rs.3.rs-7476309/v1

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Background

Digital transformation in health sciences education is accelerating, yet little is known about how institutional strategies, pedagogical models, and technology adoption intersect in practice. Anaesthesiology, a high-stakes and procedurally intensive discipline, presents a unique lens through which to examine digital education innovation.

Purpose

This systematic review synthesizes current research on digital education strategies in anaesthesiology and evaluates their pedagogical impact, implementation dynamics, and institutional alignment using a Hybrid Thematic–SWOT framework.

Methods

We conducted a PRISMA-guided review of 40 peer-reviewed studies published between 2014 and 2024, drawn from Scopus, Web of Science, and PubMed. Studies were appraised using CASP and NOS tools. Thematic coding followed Braun & Clarke’s (2006) framework, with findings analyzed across four domains: pedagogical design, learner engagement, institutional capacity, and equity.

Results

Simulation-based learning, blended formats, AI-enhanced feedback, and adaptive systems are widely used to support technical skill acquisition. Success factors include faculty readiness, curriculum alignment, and strategic funding. Key weaknesses relate to infrastructure gaps and uneven digital competencies. Opportunities include emerging accreditation models and AI-assisted personalization, while threats involve ethical risks and regulatory ambiguity.

Conclusion

This review highlights the strategic role of institutional leadership in translating digital innovation into sustainable, equitable learning ecosystems. By linking empirical outcomes to organizational challenges, the study contributes a transferable framework for digital curriculum development in health professions education globally.

Anesthesiology & Pain Medicine

Digital education

Anaesthesiology training

Simulation

Health professions education

AI in education

SWOT analysis

Systematic review

Curriculum innovation

Digital education in anaesthesiology now includes simulation, AR/VR, AI, and mobile platforms to support clinical training. These innovations address procedural skills, crisis management, and remote learning, especially in LMICs (Alenezi et al., 2023; Katyeudo & de Souza, 2022; Shen et al., 2015), while AI feedback and gamified modules boost motivation (De Sá et al., 2024). Yet most research targets high-resource settings, with limited focus on LMIC barriers like infrastructure and training (Brewis et al., 2023; Piccoli et al., 2024). The impact and adaptability of these tools in anaesthesiology remain underexplored.

Furthermore, few reviews have applied structured analytical frameworks to map institutional enablers and barriers, limiting their utility for policy formulation or curriculum design. This systematic review addresses these gaps by applying a PRISMA-guided methodology and a Hybrid Thematic–SWOT analysis to examine:

How digital education strategies influence clinical skill development, learner engagement, and inclusivity in anaesthesiology training; and

What institutional, infrastructural, and policy-level factors shape the adoption and scalability of such strategies.

While prior reviews often catalog digital tools or evaluate effectiveness in isolation, few, if any, have integrated pedagogical outcomes with systemic enablers through a structured framework. This review contributes a conceptual synthesis model that links pedagogical outcomes with institutional enablers through a structured SWOT-thematic lens, offering a transferable framework for digital strategy development in health sciences education.

We conducted a PROSPERO-registered systematic review using PRISMA 2020 guidelines (Page et al., 2021). Studies from 2014–2024 evaluating digital education in anaesthesiology, including simulation, AR/VR, AI, and blended learning, were included. The review aimed to synthesize empirical evidence on digital education strategies in anaesthesiology by applying a Hybrid Thematic–SWOT analysis to enhance both depth and applicability.

Research framework

The study used the PICo framework:

Population (P): Clinical trainees and educators in anaesthesiology education
Interest (I): Digital education strategies (AI, gamification, AR/VR, blended learning)
Context (Co): Undergraduate, postgraduate, or continuing professional development in anaesthesiology across different healthcare settings.

Search strategy

A comprehensive search of PubMed, Scopus, Web of Science, and Google Scholar identified studies published from January 2014 to January 2024. The search used Boolean logic combining terms such as: (“anesthesiology” OR “anaesthesiology” OR “anaesthesia education”) AND (“AI” OR “artificial intelligence” OR “augmented reality” OR “simulation” OR “e-learning” OR “gamification”).

Gray literature was not included to ensure methodological consistency and replicability. All search results were managed in Zotero and screened using Rayyan for deduplication and blinded selection.

