Assessing the semantic content of clinical case presentations

Bordage, Georges; Connell, Kristopher J.; Chang, Ru-Yng; Gecht, Maureen R.; Sinacore, James

doi:10.1097/00001888-199710000-00036

Cited by 18 publications

(30 citation statements)

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“…Ces réseaux sémantiques peuvent être plus ou moins déve-loppés. Bordage propose une classification du discours des médecins en quatre catégories, selon la richesse de l'organisation de leurs connaissances et leur capacité à comparer et contraster les hypothèses diagnostiques en utilisant les données cliniques (Tableau 1) 40,49 . Les connaissances peuvent être: 1) réduites (le discours ne révèle ni effort de transformation sémantique, ni connexion entre données du patient et connaissances); 2) dispersées (quelques transformations, mais les hypothèses sont désordonnées, ne se réfèrent pas aux données obtenues et sont listées sans être comparées et contrastées les unes aux autres); 3) élaborées (les transformations sémantiques sont nombreuses et utilisées à bon escient pour comparer et contraster les hypothèses) ; 4) compilées (le clinicien reconnaît d'emblée un ensemble sémantique de données cliniques qu'il associe à une hypothèse clinique).…”

Section: Les Architectures Complexesunclassified

Le raisonnement clinique: données issues de la recherche et implications pour l’enseignement

et al. 2005

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Section: Les Architectures Complexesunclassified

Le raisonnement clinique: données issues de la recherche et implications pour l’enseignement

et al. 2005

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“…Why is this important? By analyzing transcripts from medical students' and experienced physicians' oral case presentations, Bordage and colleagues have shown that the "think-aloud" discourse patterns of clinicians who eventually arrived at the correct diagnosis used language structures representative of a broad and deep understanding of the clinical problem (Bordage et al 1997;Bordage and Lemieux 1991). Specifically, those physicians with greater diagnostic competence translated specific clinical features into abstract semantic qualifiers, which facilitated their ability to abstractly define the clinical problem that needed to be solved.…”

Section: Clinical Vocabularymentioning

confidence: 99%

“…Examples of semantic qualifiers are shown in the third column of Tables 4.1 and 4.2. One small study among third-year medical students completing standardized patient examinations noted that students who used semantic qualifiers during case presentations as compared to those who simply reported the patient's signs and symptoms demonstrated stronger diagnostic competence (Bordage et al 1997).…”

Section: Clinical Vocabularymentioning

confidence: 99%

Prerequisites for Learning Clinical Reasoning

Bowen

Cate

2017

Innovation and Change in Professional Education

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“…Students have greater recall and can more easily and accurately classify prototype cases and master categories of cases when initial learning occurs through prototypes. 11 We selected patient conditions that were especially appropriate for demonstrating important teaching points. Since head injuries are complex and difficult to assess, staying alert for delayed problems, and recognizing and preventing secondary injuries from occurring (like increased intracranial pressure from a hematoma) is vital.…”

Section: The Use Of Teaching Pointsmentioning

confidence: 99%

“…These experts have had the chance to identify the most representative problems and case features, and can choose the most prototypical physiologic signs and symptoms encountered in the field. 11 In this project, representative cases-with progressively complex multiple traumatic injuries were selected for prototyping to emphasize teaching points.…”

Section: Patient Cases Selectedmentioning

confidence: 99%

A virtual reality training system for the triage and stabilization of head trauma and multiple injury patients.

Freeman¹,

Thompson²,

Allely³

et al. 2000

PsycEXTRA Dataset

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SUMMARY ProblemEarly and accurate intervention for medical emergencies sustained on the battlefield, in chemical/biological warfare environments, or while rendering humanitarian service is critical to saving lives and limiting long-term disability. Head traumas and multiple injury cases are particularly complex to treat. Optimum emergency care requires improved training for first responders and military medical personnel. Conventional training techniques, such as classroom instruction and field exercises, and noncombat experience acquired in the hospital contribute to the learning process, but have innate drawbacks and limitations. As a result, the inexperienced provider may suffer in performance when faced with limited supplies and the demands of stabilizing casualties never encountered back home in the resource-rich hospital setting. Training could be significantly improved if trainees had the opportunity to repeatedly practice on virtual patients using immersive, dynamic case simulations and could experience the consequences of their assessment and treatment decisions. ObjectiveThe objective of this project was to develop a virtual reality training system that would provide immersive, three-dimensional (3D) graphical training opportunities for teaching emergency response skills to medical providers. The simulation tool would provide a virtual environment in which trainees could see, hear, and interact with simulated casualties. The system would simulate both the dynamic state of the patient and the results of trainee intervention, allowing the trainee to perform diagnoses and interventions and to receive immediate feedback (in the form of changing patient symptomatology) on the trainee's assessment and triage decisions. ApproachWe selected a training approach employing experiential, problem-based learning. The system was designed to provide a clinically accurate, dynamic environment engaging the user and facilitating the acquisition of emergency decision-making skills in high-stress, resourceconstrained settings. Representative cases emphasizing important teaching points were identified. Subject matter experts developed clinical algorithms for these cases using a simple mathematical model, the Finite State Automata (FSA), for casualty simulation. The FSA depicted the succession of physiological changes occurring in the patient as a result of the natural course of the trauma; specific actions initiated to treat the patient, both optimal and suboptimal; the absence of treatment; and the passage of time. ResultsA modular training capability was developed by adapting and refining preexisting multimedia personal computer and virtual reality systems. In this project, the server and user interfaces were developed into separate modules. This enabled one case simulation to be presented to different types of user interfaces. The final system included five components: (1) a tool for authoring simple trauma cases, (2) the FSA-based case algorithms, (3) the server with resident data files and algorithm-based code, (4) a t...

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Assessing the semantic content of clinical case presentations

Cited by 18 publications

References 0 publications

Le raisonnement clinique: données issues de la recherche et implications pour l’enseignement

Le raisonnement clinique: données issues de la recherche et implications pour l’enseignement

Prerequisites for Learning Clinical Reasoning

A virtual reality training system for the triage and stabilization of head trauma and multiple injury patients.

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