Taxonomy of clinical simulation

Taxonomía de la simulación clínica: una revisión narrativa y un marco práctico basado en la vinculación entre objetivo de aprendizaje, estrategia educativa y herramienta de simulación

Introduction

Clinical simulation has consolidated its position as a reference educational methodology in the health sciences, because it makes it possible to recreate controlled clinical scenarios in which students acquire and refine competencies without exposing patients to unnecessary risk^1,2. The accumulated evidence shows that its systematic use contributes to reducing clinical errors, strengthening patient safety and developing technical and non-technical skills that transfer to the real care setting^3,4. However, the prevailing classification in the literature is still based on the level of technological fidelity —low, medium and high—, a criterion centred on the device rather than on the learner’s pedagogical needs, and one that is insufficient to guide training decisions^5,6.

Authors such as Hamstra et al. and Motola et al. have shown that the effectiveness of simulation does not depend on the technological complexity of the simulator, but on how relevant the scenario is to the learning objective —a concept known as functional fidelity— and on the constructive alignment between tool, educational strategy and training objective^7,8,9. The theoretical frameworks of Bloom, Miller and Kolb provide the basis for structuring this progression, from the acquisition of declarative knowledge to the demonstration of competencies in contexts of increasing complexity^10,11,12. The growing ecosystem of tools —from task trainers to virtual patients powered by artificial intelligence— calls for a classification system that prioritises the educational intent of the clinical educator^13,14. Against this background, this paper proposes a functional taxonomy of clinical simulation organised into five categories defined by three axes: the type of simulator, the learning strategy and the training objective.

Theoretical framework

Clinical simulation as an educational methodology

Clinical simulation can be defined as the use of materials, anatomical models, trained people or technological environments to reproduce real clinical situations for training, assessment or research purposes^1,15. This broad definition ranges from the practice of procedural skills on anatomical models to the resolution of clinical crises in in situ simulation settings or the interaction with virtual patients mediated by artificial intelligence.

Kolb’s principles of experiential learning underpin the effectiveness of simulation by proposing a cycle that moves from concrete experience to reflective observation, abstract conceptualisation and active experimentation¹¹. Clinical simulation provides the ideal setting for this cycle, since it offers concrete experiences in safe environments where error is part of learning and does not pose a threat to the patient¹⁶.

Limitations of the technological fidelity model

The classification model based on the level of technological fidelity arose in response to the need to organise the wide range of tools available. However, recent studies have questioned its pedagogical usefulness, showing that high technological fidelity does not in itself guarantee better learning outcomes than those achieved with less complex tools^5,17.

Hamstra et al. argued that the concept of technological fidelity has been systematically misinterpreted in the literature, and proposed replacing it with that of functional fidelity, which describes the degree to which the simulated environment activates the learning processes required to achieve a particular competency⁷. This perspective is more consistent with the principles of constructive alignment proposed by Biggs, which emphasise the necessary congruence between the learning objective, the learning activity and the assessment method⁶.

Taxonomic foundations in health sciences education

Bloom’s taxonomy, as revised by Anderson and Krathwohl, offers a useful framework for classifying learning objectives according to the cognitive processes involved, from recognition and understanding to analysis, evaluation and creation¹⁰. In the health sciences, Miller’s pyramid complements this approach by incorporating the psychomotor domain and the situational dimension of clinical performance¹².

Integrating these theoretical frameworks with the available evidence on the differential effectiveness of the various simulation modalities provides the grounds for proposing a functional taxonomy that guides pedagogical decision-making in a systematic and replicable way^8,18.

Objective

To propose a functional taxonomy of clinical simulation, based on the alignment between the learning objective, the educational strategy and the simulation tool, as an alternative to the traditional model of classification by technological fidelity, with the aim of guiding the clinical educator’s pedagogical decision-making in a well-founded, practical and replicable way.

Methods

A narrative review was carried out of the scientific literature published between 2016 and 2026 in the PubMed, SciELO, Scopus and Google Scholar databases. The search was conducted in Spanish and English using the following terms and their combinations: clinical simulation, simulation taxonomy, standardized patient, task trainers, high-fidelity simulation, learning objectives, health sciences education, simulation fidelity, virtual reality simulation, artificial intelligence medical education.