Inclusion and exclusion criteria

Studies were included if they met the following criteria:

Published in English between 2014 and 2024
Reported empirical findings from clinical education involving anaesthesiology trainees
Assessed digital interventions with measurable outcomes (e.g., procedural accuracy, learner engagement)
Scored ≥ 0.7 on the Newcastle-Ottawa Scale (NOS) or passed CASP quality criteria

Exclusion criteria included:

Non-empirical works (e.g., editorials, opinion pieces)
Studies in non-clinical or basic science domains
Papers without accessible full texts

Screening and quality appraisal

Two reviewers independently screened titles, abstracts, and full texts. Discrepancies were resolved by consensus. Methodological quality was evaluated using the NOS (for non-randomized studies) and CASP tools (for qualitative and RCT designs). Inter-coder reliability was assessed with Cohen’s Kappa (κ > 0.80).

Data extraction and synthesis

Data were extracted on study design, country, sample characteristics, digital intervention type, and measured outcomes. A thematic analysis (Braun & Clarke, 2006) was conducted using inductive coding. Resulting themes were then mapped into a SWOT matrix to identify:

Strengths and Weaknesses of each intervention
Opportunities for institutional and curricular enhancement
Threats to implementation and scalability

The Hybrid Thematic–SWOT method was selected to bridge pedagogical analysis with strategic decision-making, especially relevant for curriculum planning in anaesthesiology. This dual-layered approach goes beyond thematic aggregation by connecting educational outcomes to strategic institutional capacities, providing a more actionable foundation for scalable implementation than traditional meta-synthesis or narrative reviews. Unlike narrative or meta-aggregative reviews, the Hybrid Thematic–SWOT framework enables the simultaneous extraction of pedagogical efficacy and systemic scalability, which is crucial in translating educational interventions into policy.

The integration of thematic analysis with SWOT mapping (i.e., Hybrid Thematic–SWOT) was chosen to provide both depth and strategic breadth, allowing the study to synthesise pedagogical insights alongside institutional and systemic enablers. This dual-layered approach offers a more actionable framework than traditional narrative or thematic reviews, particularly for curriculum designers and policy stakeholders.

To enhance rigour and minimise interpretative bias, two reviewers independently extracted data and categorised themes through iterative coding. Disagreements were resolved through consensus discussions, following JBI’s interpretive synthesis principles. A third reviewer audited the final theme structure to ensure transparency and consistency.

This systematic review identified 4,700 records from six major databases (PubMed, Scopus, Web of Science, Cochrane, CINAHL, and Embase) published between 2014 and 2024. After deduplication and screening, 136 studies were assessed in full text, and 40 high-quality studies meeting the inclusion criteria (NOS ≥ 0.7; CASP = pass, available in appendix A-B) were retained for final analysis (Fig. 1). The final corpus comprised empirical studies focused on digital education strategies in anaesthesiology training, including technologies such as virtual reality (VR), augmented reality (AR), artificial intelligence (AI), gamification, mobile apps, and simulation-based learning. Interventions included AI-based adaptive learning, AR/VR simulation, gamification, mobile learning, and blended or flipped classroom models in anaesthesiology (Available in Appendix C).

Effectiveness of digital education strategies in anaesthesiology training

From the 40 studies meeting inclusion criteria, digital education interventions were grouped thematically into six categories: simulation-based learning, augmented/virtual reality (XR/AR), artificial intelligence (AI), gamification, mobile applications, and blended learning models. Findings were synthesized through a Hybrid Thematic–SWOT analysis (Table 1).

Simulation-based training (n = 14) demonstrated consistent improvements in procedural accuracy (53–67%), particularly for ultrasound-guided vascular access, airway management, and echocardiography (Bernard et al., 2019; Romito et al., 2016). However, studies noted that excessive reliance on simulation without clinical mentoring could inflate learner confidence without real-world transferability (Turner et al., 2015).

AR/XR platforms (n = 11), such as those used by Rochlen et al. (2017) and Heo et al. (2022), provided enhanced anatomical visualization and task sequencing, especially in mechanical ventilation and regional anesthesia modules. Strengths included improved learner immersion and independence; weaknesses involved device cost, technical maintenance, and limited faculty familiarity.

