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[192] Citation: Abstract
Inherent difficulties in evaluating clinical competence of
physicians has lead to the widespread use of subjective skill assessment
techniques. Inspired by an analogy between medical procedure and spoken
language, proven modeling methods in the field of speech recognition were
adapted for use as objective skill assessment techniques. A generalized
methodology using Markov Models (MM) was developed. The database under study
was collected with the E-Pelvis physical simulator. The simulator
incorporates an array of five contact force sensors located in key
anatomical landmarks. Two 32-state fully connected MMs are used, one for
each skill level. Each state in the model corresponds to one of the possible
combinations of the 5 active contact force sensors distributed in the
simulator. Statistical distances measured between models representing
subjects with different skill levels are sensitive enough to provide an
objective measure of medical skill level. The method was tested with 41
expert subjects and 41 novice subjects in addition to the 30 subjects used
for training the MM. Of the 82 subjects, 76 were classified correctly (92%).
Moreover, unique state transitions as well as force magnitudes for
corresponding states (expert/novice) were found to be skill dependent. Given
the white box nature of the model, analyzing the MMs provides insight into
the examination process performed. This methodology is independent of the
modality under study. It was previously used to assess surgical skill in a
minimally invasive surgical setup using the Blue DRAGON, and it is currently
applied to data collected using the E-Pelvis.
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Updated: Tue Jul 15 23:54:51 2008
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