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BACKGROUND: A complex chain of events underpins every clinical intervention, especially those involving invasive procedures. Safety requires high levels of awareness and vigilance. In this paper we propose a structured approach to procedural training, mapping each learner's evolving experience within a matrix of clinical risk and procedural complexity. We use a traffic light analogy to conceptualize a dynamic awareness of prevailing risk and the implications of moving between zones. THE IMPORTANCE OF CONTEXT: We argue that clinical exposure can be consolidated by simulation where appropriate, ensuring that each learner gains the skills for safe care within the increasingly limited time available for training. To be effective, however, such simulation must be realistic, patient-focused, structured and grounded in an authentic clinical context. Challenge comes not only from technical difficulty but also from the need for interpersonal skills and professionalism within clinical encounters. PATIENT FOCUSED SIMULATION: Many existing simulations focus on crises, so clinicians are in a heightened state of expectation that may not reflect their usual practice. We argue that simulation should also reflect commonly occurring non-crisis situations, allowing clinicians to develop an awareness of the complex events that underpin clinical encounters. We describe a patient-focused approach to simulation, using simulated patients and inanimate models within realistic scenarios, to ground experience in authentic clinical practice and bring together the complex elements that underpin clinical events. APPLICATIONS: Although our argument has evolved from surgical practice and operating theatre teams, we believe it can be widely applied to the increasing number of health care professionals who perform clinical interventions.

Original publication

DOI

10.1111/j.1365-2923.2007.02799.x

Type

Journal article

Journal

Med Educ

Publication Date

08/2007

Volume

41

Pages

808 - 814

Keywords

Curriculum, Education, Medical, Graduate, Humans, Learning, Patient Simulation, Risk Assessment, Safety Management