AI-enhanced feedback systems (n = 7) personalized learning and improved diagnostic efficiency (Daniel & Wolbrink, 2023; Zanin et al., 2023). However, concerns included cognitive overload and high infrastructure demands. The opportunity lies in AI integration for adaptive repetition and diagnostic tutoring, particularly in repetitive skill domains.

Gamification and virtual patients (n = 8) increased engagement and motivation through competitive and scenario-based learning (Bass et al., 2024). However, their impact on empathy, communication, and complex decision-making was inconsistent, highlighting the threat of superficial learning when not paired with reflective debriefs.

Blended learning models (n = 10), including flipped classrooms, demonstrated enhanced learner autonomy and academic scores (Marchalot et al., 2018), although these required significant faculty effort and readiness for success. Mobile platforms (n = 9) improved accessibility for geographically dispersed learners but offered limited procedural fidelity.

Inclusivity outcomes were reported in 12 studies, with mobile-first, multilingual, and low-bandwidth content expanding reach, particularly in LMICs (Latif et al., 2024; Paziana et al., 2018). However, inconsistencies in content quality and digital infrastructure threatened sustainability.

Table 1
SWOT Digital Strategies in Anaesthesiology Training
Domain	Technology	Reference	SWOT (S/W/O/T) Summary
Engagement & Interactivity	Augmented Reality	Rochlen et al. (2017); Jeon et al. (2014)	S: Enhanced anatomical visualization W: Risk of overconfidence O: Scalable immersive training T: High device cost & low faculty readiness
Engagement & Interactivity	Virtual Environments	Alam & Matava (2022); Harazim et al. (2015)	S: Immersive learning boosts motivation W: Motion sickness, lack of standardization O: Certification via IVE T: Bandwidth limits in LMICs
Engagement & Interactivity	Gamification & Virtual Patients	Bass et al. (2024); Leung et al. (2015)	S: Increased engagement and exam scores W: Inconsistent impact on empathy O: Emergency prep integration T: Risk of content trivialization
Clinical Simulation	XR / AR Simulation	Heo et al. (2022); Goldsworthy et al. (2023)	S: Improved procedural confidence W: Limited user exposure O: ICU and emergency procedure training T: Infection control & headset cost
Access & Flexibility	Mobile App / Telemedicine	Linganna et al. (2020); Shoemaker et al. (2021)	S: Improved access to TEE knowledge W: Lacks tactile learning O: Skill refreshers for remote areas T: Limited procedural realism
Feedback & Performance	AI-Enhanced Feedback & XR	Daniel (2023); Kinney et al. (2018); Zanin et al. (2023)	S: Boosted diagnostic accuracy & motivation W: Cognitive overload risk O: Adaptive XR repetition T: High tech-fatigue potential
Clinical Decision & Problem-Solving	Simulation Problem Solving	Wadyet al. (2021); Muriel-Fernández et al. (2019)	S: Improved resuscitation & decision-making W: Overestimated realism O: Interprofessional expansion T: Scripted simulation overreliance
Clinical Skill Development	Simulation-Based Learning	Breen et al. (2019); Bernard et al. (2019); Turner et al. (2015); Romito et al. (2016)	S: Improved proficiency & accuracy W: Confidence without competence O: Combine with AI platforms T: High maintenance cost
Pedagogical Models	Blended & Flipped Classrooms	Marchalot et al. (2018); Davids et al. (2015)	S: Improved autonomy and test scores W: Time-intensive implementation O: Personalized adaptive content T: Faculty resistance
Device-Specific Skills	EEG & Device Setup	Berger et al. (2022); Andrade et al. (2023)	S: Increased device safety & knowledge W: Unclear long-term outcomes O: Combine with bedside mentoring T: Tech overreliance
Procedural Imaging	Echo & Ultrasound	Kailin (2021); Röhrig (2014)	S: Confidence in echocardiography W: Platform fragmentation O: Hybrid echo training T: Attrition in self-paced learning
Complex Patient Training	Pain & Geriatrics	Moehl et al. (2020); Jacobs et al. (2018)	S: Improved dementia-related care W: Minor attitude shifts O: Integrate aging-related scenarios T: Oversimplification of context
Pain Communication	Pain & Opioid Management	Nixon et al. (2019); Onyeka et al. (2020)	S: Improved communication & cultural relevance W: Limited prescribing change O: Debriefing-based reinforcement T: Superficial engagement risk
Remote Competency	Procedural Tele-ultrasound	Hempel et al. (2022); Latif et al. (2024)	S: Retained skills, remote feedback W: Unrealistic in chaotic settings O: Live tele-mentoring T: Delayed tactile experience
Educational Equity	E-learning Modules	Weiner et al. (2014); Liossi et al. (2018); Wolbrink et al. (2014)	S: Increased access & retention W: Weak on affective outcomes O: Global platforms T: Variable content quality
Educational Equity	Post-COVID Innovations	Haldar et al. (2020); Lewandrowski et al. (2023)	S: Expanded hybrid adoption W: Disrupted clinical practice O: Long-term hybrid design T: Lag in accreditation updates
Blended Access	E-learning + Blended	Vodovar (2020); Friesgaard (2017); Hancock (2017)	S: Improved knowledge & satisfaction W: Weak translation to practice O: ICU module scaling T: Off-hour fatigue
Global Mobility	Mobile & Virtual Learning	Paziana et al. (2018); Kinney et al. (2018); Latif et al. (2024)	S: Mobile-first, multilingual access W: Wi-Fi dependence O: Offline-enabled versions T: Tech inequity

