Enhancing Authentic Learning in Simulation-Based Education Through Electronic Medical Record Integration: A Practice-Based Commentary

Abstract

As new technologies, such as electronic medical records (EMRs), are introduced into healthcare services, we need to consider how they may be incorporated into simulated environments, so as to maintain and enhance authenticity and learning opportunities. While EMRs have revolutionised clinical practice, many education settings continue to rely on paper-based documentation in simulation, creating a widening gap between educational environments and real-world clinical workflows. This disconnect limits learners’ ability to engage authentically with the tools and resources that underpin contemporary healthcare, impeding the transfer of knowledge to the clinical environment. This practice-based commentary draws on institutional experience from a large, multi-disciplinary simulation-based education facility that explored approaches to integrating EMRs into simulation-based education. It describes the decision points and efforts made to integrate an EMR into simulation-based education and concludes that while genuine EMR systems increase fidelity, their technical rigidity and data governance constraints reduce authenticity. To overcome this, Adelaide Health Simulation adopted an academic EMR (AEMR), a purpose-built digital platform designed for education. The AEMR maintains the functional realism of clinical systems while offering the pedagogical flexibility required to control data, timelines, and learner interactions. Drawing on this experience, this commentary highlights how authenticity in simulation-based education is best achieved not through technological replication alone, but through deliberate use of technologies that align with clinical realities while supporting flexible, learner-centred design. Purpose-built AEMRs exemplify how digital tools can enhance both fidelity and authenticity, fostering higher-order thinking, clinical reasoning, and digital fluency essential for safe and effective contemporary healthcare practice. Here, we argue that advancing simulation-based education in parallel with health service innovations is required if we want to adequately prepare learners for contemporary clinical practice.

1. Introduction

The transition from paper-based medical records to electronic medical records (EMRs) has transformed modern healthcare practice, fundamentally reshaping the way that healthcare professionals record, access, and manage clinical information. EMRs are widely accepted for their capacity to improve quality and safety, to enhance continuity of care, and to provide clinical decision support (Uslu & Stausberg, 2021). While health professions students and recent graduates are learning how to interact and engage with EMRs on clinical practice placements and upon entering the workforce, many university courses and simulation-based education programmes continue to rely on paper-based documentation (Mollart et al., 2023; Parrish et al., 2025).

Disparities in documentation tools and technologies between clinical practice and health simulation environments have become problematic, with paper-based documents now arguably irrelevant for those expected to deliver care in environments where paper-based records are no longer used. Now that EMRs are established as integral tools in many of our contemporary clinical workflows, they need to be integrated into simulated learning environments. Not doing so impedes our ability to provide authentic learning environments that can adequately prepare healthcare providers for their future workplaces and ignores opportunities to scaffold learning relating to digital literacy and fluency in our future workforce.

Authentic learning is considered a ‘pedagogical approach that approximates true-life experiences and involves learning tasks that require knowledge application in preparation for future use’ (Levin et al., 2023, p. 292). As an approach, simulation-based education is particularly focused on creating authentic learning activities (J. Lee et al., 2022). Authentic learning activities are considered to be ‘contextualized learning experiences such that learners recognize the value, utility, meaning, and functionality of the knowledge to be acquired, while promoting both cognitive (e.g., development of a deep understanding) and motivational (e.g., development of intrinsic motivation to learn) effects.’ (Nachtigall et al., 2022, p. 1480).

In designing individual learning activities, simulation curricula and simulation centres, decisions need to be made as to where time and resources should be allocated to maximise learner engagement and outcomes. In this practice-based commentary, we explore our rationale and decision-making processes for investing in an academic EMR (AEMR), as they relate to the concept of authentic learning. The reflections presented are informed by educational practice within our simulation programme over several years. Feedback that is described was gathered through informal discussions with learners and faculty during and after simulation activities and from educator reflections arising from scenario design and delivery. These insights were not collected through formal surveys or structured evaluation tools but emerged organically through ongoing teaching, implementation, and refinement of simulation activities. Learners in our context include undergraduate and postgraduate students from medicine, nursing, and allied health disciplines. We argue that embedding AEMRs within health simulation is becoming necessary for authentic learning experiences in our own and in similar contexts. These tools enable educators to design and deliver authentic learning activities and allow learners to engage with the technologies, data systems, and decision-making processes that define modern clinical practice.

