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Enregistrement W3002290462 · doi:10.4103/ijam.ijam_52_17

What's new in academic medicine? Simulation in medical education: An imperative for efficient learning, patient safety, and interprofessional collaboration

2017· article· en· W3002290462 sur OpenAlexaboutno aff
Konstantinos Stroumpoulis

Notice bibliographique

RevueInternational Journal of Academic Medicine · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueSimulation-Based Education in Healthcare
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDebriefingPatient safetyTeamworkMedical educationDeclarationAnesthesiologyHealth careMedicinePsychologyComputer science

Résumé

récupéré en direct d'OpenAlex

Like aviation, medicine is a high-risk industry where errors can be fatal and extremely expensive. The need for creating a controlled and safe environment where doctors (both juniors and seniors) can be exposed in high-risk conditions that could rarely be encountered in the clinical setting has been widely understood particularly over the past two decades. However, errors do not only occur in difficult or rare clinical settings. It has been found that errors occur in our everyday clinical practice and they involve mostly nontechnical skills. Furthermore, whereas medical simulation training at its early beginnings focused on training in following algorithms and procedures, it was rapidly recognized that it should also focus on human factors and teamwork.[1,2] Simulation is a standardized process that enables trainers to reproduce clinical settings of infinite variety and complexity so as to allow to health-care professionals of with different skills and background to achieve clinical competency as a team. In a practice that tolerates zero margin of error, and where error is poorly dealt and accepted by physicians, simulation has a paramount role to play in a paradigm shift to our current practice by promoting feedback, debriefing, and discussion. Of all medical specialties, anesthesiologists and surgeons have been the pioneers in simulation training over the past three decades. Since 2010, the “Helsinki Declaration for Safety in Anaesthesiology” by the European Board of Anesthesiology and the European Society of Anaesthesiology has been disseminated and adopted or supported from numerous countries worldwide.[3] The declaration recognizes that human factors are of primordial importance in the delivery of safe patient care to. Therefore, operating room teams (anesthetists, surgeons, nurses, as well as all other clinical partners involved), should work in close cooperation to effectively and reliably provide this. The text emphasizes the role of education's key role in improving patient safety and stresses the need of “development, dissemination, and delivery of patient safety training” stipulating thus that team training should become part of the curricula.[3] Furthermore, in 2014, the Accreditation Council for Graduate Medical Education (ACGME) in the United States implemented the “Next Accreditation System” for anesthesiology, emphasizing on “competency-based” learning in residency. Among the ACGME core competencies, we can find knowledge, patient care, professionalism, communication and interpersonal skills, practice-based learning and improvement, and systems-based practice. The same applies for several other national anesthesiology societies as in Israel, UK, Canada, and Australia.[4,5,6] Traditional training inside a single specialty resulted in enhancing bonds, communication and perhaps performance between health-care professionals of the same specialty (e.g., anesthetists and nurse anesthetists) but at the same time, it could lead to the creation of barriers between them and practitioners coming from other specialties (surgeons). One could say that an anesthetic team may be perfectly aware of and capable of perfectly following hemorrhage management algorithms but without correct communication with the surgical team and vice versa the outcome could be detrimental for the patient. However, there is a fundamental misconception in that statement: there are no different teams! The health-care professionals taking in charge a patient should constitute a unique team using the same communication codes. This topic of multidisciplinary simulation is analyzed in detail by Stroud et al., in the present issue. Current research indicates that simulation training not only enhances technical skills acquisition (central venous line insertion, lumbar puncture fiber-optic intubation, and advanced cardiac life support[7,8,9]) but it also enhances teamwork, leadership, decision-making, and critical thinking.[10,11,12] In addition to the above, simulation has been proven useful in developing clinical decision support systems (CDSSs) not only in anesthesiology and surgery but also in geriatrics, emergency department, Intensive Care Unit (ICU), decision-making in end of life ICU use, policy making in stroke prevention, and management of in-hospital cardiac arrest.[13,14,15,16,17,18,19,20,21,22] However, creating CDSSs is an extremely complex task to undertake as it necessitates an analysis not only of the clinical requirements and of the cognitive processes that take place during a given medical activity, but they also have to provide and present an information in an optimal context that fits the receptor's (i.e., the health-care professional) processing activities so that they can achieve situation awareness and be accurately guided through their decision-making process. Pappada and Papadimos describe how CDSSs embody the principles of medical simulation and allow their transfer to clinical practice. It was at late 1980's when David Gaba identified the existence of gaps concerning nontechnical skills in the curriculum of anesthesia training programs. Since then it has been well clear that to effectively close these gaps nontechnical skills should perhaps be taught earlier than in residency but during medical studies.