This SWOT analysis indicates that immersive technologies like AR/VR improve anatomical understanding and procedural confidence, especially in airway management. Without adequate mentoring, these tools risk fostering overconfidence. Blended learning is the most scalable, combining technology with clinical supervision.

To offer a comparative summary of the pedagogical findings, Table 2 synthesizes the effectiveness and limitations of each major digital strategy identified in the review. This table complements the in-depth SWOT analysis by providing a clear, side-by-side overview for educators, decision-makers, and policy planners.

Table 2
Summary of Digital Education Strategies in Anaesthesiology: Effectiveness and Limitations
Technology	Effectiveness	Limitations
Simulation-Based Learning	Improves procedural accuracy (53–67%), especially for airway and ultrasound skills	Risk of overconfidence without real clinical transfer; high setup and maintenance cost
Augmented / Virtual Reality	Enhances anatomical understanding and immersive learning	High device costs; limited faculty readiness and technical support
AI-Enhanced Feedback Systems	Boosts diagnostic accuracy and personalizes learning	Cognitive overload; high infrastructure demands
Gamification & Virtual Patients	Increases learner engagement and exam performance	Inconsistent effects on empathy and communication; risk of superficial learning
Mobile Platforms	Improves access in LMICs, supports multilingual and offline learning	Limited procedural fidelity; device and bandwidth constraints
Blended / Flipped Classrooms	Enhances learner autonomy and academic achievement	Time-intensive for faculty; resistance to new pedagogical formats

Institutional and contextual factors affecting adoption and scalability

Analysis of structural and contextual enablers revealed six overarching domains: faculty readiness, accreditation and policy alignment, infrastructure, curriculum design, learner diversity, and equity (Table 3).

Faculty resistance and limited digital literacy were cited in 18 studies as major threats, particularly where institutional support and CPD were lacking (Moehl et al., 2020; Röhrig et al., 2014). Conversely, when faculty received training and protected innovation time, adoption was significantly higher.

Accreditation frameworks were inconsistently aligned with digital competencies. While some settings (e.g., MOCA 2.0 in the U.S.) began recognizing digital hours, other regions lagged in policy reform (Alam & Matava, 2022).

Infrastructure and platform integration, including simulation labs and interoperable learning systems, were key facilitators in 14 studies (Harazim et al., 2015; Wolbrink et al., 2014). Nevertheless, high setup costs and ongoing IT maintenance remained significant barriers.

Curriculum alignment was essential for institutional uptake. When digital modules were embedded within certification pathways or addressed real clinical scenarios (e.g., pain management, opioid protocols), they had higher faculty and learner buy-in (Kailin et al., 2021; Nixon et al., 2019).

Equity and accessibility emerged as cross-cutting themes. While mobile learning addressed inclusion in LMICs, technical fatigue, lack of multilingual content, and device inequality remained persistent challenges.