2. Authentic Learning in Simulation-Based Education

Simulation-based education is a structured educational approach in healthcare that, in part, strives to replicate clinical encounters in an artificial environment for purposes including education, assessment, and research (Gaba, 2004). For several decades, simulation has been used as an educational approach to improve knowledge, skills, and attitudes of learners across a broad spectrum of clinical conditions and scenarios, with defined outcomes and individualised learning (Chernikova et al., 2020; Issenberg et al., 2005). It allows for structured approaches to clinical assessment and management when paired with defined learning outcomes and enables iterative practice and skill refinement through experiential learning (Elendu et al., 2024).

Simulation-based education has been widely championed across all facets of healthcare education, including undergraduate and postgraduate nursing, medical and allied health education in university settings, and continuing education in clinical settings (Elendu et al., 2024; Koukourikos et al., 2021). Over the past half-century, simulation modalities have diversified and matured to include physical models (task trainers) that support basic skills training, manikins, simulated patients, virtual environments including extended reality, and hybrid techniques that integrate multiple modalities (Alinier, 2007; Co et al., 2023). Our understanding of debriefing techniques has also evolved, emphasising reflective practice as a critical process to the translation of experiential learning into clinical capability and sustained knowledge retention (Sawyer et al., 2016).

As a teaching and learning approach, simulation-based education has adopted many of the situated learning elements posited by Herrington and Oliver (2000), including that authentic learning contexts ‘reflect the way knowledge will be used in real life’ (p. 25), that coaching and scaffolding are provided by the educator, that opportunities are provided for reflection and abstraction, and that learners have access to multiple roles and perspectives (Herrington & Oliver, 2000). Learners rely on educators to create authentic conditions that allow them to engage with simulated tasks in a manner that closely mirrors genuine clinical practice (C. Y. Lee et al., 2025; Levin et al., 2023).

In designing simulation environments, learning outcomes and debrief guides, educators need to carefully consider how to make the most of the time we have with learners: how do we make it authentic enough that our learners feel that they are engaging in the behaviours, strategies and conversations that are a facsimile of what occurs in real clinical environments. Likewise, educators rely on learners to ‘buy-in’ to simulated scenarios, knowing that some elements of the simulation will not entirely replicate clinical practice, but there is enough similarity that they are able to suspend their disbelief (Dieckmann et al., 2007; Muckler, 2017).

In the health simulation field, several terms have been used to describe the approximation of simulated clinical environments to the real clinical environments they represent (realism, immersive, fidelity, authentic learning environment, to name a few). These terms are frequently used inter-changeably in the literature and in practice, but they do have distinct meanings. Fidelity describes how closely a simulated environment reflects different aspects of real-world conditions (Lioce et al., 2025). Physical fidelity refers to the realism of anatomical features, clinical signs, and equipment; psychological fidelity refers to how real the simulation feels to the learner, and environmental fidelity relates to the surrounding clinical environment in terms of layout, cues, and contextual features (Lioce et al., 2025). While fidelity focuses on the reproduction of clinical conditions and features, authenticity refers to the extent to which a simulation aligns with real-world clinical practice, including the relevance of tasks and workflows, and the degree to which learners perceive the simulated activities as meaningful and transferable to the clinical setting (Lavoie et al., 2020).