Original articles, systematic reviews, narrative reviews and consensus documents were included if they addressed: (a) clinical simulation modalities and their characteristics; (b) learning theories applied to the health sciences; (c) the effectiveness of simulated tools in the acquisition of competencies; and (d) simulation classification models current in the literature^19,20. Articles published before 2016 were excluded —except for theoretical references widely cited in the current literature—, as were studies without access to the full text.

Results

The literature review made it possible to identify consistent patterns regarding the differential relevance of each simulation modality according to the type of competency to be developed. On the basis of this analysis, a taxonomy of five functional categories is proposed, each defined along three structuring axes: the main simulator group or tool, the most suitable learning strategy and the learning objective pursued.

Category 1: Task trainers

Task trainers are partial anatomical models designed for the repetitive practice of specific procedures. Their pedagogical strength lies in the possibility of separating procedural dexterity from the clinical complexity of the real patient²¹,²². Through deliberate practice and immediate feedback, the learner develops muscle memory, hand-eye coordination and the automation of procedural sequences that form the basis of technical competence²³.

This category includes models for venepuncture, central venous catheterisation, orotracheal intubation, suturing and auscultation, among others. The available evidence indicates that practice with task trainers before real clinical exposure significantly reduces procedural errors and improves patient safety in real care settings²⁴.

Category 2: Standardised patient

The standardised patient (SP) is a person trained to reproduce, consistently and in a standardised way, the signs, symptoms and behaviours of a patient with a defined clinical presentation. This modality is the most effective tool for developing competencies in clinical communication, history-taking, the doctor-patient relationship and interpersonal judgement²⁵,²⁶.

Several studies have documented the validity of the SP as an instrument for formative and summative assessment, particularly in the context of the OSCE (Objective Structured Clinical Examination), where it allows the student’s communicative and interpersonal competencies to be measured in a standardised way²⁷. Structured debriefing after the interaction with the SP has proved to be an essential component for consolidating learning and encouraging critical reflection on practice²⁸.

Category 3: Patient and team simulators

This category groups the computer-controlled simulation manikins that reproduce dynamic physiological responses, as well as simulation environments that include real or simulated clinical equipment. Their main pedagogical contribution is the possibility of recreating clinical scenarios of medium-to-high complexity that demand the integration of technical and non-technical skills, crisis management and patient-centred decision-making²⁹,³⁰.

Team simulation with this modality has proved particularly effective for training competencies in clinical leadership, communication in emergencies and the application of crisis protocols. Integrating CRM (Crisis Resource Management) as an educational strategy in these scenarios has shown positive results in improving teamwork dynamics and reducing preventable adverse events³¹,³².

Category 4: Ultra realism

Ultra-realism simulation is characterised by the reproduction of clinical environments of high complexity and pressure, including disaster and mass-casualty scenarios, emergency surgery or pre-hospital care, and even patient simulators with ultra-realistic anatomical features that create an emotional impact. The main objective of this category is not the acquisition of new skills, but to support the maintenance of emotional intelligence and ethical judgement, as well as the integration and consolidation of previously acquired competencies under conditions of controlled stress³³,³⁴.

Neuroscientific research on memory under stress and on the automation of procedural responses in threatening situations supports the educational value of this modality when it is used with learners at advanced stages of their clinical training³⁵.

Category 5: Digital tools

Digital tools are the most recent addition to the field of clinical simulation and the category with the greatest growth potential in the coming years. They include virtual patients based on large language models, virtual and augmented reality platforms, diagnostic imaging simulators, clinical decision-making software and adaptive learning systems³⁶,³⁷.

Their main pedagogical value lies in the ability to personalise the learning process, adapting the difficulty and type of feedback to the individual needs of the learner. Recent research has shown the effectiveness of virtual patients for training clinical reasoning and differential diagnosis, particularly in settings with limited access to real clinical scenarios³⁸,³⁹.

Practical selection framework

Taking into account the definition of each category, Table 1 illustrates how to apply the framework to the selection of the tool on the basis of the pedagogical objectives defined for structured simulation.