Table 3
SWOT Adoption and Scalability Factors in Anaesthesiology Education
Category	Focus Area	Reference	SWOT (S/W/O/T) Summary
Accreditation & Policy	Curricular Standards	Alam & Matava I (2022); Lewandrowski et al. (2023)	S: VR/AR accepted in assessments W: Misaligned with traditional competency models O: Push for digital credentialing T: Regulatory delay
Accreditation & Policy	Digital Hours Recognition	Latif et al. (2024); Kinney et al. (2018)	S: Cross-border learning accredited W: No global standards O: CPD/CME integration T: Uneven implementation
Equity & Access	Low-Resource Settings	Paziana et al. (2018); Onyeka et al. (2020)	S: Multilingual offline access W: Tech readiness disparities O: Lightweight app development T: Device inequality
Faculty Engagement	Curriculum Fit	Nixon et al. (2019); Onyeka et al. (2020))	S: Alignment with clinical needs W: Behavioral change limited without mentoring O: Pairing with mentorship T: Resistance to format shifts
Curriculum Design	Module Development	Kailin et al. (2021); Röhrig et al. (2014); Moehl et al. (2020)	S: High learner acceptance W: Faculty time burden O: Shared design initiatives T: Tech fluency gaps
Faculty Readiness	Digital Literacy & Mindset	Haldar et al. (2020); Marchalot et al. (2018); Davids et al. (2015)	S: Improved usability outcomes W: Inadequate digital pedagogy training O: CPD for faculty T: Change aversion
Cost Efficiency	Investment & ROI	Jeon (2014); Breen et al. (2019); Turner et al. (2015)	S: High training impact with modest scale W: High startup & maintenance costs O: Regional training centers T: Budget constraints
Infrastructure	Simulation & Platform Integration	Wolbrink et al. (2014); Harazim et al. (2015); Bernard et al. (2019);	S: Centralized access & scalability W: Heavy technical maintenance O: Shared international platforms T: Licensing & obsolescence
Scheduling & Workflow	Platform Use & Flexibility	Vodovar (2020); Shoemaker et al. (2021); Andrade et al. (2023)	S: Adapted to clinical shifts W: Learning spills into off-hours O: Duty-aligned modules T: Burnout risk
Simulation Culture	Program Implementation	Hempel (2022); Muriel-Fernández (2019); Wady (2021)	S: Embedded in curricula increases acceptance W: Requires space, staff, and budget O: Cross-disciplinary expansion T: High resource demand
Learner-Centeredness	Access & Personalization	Liossi (2018); Weiner (2014); Rochlen (2017)	S: Inclusive for diverse learners W: Language and bandwidth barriers O: AI-driven personalization T: Digital access inequity
Evaluation Standards	Learning Expectations	Berger (2022); Hancock (2017)	S: Addresses EEG gaps, organ donation education W: Disconnect between knowledge & practice O: Embed in certification pathways T: No standard assessment model
Tech Infrastructure	AR/VR/Mobile Tools	Heo (2022); Goldsworthy (2023); Linganna (2020)	S: Feasible across use cases W: XR device inequality, steep learning curves O: Use of low-cost tools T: Institutional IT support lacking
Advanced Platforms	AI & XR Feedback Systems	Daniel (2023); Kinney (2018); Zanin (Zanin et al., 2023); Latif (2024)	S: Enhanced feedback & multisensory learning W: Resource-intensive O: AI for flipped classrooms T: Tech fatigue, access cost
Inclusivity & Roles	Adaptive Learning Needs	Friesgaard (2017); Bass (2024); Jacobs (2018)	S: Fits varied learner types W: Gains not sustained O: Personalized learning paths T: One-size-fits-all mismatch

Analysis shows that faculty development, curriculum integration, and accreditation reform are key enablers. Barriers include high infrastructure costs, limited digital literacy, and inconsistent policy recognition, especially in LMICs. To provide a concise institutional perspective, Table 4 summarizes the core enablers and barriers affecting digital education scalability in anaesthesiology.