A simulation may demonstrate high fidelity in one aspect yet lack authenticity in others if certain features do not translate into genuine clinical purpose or contextual relevance. For example, a simulation scenario may use a simulated participant with realistic physiological responses, appropriate and convincing moulage, and advanced bedside monitoring equipment, giving the appearance of a lifelike clinical encounter. However, when learners are handed a paper-based summary of the patient’s history and investigations, rather than having access to clinical information through the hospital’s EMR system, they know they are not following established clinical workflows employed in practice and are possibly more likely to disengage and be distracted from the goals of the scenario. Despite the high fidelity of this clinical simulation, the scenario lacks authenticity because it fails to replicate real clinical workflows and decision-making processes, limiting the transferability of learning to real patient care.

Creating authentic simulated learning environments, particularly for students and novice practitioners, requires significant investment in infrastructure and human resource (Naismith et al., 2025). In health, and other professions, this investment in simulation-based education is considered worthwhile for the opportunities it affords participants to learn challenging behavioural and technical skills that may not be safely acquired or experienced while on clinical placement or in practice (Chernikova et al., 2020; Lateef, 2010). When new technologies (such as EMRs) and practices are widely adopted in real clinical environments, simulation educators need to start considering how and when these changes will be incorporated into their simulated environments.

3. Our Context and Experience

Adelaide Health Simulation (AHS) is a simulation-based education facility located at Adelaide University in Australia. We have 48 dedicated teaching rooms, including 28 high-fidelity simulation suites, a 360-degree immersion room, extended reality capabilities, and advanced 4K audio-visual recording and debriefing capabilities across the facility. AHS provides training across the undergraduate and postgraduate spectrum to a multidisciplinary learner group incorporating medicine, nursing, and allied health. In the design, maintenance and running of the facility, AHS prioritises both fidelity and authenticity, integrating a wide range of physical and operational features with the aim of closely replicating real clinical environments and processes. Features include patient call bells, emergency response systems, phone and text paging systems, oxygen and medical air outlets, suction, fully functional monitoring equipment, and a realistic ward layout with a simulated pharmacy and nurses’ station.

AHS has consistently integrated medical records within its scenarios, aligning simulation workflows with those of clinical practice. Until 2023, this was achieved using paper-based charts, contributing to physical fidelity by mirroring the format of hospital documentation, and to authenticity by requiring learners to use and interpret clinical records correctly. We used copies of hospital stationary in physical patient folders, and included triage documentation, progress notes, observation charts, medication charts, infusion charts, and specialty observation forms such as fluid balance charts, neurological observations, alcohol withdrawal charts, and so forth. Laboratory and imaging test requests were also paper-based, with learners encouraged to complete investigation requests during simulation scenarios, as they would in clinical practice, before they could be provided with any results. These processes mirrored authentic clinical practice, with alignment of cognitive and communication workflows to those with healthcare systems. Learners were required to interpret and apply physically documented information within the context of the scenario, make informed clinical decisions, and document their actions and outcomes using paper-based charts. By engaging with clinical information in a format consistent with real practice, learners were encouraged to move beyond remembering and understanding clinical facts, and toward applying information in context, analysing complex and evolving data, evaluating clinical options, and, for advanced simulation participants, creating management plans. These high-order cognitive processes align with the revised Bloom’s taxonomy (Krathwohl, 2002; Bloom, 1974), and reflect the types of thinking required for safe and effective practice in digitally mediated healthcare environments.

Over the past decade, South Australia has progressively implemented a single statewide EMR across all public hospitals and public outpatient services, completing the rollout in April 2025 (Fletcher, 2025). This digital platform has now largely supplanted traditional paper-based records across the state, with the vast majority of clinical documentation now entered directly into the electronic system. This EMR facilitates real-time access to clinical documentation, and connects almost 100 sites across metropolitan and regional South Australia into a single system for a unified and integrated view of specialty input, laboratory results, imaging, and medication management.

As clinical documentation and workflows transitioned to digital systems, a growing fidelity gap emerged between contemporary clinical environments and AHS’ simulation facility, which continued to rely on paper-based records. As learners were increasingly expected to engage with the digital tools and documentation systems that underpin real-world clinical practice, this discrepancy challenged the authenticity and relevance of simulation activities.

