Knowledge based solution strategies in medical reasoning

Patel, Vimla L.; Groen, Guy J.

doi:10.1016/s0364-0213(86)80010-6

Cited by 255 publications

(265 citation statements)

References 23 publications

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“…This is supported by findings that while clinical volume is related to outcomes, there is still great variation in the performance of surgeons with high case volumes [6,25]. Similar to experts in other domains, expert surgeons have superior pattern recall and recognition and use both forward and backward reasoning with a highly structured knowledge base [26][27]. They also demonstrate greater automaticity, superior self-monitoring and less distractibility [27][28].…”

Section: The Expert Surgeonmentioning

confidence: 76%

Sim One, Do One, Teach One: Considerations in Designing Training Curricula for Surgical Simulation

Kolozsvari

Feldman

Vassiliou

et al. 2011

Journal of Surgical Education

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Section: The Expert Surgeonmentioning

confidence: 76%

Sim One, Do One, Teach One: Considerations in Designing Training Curricula for Surgical Simulation

Kolozsvari

Feldman

Vassiliou

et al. 2011

Journal of Surgical Education

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“…Might this pattern of representation change for more expert subjects who learn more complex diagnostic tasks, such as actual medical diagnosis? Patel and Groen (1986) found evidence, based on verbal protocols obtained during a task involving explanation of medical cases, that the overall direction of reasoning may reverse from less skilled to expert diagnosticians. The protocols of less experienced subjects tended to refer first to diseases and then to the symptoms they might account for (i.e., following the cause-to-effect direction), whereas the protocols of experts tended to move directly from symptoms to a diagnosis (i.e., following the effect-to-cause direction).…”

Section: Possible Changes In Diagnostic Reasoning With Expertisementioning

confidence: 99%

Predictive and diagnostic learning within causal models: Asymmetries in cue competition.

Waldmann¹,

Holyoak²

1992

Journal of Experimental Psychology: General

334

510

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Several researchers have recently claimed that higher order types of learning, such as categorization and causal induction, can be reduced to lower order associative learning. These claims are based in part on reports of cue competition in higher order learning, apparently analogous to blocking in classical conditioning. Three experiments are reported in which subjects had to learn to respond on the basis of cues that were defined either as possible causes of a common effect (predictive learning) or as possible effects of a common cause (diagnostic learning). The results indicate that diagnostic and predictive reasoning, far from being identical as predicted by associationistic models, are not even symmetrical. Although cue competition occurs among multiple possible causes during predictive learning, multiple possible effects need not compete during diagnostic learning. The results favor a causal-model theory.

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“…Este proceso, ilustrado en la Figura 1, continua siendo promovido por individuos cercanos al movimiento de la Medicina Basada en la Evidencia 9,10 . Mientras que algunos han argumentado que el flujo de información hacia delante ilustrado en la Figura 1 (p.ej., razonamiento desde la evidencia hacia los diagnósti-cos) es el que mejor capta la esencia del razonamiento clínico "experto" 11,12 , las probabilidades subsiguientes detectadas también podrían usarse fácil-NOTA: Los diagnósticos únicos están indicados con números, las características clínicas con letras. Cada característica mantiene una relación única (p.ej., peso) con cada diagnóstico.…”

Section: ¿Cuál Es El Diagnóstico Más Probable (Y Cómo Se Ha Llegado Aunclassified

Lo que todo profesor necesita saber sobre el razonamiento clínico

Kevin

2005

Educ. méd.

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Contexto: Una de las tareas nucleares que se asignan a los profesores clínicos es capacitar a los estudiantes para escoger entre un grupo de características que presenta un paciente y asignar con precisión una etiqueta diagnóstica con el objetivo final de desarrollar una estrategia de tratamiento apropiada. Durante los últimos 30 años se ha debatido considerablemente en el seno de la literatura en Educación en Ciencias de la Salud, sobre el modelo que mejor describe cómo los clínicos expertos generan decisiones diagnósticas. Propósito: El propósito de este ensayo es proporcionar una revision bibliográfica de la investigación sobre razonamiento clínico. Los puntos fuertes y débiles de las diferentes aproximaciones del razonamiento clínico se examinarán usando una de las principales divergencias se da entre los modelos de estrategia de razonamiento analítico (p.ej., consciente / controlado) versus estrategias de razonamiento no analítico (p.ej., Context: One of the core tasks assigned to clinical teachers is to enable students to sort through a cluster of features presented by a patient and accurately assign a diagnostic label, with the development of an appropriate treatment strategy being the end goal. Over the last 30 years there has been considerable debate within the Health Sciences Education literature regarding the model that best describes how expert clinicians generate diagnostic decisions. Purpose: The purpose of this essay is to provide a review of the research literature on clinical reasoning for frontline clinical teachers. The strengths and weaknesses of different approaches to clinical reasoning will be examined using one of the core divides between various models (that of analytic (i.e., conscious/controlled) versus non-analytic (i.e., unconscious/automatic) reasoning strategies) as an orienting framework. Discussion: Recent work suggests that clinical teachers should stress the importance of both forms of reasoning, thereby enabling students to marshal Lo que todo profesor necesita saber sobre el razonamiento clínico* Correspondencia:

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Knowledge based solution strategies in medical reasoning

Cited by 255 publications

References 23 publications

Sim One, Do One, Teach One: Considerations in Designing Training Curricula for Surgical Simulation

Sim One, Do One, Teach One: Considerations in Designing Training Curricula for Surgical Simulation

Predictive and diagnostic learning within causal models: Asymmetries in cue competition.

Lo que todo profesor necesita saber sobre el razonamiento clínico

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