Table 4
Institutional-Level SWOT Summary for Adoption of Digital Education in Anaesthesiology
Domain	Strengths (S)	Weaknesses (W)	Opportunities (O)	Threats (T)
Faculty Readiness	Improved usability with training	Low digital pedagogy skills	CPD for faculty	Resistance, lack of protected time
Curriculum Alignment	High learner acceptance when clinically relevant	Faculty burden in module design	Co-design with clinical teams	Misalignment with core competencies
Accreditation	Some systems accept digital modules (e.g., MOCA 2.0)	Inconsistent policy recognition	Push for global digital credentialing	Delayed regulatory adaptation
Infrastructure	Centralized access via shared platforms	High cost and tech maintenance	Regional simulation centers	Licensing issues, obsolescence
Equity & Access	Offline and mobile access increases inclusion	Device and bandwidth gaps	Low-cost, multilingual solutions	Digital divide in LMICs
Note: This summary highlights key institutional enablers and constraints for digital education integration.

Prior systematic reviews in medical education have primarily evaluated digital innovations through outcomes such as student satisfaction, technical feasibility, or knowledge gain (e.g., Alenezi et al., 2023; Katyeudo & de Souza, 2022; Shen et al., 2015). However, these studies often overlook the broader institutional and systemic factors that enable or constrain long-term integration. Unlike these reviews, the present study offers a dual-layered synthesis by combining pedagogical findings with structural SWOT analysis. In doing so, it identifies how contextual factors—such as accreditation frameworks, workforce readiness, and equity in access—interact with pedagogical effectiveness. This approach extends recent contributions (e.g., Alam & Matava, 2022; Bass et al., 2024) by offering a framework that is both theory-informed and implementation-focused.

Impact of digital strategies on learning outcomes

Immersive technologies such as simulation-based learning, augmented/virtual reality (XR/AR), and AI-enhanced platforms demonstrated consistent improvements in learner engagement, procedural accuracy, and diagnostic confidence. These findings align with previous literature indicating that realism and interactivity foster deeper cognitive and motor learning (Rochlen et al., 2017; Wolbrink et al., 2014).

However, this review reveals a critical nuance: gains in confidence do not always translate into clinical competence. For instance, several studies reported inflated self-efficacy in learners who had limited clinical exposure or lacked supervision, echoing concerns about the overestimation of performance in tech-mediated settings (Turner et al., 2015). This underscores the importance of scaffolded integration, where digital modalities complement, not replace, direct mentorship.

Unlike prior systematic reviews in medical education that focus solely on effectiveness metrics (e.g., exam scores, student satisfaction), this study integrates structural implementation dimensions that often remain implicit yet critically shape sustainability.

Gamification and virtual patient modules increased learner motivation but showed inconsistent effects on empathy and communication, skills central to anesthetic care. These findings suggest a limitation of current digital designs, which may prioritize cognitive learning at the expense of affective domains.

Institutional readiness and equity considerations

A key contribution of this review is the identification of structural enablers and barriers through a SWOT lens. Faculty readiness and institutional support emerged as decisive factors. Despite promising outcomes from simulation or AI tools, their success was contingent on adequate staff training, curricular alignment, and workload protection (Moehl et al., 2020; Röhrig et al., 2014).

Furthermore, policy frameworks were found to lag behind technological adoption. While initiatives such as MOCA 2.0 in the U.S. have begun to incorporate digital credentials, several regions lack formal recognition of online learning hours. Without regulatory adaptation, even high-quality innovations may fail to achieve accreditation or scalability (Alam & Matava, 2022).

From an equity perspective, mobile-first and offline-compatible designs enhanced accessibility in low-resource settings. Yet, digital divides, particularly in bandwidth, device availability, and faculty upskilling, persist. These disparities threaten to widen global gaps in anaesthesiology training unless explicitly addressed through policy and funding mechanisms. Hence, the value of this review lies not only in its pedagogical insights but also in its strategic relevance for educational leaders and policymakers seeking systemic reform in digital medical education.

Unlike prior reviews that catalog technologies, this synthesis evaluates their pedagogical value and strategic fit in anaesthesiology. It underscores the need for faculty training and accreditation reform to enable sustainable adoption aligned with institutional priorities.

Theoretical and methodological contributions

This review contributes methodologically by combining thematic analysis and SWOT mapping in a novel way. While previous reviews focused on effectiveness or usability, few provided a structured cross-contextual synthesis linking pedagogical outcomes with institutional dynamics. This hybrid approach enables both descriptive depth and strategic foresight, providing actionable knowledge for different stakeholders.