Through the clinical transition period, AHS received informal feedback from both learners and educators indicating that paper-based charts no longer reflected contemporary clinical practice or workflows in hospitals and other clinical settings. Learners reported that using paper-based documentation to gather and record information felt increasingly artificial, as digital workflows were now more common. Educators also noted that simulation scenarios using paper records did not prepare learners for the realities of modern clinical practice, where clinical reasoning, documentation, and ordering are increasingly embedded into digital workflows. The absence of an electronic interface in simulation prevented learners from utilising this technology to practice their skillset, including navigating clinical records, understanding care inputs and recommendations from different health professionals, locating clinical information within electronic charts, contemporaneous ordering and documenting of clinical care. From a cognitive perspective, it was felt that this lack of authenticity may limit opportunities for learners to engage in higher-order thinking processes, such as data synthesis, clinical reasoning, and decision-making within an electronic context, and may therefore constrain the development of digital competency (‘the safe, critical, and creative use of digital technologies to achieve goals related to work, employability, learning, leisure, inclusion, and/or participation in society’ (Sanches, 2022, p. 475) and digital fluency (ethical and appropriate use of tools, with ability to adapt to new technologies and apply them in various contexts) (Makhafola et al., 2025).

Instead of directing their cognitive resources toward authentic problem-solving, learners were required to navigate an artificial process, mentally translating between paper-based and imagined electronic workflows. This mismatch risked not only increasing extraneous cognitive load but also limited the transferability of learning to real-world clinical environments. Ultimately, paper-based records no longer reflected current clinical fidelity nor authentic clinical practice, necessitating the exploration of digital alternatives within simulation.

4. Integrating an EMR into Our Simulation Centre

In 2021, we explored the feasibility of integrating the statewide EMR within the simulation programme at AHS. By 2022, substantial progress had been achieved through collaborative discussions with the statewide EMR team in our Department of Health, who are responsible for the implementation and ongoing management of the EMR locally. It was identified that the existing EMR training environment, previously used exclusively for the training of hospital staff in the use of the system, could potentially be adapted for simulation-based education purposes. In response, a series of requirements were subsequently defined in a collaboration between our simulation centre and a commercial EMR provider. Through a series of interviews with simulation educators and observations of simulation activity, EMR software technicians developed a set of high-level functional and technical requirements to support educational use. The statewide EMR was subsequently integrated into AHS in 2023, initially in a trial capacity across selected simulations and assessments. Feedback on the implementation was practice-based, arising from facilitated debrief discussions, educator observations, and informal learner feedback during and following simulated activities. This feedback was mixed: while the integration was commended for enhancing fidelity and aligning simulation practice closer to digital workflows, several challenges were identified relating to simulation-specific functionality, which reduced authenticity.

The integration enabled learners to use the same digital platform used across South Australian public hospitals and outpatient services in a simulated environment. We observed that learners were able to review simulated records pertinent to their patient, retrieve background medical history, review multi-disciplinary notes, record clinical observations, order and review laboratory investigations and imaging, and refer to other specialty providers. While simulated paper-based notes may have had dozens of data points, electronic documentation could be created that contained hundreds or even thousands of data points for learners to review and interpret. By replicating these real-world data interactions and conditions, and by interacting with an identical platform and interface to a genuine clinical setting, learners were observed to engage more fluently in contemporary digital health practices. These factors all increase the fidelity of the scenario.

