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  • Title: Model-based simulation for early neurosurgical learners.
    Author: Selden NR, Origitano TC, Hadjipanayis C, Byrne R.
    Journal: Neurosurgery; 2013 Oct; 73 Suppl 1():15-24. PubMed ID: 24051878.
    Abstract:
    BACKGROUND: Restrictions on duty hours and shift length by the Accreditation Council for Graduate Medical Education and public pressure to reduce complications and to improve outcomes in the clinical educational environment have enhanced interest in the use of procedural and surgical simulation to train neurosurgical residents. OBJECTIVE: To introduce simple, available, and, when possible, inexpensive model-based simulation for early learners into the initial stages of neurosurgical residency training. METHODS: Simulation for early-stage trainees in neurological surgery has taken advantage of model-based systems. The Society of Neurological Surgeons postgraduate year 1 courses have served as one paradigm for designing and using model-based simulators for procedural and surgical skill training as part of a purpose-designed overall curriculum. Ongoing surveys of resident and faculty course participants have supported iterative improvements in simulator models and curriculum from year to year. RESULTS: Simulation for basic neurosurgical and intensive care procedures has been undertaken through the use of available materials, surgical technology, and modifications of related existing model simulators. Simulation of common, standard surgical procedures for early learners may be broken into individual surgical skills and maneuvers to prepare trainees for safe practice of these component skills during live procedures under direct supervision appropriate to their training stage. CONCLUSION: Model-based simulation is particularly effective for early surgical learners as part of a coordinated curriculum. Almost 600 residents have used model-based simulation during the first 3 years of the Society of Neurological Surgeons boot camp courses, with ongoing modification and improvement of individual simulation models.
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