Thematically, our findings suggest that digital education in anaesthesiology is most effective when delivered as a blended model: combining high-fidelity simulations or AI-adaptive systems with human mentorship and curriculum integration. Such models foster both procedural proficiency and contextual judgment, key to safe anesthetic practice.

Effective integration requires more than purchasing technology. Faculty capacity building, curriculum alignment, and equitable access tailored to local resources are essential. In LMICs, combining offline modules with simulation and mentorship can address infrastructure gaps and support sustainable skill acquisition.

Implications for Broader Health Professions Education

To ensure the relevance of this review beyond the context of anaesthesiology, the findings were further analyzed for their transferability across health professions education. Table 5 illustrates how the key digital strategies identified in this review, ranging from simulation to institutional leadership, can inform educational design, faculty development, and policy decisions across diverse clinical and interprofessional settings. This cross-professional synthesis underscores the scalability and systemic importance of digital transformation strategies in health sciences education broadly.

Table 5
Translating Findings Across Health Professions Education
Theme	Key Findings	Cross-Professional Relevance
Simulation and AR/VR Integration	Enhances psychomotor and decision-making skills in anaesthesiology training.	Applicable in nursing, surgery, emergency medicine, physiotherapy, and OT.
AI-Enhanced Feedback Systems	Improves procedural accuracy and adaptive learning through real-time analytics.	Valuable for clinical reasoning training in pharmacy, diagnostics, and med.
Faculty Readiness and Pedagogy	Educator digital literacy is a determinant of successful implementation.	Supports faculty development in all health disciplines with digital curricula.
Infrastructure and Equity	Digital divide impacts learning outcomes across settings.	Promotes investment and access strategies across rural and urban institutions.
Leadership and Institutional Alignment	Leadership support influences long-term sustainability of digital innovation.	Relevant to administrators in interprofessional education (IPE) settings.

Table 5. Cross-professional implications of the review findings. This synthesis illustrates how digital strategies effective in anaesthesiology education can be transferred and adapted to other domains of health professions education, supporting both curricular design and policy development.

Digital education strategies, particularly simulation-based training, XR/AR technologies, and AI-enhanced platforms, offer promising avenues to improve clinical skill acquisition, learner engagement, and educational access in anaesthesiology. However, their effectiveness and scalability depend not only on technological sophistication but also on their strategic integration within institutional, curricular, and policy frameworks. A blended, equity-driven approach that combines these digital innovations with human mentorship and thoughtful pedagogical alignment emerges as the most sustainable model for advancing anaesthesiology education.

This review contributes a novel synthesis by applying a Hybrid Thematic–SWOT framework to systematically examine both pedagogical outcomes and structural enablers across 40 high-quality studies. The findings emphasize that beyond proving efficacy, digital education must navigate institutional resistance, regulatory inertia, and infrastructure inequality, especially in low- and middle-income contexts.

Future efforts should focus not only on advancing technological solutions but also on transforming systems: equipping educators with digital pedagogy competencies, aligning accreditation with digital competencies, and embedding inclusivity principles in instructional design. Digital education strategies, particularly simulation and AR/VR, can enhance clinical skills training in anaesthesiology if thoughtfully integrated into curricula. A blended, equity-driven approach, supported by faculty development and accreditation reform, offers the most sustainable model for improving patient safety and training outcomes.

Ultimately, digital transformation in anaesthesiology education is less about adopting tools and more about reimagining systems, placing faculty empowerment, accreditation evolution, and context-responsive equity at the core of future innovations.

Policy implications

Based on the SWOT-informed analysis of digital anaesthesiology education, several key policy recommendations are proposed:

Align Certification and AccreditationNational boards and certification bodies should formally recognize digital learning hours (e.g., simulations, AR modules, AI-based assessments) within continuing medical education (CME) and competency-based frameworks such as MOCA 2.0.

Invest in Faculty DevelopmentInstitutions should mandate digital pedagogy training and provide protected time for curriculum innovation. Faculty resistance stems less from technology aversion than from lack of time, incentives, and capacity.