Despite these benefits, we felt that authenticity was compromised due to gaps in clinical realism and workflow alignment. Although the EMR interface mirrored the clinical system visually, the data within it did not consistently reflect clinical realities. For example, patient documentation, observations, and investigation results were dated at the time of data entry, which may have been weeks in the past (and would eventually be months and years in the past), requiring learners to repeatedly adjust filters and search functions to locate relevant information. Data entered during previous simulation sessions also meant that incorrect or irrelevant entries sometimes remained in the record, reducing clinical coherence and causing confusion during decision-making. Learners were instructed to ignore system-generated alerts, such as duplicate medication orders or missing data from external systems, because these reflected limitations of the training environment rather than genuine clinical risk. Limitations such as these required workarounds that disrupted both the simulation and the typical flow of clinical tasks, reducing authenticity by requiring learners to engage with the EMR in ways that were markedly inconsistent with real-world practice. Despite the higher fidelity afforded by a genuine EMR system, the lack of alignment between the simulation and actual clinical workflows created a deficit in authenticity.

5. Importance of an Academic EMR

To address identified limitations of enterprise clinical EMRs and to enhance authenticity, AHS sought to implement an alternative EMR that was appropriate for simulation-based education. The goal was not only to increase fidelity from paper-based charts, but to enable learners to interact with digital documentation and systems in a manner that reflected real clinical workflows and decision-making processes, thereby creating a more authentic and immersive learning experience and increasing their opportunities to develop digital literacy and fluency. Achieving this required one of two approaches: extensive customisation of the genuine statewide EMR training environment already installed at AHS; or the use of bespoke simulation software designed to replicate the EMR interface.

Key features such as progressive case disclosure, controlled data timelines, the capability to reset patients between scenarios, and opportunities for deliberate practice without compromising data integrity were deemed critical to maintaining authenticity. However, despite extensive investigation, these simulation-specific requirements could not be accommodated within the statewide EMR environment due to software engineering constraints, governance restrictions, and prohibitive development and licencing costs. As a result, the adoption of a bespoke simulated AEMR emerged as the necessary approach.

An AEMR is a simulated or adapted version of a genuine EMR that has been purpose-built or substantially modified for use exclusively in the education setting (Raghunathan et al., 2021). These systems are designed to replicate the core functions of a genuine EMR, such as clinical documentation, order entry, medication charting, and review of patient information, while operating independently of external systems such as those used in pharmacy, laboratory, and imaging departments. Academic EMRs also include additional features specific to learning, which may include the ability to modify or reset patient cases, monitor learner activity, and control the sequence of clinical events (Hong et al., 2022). Recent advancements among commercial AEMR providers have incorporated artificial intelligence (AI) to generatively enhance the depth and complexity of patient records. Additional medical, nursing, and allied documentation, along with additional historical diagnoses and investigation results increases the quantity and richness of information available for learner review. This increased data density more closely replicates the complexity of contemporary clinical records, enabling learners to practice synthesising information across disciplines and over time, while also supporting the development of digital fluency required for effective EMR use in clinical practice. When deliberately designed and curated by educators, we see such generative augmentation using AI as having significant capability to enhance authenticity by reflecting real-world data complexity while remaining aligned with learning objectives and scenario intent.

AHS undertook an exploratory review of commercially available academic EMR platforms to identify a solution that would meet educational requirements in a simulation setting. The search included commercially developed AEMRs, open-source educational platforms, and modified training instances of clinical EMR systems. While not a formal procurement or research evaluation, it was guided by defined functional and technical requirements developed by the centre’s academic and technical team. These requirements reflected mandatory education and operational needs, and platforms were assessed against them on a feature-presence basis, rather than through scoring or ranking. This process identified several viable solutions, which were shortlisted for detailed evaluation, demonstration, and trial by educators. Following these trials, one platform was selected for an implementation trial based on consensus among educators that it best met the identified requirements and aligned with the intended educational use.