Pedagogical objective	Competencies involved	Teaching strategy	Recommended tool
Acquire or automate a specific procedural skill	Discrete, repeatable technical skills: motor dexterity, hand-eye coordination and muscle memory.	Deliberate practice, step-by-step sequenced training and immediate corrective feedback.	Task trainer
Develop communication, clinical interviewing or therapeutic relationship competencies	History-taking, breaking bad news, the doctor-patient relationship and interpersonal judgement.	Guided interaction with structured communication feedback and supervised verbal debriefing.	Standardised patient
Strengthen clinical reasoning and diagnostic and therapeutic decision-making	Differential diagnosis, managing uncertainty, evidence-based prescribing and adaptive learning.	Sequential analysis of interactive clinical cases, asynchronous or blended, with progressive disclosure of information.	Digital tools
Integrate technical and non-technical skills in a real clinical context	Teamwork, leadership, situational awareness and crisis resource management (CRM), together with clinical skills.	Complete clinical scenarios with contextualised simulation, interdisciplinary work and structured debriefing.	Patient and team simulator
Train the response to critical, low-frequency, high-impact scenarios	Stress response, empathy, decision-making under pressure and integration of competencies in complex environments.	Crisis scenarios with full environmental immersion and a high, controlled contextual and emotional load.	Ultra realism

This table offers a practical guide for selecting the most suitable tool according to the pedagogical objective, and can serve as a basis for planning, organising and procuring equipment in simulation centres and university settings.

Discussion

The functional taxonomy proposed in this paper is a pedagogically more useful alternative to the model of classification by technological fidelity that prevails in the literature. By anchoring the selection of the simulated tool in the educator’s educational intent and in the characteristics of the learning objective, this proposal operates in line with the principles of constructive alignment and competency-based education^6,8,40.

One of its main strengths is its practical applicability: the educator or trainer can make well-founded pedagogical decisions by identifying the type of competency they wish to develop, without having to start from technical or budgetary criteria. This is especially relevant in resource-limited settings.

To make it easier to apply, Table 2 summarises the relevance of each tool, making it possible to prioritise which one to select and to identify those that can contribute to the development of several competencies.

Learning objective	Task trainer	Standardised patient	Digital tools and AI	Patient and team simulator	Ultra realism
Specific procedural skill	Primary	Low	Secondary	Secondary	Low
Clinical communication / interviewing / doctor-patient relationship	Low	Primary	Secondary	Secondary	Low
Clinical reasoning / decision-making	Low	Secondary	Primary	Secondary	Low
Integration of technical and non-technical skills	Secondary	Secondary	Secondary	Primary	Secondary
Response in complex or critical scenarios	Low	Low	Secondary	Secondary	Primary

It should be borne in mind that low relevance does not rule out the use of a tool; it reflects a weaker fit for that particular objective. The pedagogical usefulness of each resource also depends on the simulation context, on its integration with other teaching strategies and on the purpose of the clinical scenario.

The proposed categories are not mutually exclusive: a well-designed scenario can integrate elements of several of them at the same time. Moreover, the assignment of a tool to a specific category will depend on the type of debriefing used^28,42.

Including digital tools as a separate category reflects the growing evidence of their effectiveness for developing clinical reasoning and the transformation of the educational field that their integration brings about^36,43. However, research in this area is still at an early stage, and more methodologically rigorous studies will be needed.

Conclusions

The proposed functional taxonomy of clinical simulation offers a practical, replicable framework that allows the educator or trainer to select the most suitable tool according to the learning objective they wish to achieve, the educational strategy they plan to implement and the learner’s level of competence. This approach represents a conceptual shift from the object to the action.

The five proposed categories —task trainers, standardised patient, patient and team simulators, ultra realism and digital tools— form an organisational structure broad enough to cover most of the usual scenarios in clinical simulation within the health sciences, guiding clinical educators, trainers and simulation unit managers towards well-founded decision-making.

Finally, we conclude that the most effective clinical simulation is not the most technologically advanced, but the one whose objectives, strategies and tools are coherently aligned with the training needs of future health professionals.

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