Build Interoperable and Equitable InfrastructureMinistries of health and education must support interoperable platforms and simulation networks, especially in LMICs, with an emphasis on offline access, multilingual content, and low-bandwidth compatibility.

Embed Digital Modules into Core CurriculaRather than viewing digital tools as supplementary, they should be strategically embedded into core anaesthesiology training programs. Co-design with clinical faculty ensures contextual relevance.

Implement Multidimensional Evaluation MetricsPolicies must expand assessment models beyond procedural accuracy to include learner motivation, empathy, equity, and long-term behavioral impact.

Promote Cross-Institutional CollaborationsRegional consortia and international platforms should be leveraged to share best practices, co-develop scalable modules, and mitigate development costs through open-source solutions.

By integrating these policy directions, medical education systems can move beyond pilot innovations toward sustainable, inclusive, and context-sensitive transformation in anaesthesiology training.

Limitations and future research

While this review offers a structured synthesis of digital education strategies in anaesthesiology, several limitations warrant consideration. First, despite rigorous selection criteria and quality appraisal, the included studies displayed significant heterogeneity in intervention design, learner populations, and outcome measures. This limited the potential for direct comparison or meta-analysis and constrained generalizability across institutional contexts.

Second, although the hybrid thematic–SWOT approach enabled nuanced mapping of both pedagogical and structural dimensions, some studies lacked explicit reporting on contextual barriers, resulting in possible overrepresentation of successful interventions. Moreover, the majority of included studies originated from high-income countries, with only 18% representing low- and middle-income contexts, posing a challenge to equity-focused generalization.

Third, the review focused on published, peer-reviewed literature in English, which may have excluded valuable insights from non-English or grey literature sources, especially from LMICs where digital education is emerging informally or through local innovation.

Future research should pursue several critical directions. Longitudinal studies are needed to assess whether improvements in knowledge and procedural skills are sustained over time and translate into behavioral change in clinical settings. Furthermore, future trials should incorporate robust affective outcome measures, such as empathy, communication, and teamwork—often overlooked in digital education assessments.

Research should also explore faculty perspectives and readiness in implementing digital pedagogies, particularly in resource-constrained environments. Co-design studies involving learners, clinicians, and instructional designers could yield more context-responsive digital curricula. Finally, economic evaluations examining cost-effectiveness, return on investment, and scalability in diverse settings are essential to guide sustainable policy and funding decisions.

Authorship Criteria and Contributions

All authors meet the ICMJE criteria for authorship:

Dwi Mariyono (DM): Conceptualization, Methodology, Data Curation, Drafting of Manuscript, Supervision, Validation, Final Approval.
Junaidi, Junaidi (JJ): Data Extraction, Thematic Coding, Review and Editing, Supervision, Validation, Final Approval.

All authors have read and approved the final version of the manuscript.

Declaration of Interest Statement

The author declares no competing financial or personal interests that could have influenced the conduct, interpretation, or reporting of this research.

This work was motivated not by external funding or commercial interest, but by a sincere academic commitment to advancing equitable, context-sensitive, and innovation-driven anaesthesiology education. As the global medical community navigates digital transformation, it is imperative that we center pedagogy over profit, inclusion over infrastructure gaps, and long-term safety over short-term novelty.

This review emerges from that very commitment, a belief that knowledge should be actionable, evidence should inspire reform, and digital innovation must always serve human learning, not replace it.

Ethics and Consent to Participate

This study is a systematic literature review and did not involve human participants, personal data, or identifiable information. Therefore, ethics approval and informed consent were not required. The research was conducted in accordance with established academic integrity and ethical scholarship standards.

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Appendices A-C are not available with this version.

The authors declare no competing interests.

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Digital Education Strategies in Anaesthesiology: A Systematic Review and SWOT Analysis

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Abstract

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Introduction

Materials and methods

Research framework

Search strategy

Inclusion and exclusion criteria

Screening and quality appraisal

Data extraction and synthesis

Results

Effectiveness of digital education strategies in anaesthesiology training

Institutional and contextual factors affecting adoption and scalability

Discussion

Impact of digital strategies on learning outcomes

Institutional readiness and equity considerations

Theoretical and methodological contributions

Implications for Broader Health Professions Education

Conclusion

Policy implications

Limitations and future research

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References

Appendices

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