The benefits of an AEMR were rapidly realised, as it offered critical functionality required for simulation-based education, and overcame several of the discussed limitations of using a genuine EMR. A summary of the features we determined were important is presented in Table 1. Although the selected AEMR did not achieve the same level of fidelity as a live clinical EMR (largely due to the different interface), it was felt that authenticity was enhanced. We believe this was because authenticity is driven less by technical replication, but more by alignment with real clinical workflows, communication frameworks and decision-making processes. By enabling learners to locate clinical information, interpret documentation, link past medical history with current presentations, enter clinical notes, prescribe medications, and order and interpret investigations within a digital record system that mirrors the structure and logic of a genuine EMR, the use of an AEMR preserves the higher-order thinking skills and digital fluency required in practice. Unlike a genuine EMR, AEMRs allow educators to scaffold learning by controlling case progression, staging the release of laboratory and radiology results, and dynamically altering data based on the learner’s clinical decisions. Repeated practice is also supported through the ability to reset patients and reuse the same medical record. See Table 1 for features we required and recommend from an AEMR.

Table 1. Features required in an AEMR.

Core EMR Features

Simulation-Specific Features

Patient information management Name, title, DOB, URN Alerts Problem lists Past medical and surgical history Clinical documentation Triage and admission notes Progress documentation Nursing entries Allied health entries Care plans Handover tools (i.e., ISBAR) Past Documentation Previous case note entries Discharge summaries Referrals in/out Observation chart Vital signs charting Early warning scores Graphical interface Fluid balance chart Medication Management Electronic medication chart Electronic prescribing Allergies Interaction alerts Ordering Pathology and microbiology Radiology Consults and referrals Results Current results from orders Historical results

Control over clinical cases Create and customise patients Reset patient records Duplicate and reuse patients Multiple patient instances for concurrent sessions Dynamic editing of data Temporal scenario control Stage the release of information/results Manage clinical timelines Relative dates and times Learner activity logs Time-stamped logs for performance review and debrief Interface Clean and simplified design Ability to turn features on and off as required by the case objectives Links to clinical guidelines and decision support systems Administration Role-based access for faculty and students

6. Recommendations and Considerations

For institutions considering EMR or AEMR integration within simulation-based education, our experiences outlined in this commentary may support strategic decision-making. The recommendations and considerations presented here were derived from a university-based simulation service; however, they may be generalisable to other health simulation settings. Guiding our decisions, and the recommendations presented here, is a desire to develop authentic conditions that support meaningful educational engagement from learners.

First and foremost, we recommend exploring and understanding your institutional context and the broader health service environment in your region. This will guide decision-making and inform justifications for investment in EMR or AEMR technologies. For example, if EMRs are yet to be established in local health services, it may be premature to implement an EMR in simulation-based education. Conversely, if your clinical services have established EMRs, but your simulation services do not, a business case for investing in this technology might be considered.

As with all simulation technology and equipment, the implementation of an EMR or AEMR introduces ongoing maintenance, support, and governance requirements (Finnegan & Mountford, 2025). Decisions regarding software architecture, such as cloud- or web-based solutions versus on-premises hosting, have implications for resources, security, and support. Accordingly, business cases should incorporate adequate human resources with appropriate technical experience to sustain operations and maintenance beyond implementation.

Our experience reinforces the importance of early collaboration between educators, digital health teams, and EMR or AEMR vendors to support simulation-specific use cases. Such vendor partnerships may enable simulation-focused functionality that preserves clinical authenticity while providing the flexibility required for effective simulation-based learning. In this context, early engagement with vendors of enterprise production clinical EMR systems used within an institution’s local healthcare service may be valuable to determine whether existing platforms can be adapted to support simulation-based education. We found that many enterprise EMR systems currently lack dedicated simulation-specific functionality, representing a clear opportunity for vendors to embed features that support authentic learning. These discussions are best undertaken early in the decision-making process to inform product selection and alignment with learner and organisational needs.

Where healthcare services are selecting an enterprise clinical EMR system, consideration should be given to the extent to which the platform supports simulation- and education-specific functionality (Wilbanks et al., 2018). Evaluating a system’s capacity to support simulation-based education aligned with institutional learning goals and to enable authentic representation of real-world clinical workflows may help ensure alignment between clinical systems and learner education, while minimising duplication of platforms and mitigating future adaptation costs. Engagement with clinical simulation specialists during system selection and configuration is also important to ensure that any training functionality supports clinically meaningful simulation, rather than being limited to instruction in EMR navigation or EMR task completion.