[23,24] Many medical school curricula are now incorporating simulation technology into their undergraduate and postgraduate programs, recognizing the ability of all levels of simulation to provide students with hands-on training in practically every clinical aspect even the rarest ones.[25,26,27] Direct hands-on learning and the classical “see one, do one, teach one” approach has disadvantages (patient discomfort, preventable errors) and raises important issues from the ethical viewpoint. As Jenkins et al. point out, high-fidelity simulation has probably an important role to play at that point, as it enables students at different academic levels to learn, apply and test their knowledge in a realistic environment simulating any given clinical field with limitless possibilities. Furthermore, no patient is put at risk, and the process can be repeated as many times as deemed necessary. What is most important is that not only medical students but practically all health-care students can be involved early in their academic life in simulation and “initiated” in the principles of communication and teamwork as depicted by Lipps et al. in the present issue. However, when coming to developing scenarios in simulation, an important question arises: Does one size fits all? For example, when preparing an obstetric airway scenario does a consultant anesthetist have the same didactic and training needs as a 3rd year resident in obstetric airway management? And even more, does a senior consultant have the same training needs as a junior consultant? Or, how will an experienced or team will respond under a junior anesthetist and vice versa? When developing a simulation scenario educational needs and goals have to be assessed and clearly defined by the educators, and team task analysis has to be undertaken. Bandhary et al. elaborate on this intriguing process of scenario development strategies. Another medical specialty that requires extensive hands-on training and teamwork skills development is certainly surgery. Vanderbilt et al. and Strosberg et al. analyze the role of medical simulation in this field of medical education. Data suggest that psychomotor performance when assessed in a virtual environment can correlate with technical skills in the operating room.[28] Furthermore, there is evidence that the skills acquired during simulation training (using predefined proficiency levels inside a structured program) are transferable in the operating room.[29,30,31,32] Data coming from meta-analyses support these findings, however, the impact on patient outcome is yet unclear.[33,34] Almost two decades after admitting that to “err is human,”[35] we are still struggling to find the optimal educational method for minimizing medical errors. Research for objective measures of competence and for appropriate assessment tools is constant, and simulation has certainly an important role to play in that process. As abovementioned, simulation has been proven to improve learner's technical skills but also teamwork and communication skills. However, its effect on patient outcome has yet to be proven in large, adequately powered studies (there are nevertheless encouraging data).[36,37,38] Learning cannot be guaranteed by simulation only, but simulation has to be considered as an important and indispensable tool in modern teacher's armamentarium, integrated in the traditional curriculum. But do only the clinicians/trainees/learners commit errors? Teachers/educators also do. The most common mistake in simulation training is that it focuses on realism instead of learning objectives. A faculty of competent educators/tutors should be the primary concern of any simulation center, to be able to identify and to successfully adapt to each particular learning group's needs. Continuous education and evaluation of these educators are strongly warranted. A triadic assessment (assessment from the students, peer- and self-assessment) seems necessary for tutors in the modern era. Motivated tutors, constantly reflecting on the philosophical questions of teachers' role, applying the principles of adult learning, and inspiring others to become like them, are the mentors required for a successful health-care system. Simulation has a certainly a primordial role to play in modern medical education to ensure Hippocrates' dictate: “Regarding disease, do two things: (Act) in patient's benefit or Acknowledgments The author wishes to thank Prof. Tom Papadimos for his editorial support and for his valuable feedback.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,029
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,055

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,029
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0030,010
Communication savante0,0130,022
Science ouverte0,0030,006
Intégrité de la recherche0,0090,015
Charge utile insuffisante (le modèle a refusé de juger)0,0140,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,045
Tête enseignante GPT0,503
Écart entre enseignants0,457 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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