When selecting an EMR or AEMR for simulation-based education, consideration should be given to the inclusion of simulation-specific functionality outlined in Table 1. These features align with those previously identified as supporting effective educational and simulation use (Kim et al., 2025; Nuamah et al., 2022). These features support the design, delivery, and evaluation of authentic simulation activities by enabling flexible control of cases, temporal manipulation of clinical information, and meaningful capture of learner interactions. Collectively, such capabilities allow educators to align EMR or AEMR use with learning objectives, support structured debriefing, and preserve clinical authenticity while maintaining the operational flexibility required for simulation-based learning.

Once the EMR or AEMR is established, planning authentic learning activities with learning outcomes related to digital literacy and fluency should be considered, as students do not acquire these skills passively (Sanches, 2022). Rather, they require purposeful integration into simulation design, including deliberate opportunities for learners to engage with clinical decision-making, documentation, and information synthesis within realistic workflows. Consistent with prior work, we observed that effective EMR integration in education requires deliberate pedagogical design rather than passive exposure (Lokmic-Tomkins et al., 2023).

AEMRs offer immense potential to expand simulated experiences beyond synchronous learning activities that occur within the simulation service. Asynchronous and inter-professional opportunities for learning linked to patient case studies and patient information from AEMRs can be explored and used to further leverage the justification for investing in this technology.

7. Limitations

As a practice-based commentary, this paper has inherent limitations to acknowledge. Practice-based commentaries prioritise experiential insight and reflective interpretation, rather than systematic data generation. The observations and arguments presented in this paper are shaped by our positionality, professional context and our local clinical and educational environments, and may limit generalisability. Readers are invited to interpret this commentary as an informed contribution to scholarly dialogue rather than as empirical evidence of best practice.

8. Conclusions

The choice of medical record system used to complement simulation-based education influences both scenario fidelity and authenticity. Traditional paper-based records, while simple and often familiar to educators, now reflect outdated clinical practice in modern healthcare. In contemporary EMR-driven healthcare settings, paper-based records demonstrate low fidelity, as they fail to reproduce the digital environment learners will encounter in practice, and low authenticity, as the workflows and associated processes diverge significantly from those used in real clinical contexts. In contrast, the integration of genuine EMRs within simulation-based education offers high fidelity, as it accurately replicates the digital systems and interfaces used in clinical environments. However, this technological realism may come at the expense of authenticity, as constraints inherent in genuine EMR systems reduce their flexibility for educational use.

AEMRs provide a solution that integrates the functional realism of genuine systems with the flexibility required for education. Purpose-built for learning, AEMRs retain sufficient fidelity to reflect the digital environments and interface design of genuine enterprise EMR systems. They enable educators to undertake education and simulation-focused activities, including the manipulation of data, control of case timelines, and resetting of scenarios. From an educational perspective, these features enhance authenticity by allowing learners to engage with the system in a manner that reflects real clinical practice, including contemporaneous case note entry, order entry, and interpretation of clinical data. The ability to balance fidelity with authentic learning supports meaningful engagement, fostering the development of higher-order clinical reasoning skills that are likely transferable to real-world contexts.

The clinical health environment is ever-changing, with new practices, technologies and evidence shaping workflows and practices. Health simulation educators need to be aware of these changes and make decisions about if, when, how and why these changes should be reflected and replicated in their simulation infrastructure and activities—balancing the desire to create authentic learning opportunities with resource realities and limitations. This commentary has provided an overview of the decisions we made as a simulation service and presented theoretically supported arguments for why we invested in an AEMR for our learners. As digital health technologies continue to shape our healthcare services, we hope that practice-based insights such as these support ongoing scholarly discourse and empirical exploration that enhances our learner experiences and outcomes.