Minimally invasive video‐assisted thyroidectomy: Learning curve in terms of mean operative time and conversion and complication rates
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Abstract Background The purpose of this study was to evaluate the learning curve for minimally invasive video‐assisted thyroidectomy (MIVAT). Methods Fifty consecutive patients were prospectively included in groups corresponding to their surgical order (10 patients in each group). Results The mean operation times between 2 neighboring groups were 33.9 minutes longer in group 1 than in group 2 ( p = .01) and 25.8 minutes longer in group 3 than in group 4 ( p = .002). The conversion rate for technical difficulties in group 1 was 6% (3 of 50). The definitive complication rate was 2% (1 of 50). Conclusions In terms of operative time, 10 patients represented the early stage of the learning curve, and 30 patients represented the number of procedures required to reach an advanced level of skill. Considering the low conversion and complication rates, improved aesthetic results and postoperative course, MIVAT should become a tool in the repertoire of high‐volume thyroid surgeons. © 2012 Wiley Periodicals, Inc. Head Neck, 2013Keywords:
Learning curve
Learning curve is a curve of describing the learning effects of individuals or groups. Learning curve was based on three hypotheses. Learning curve may be applied to not only making plans, but also making competition strategies. Enterprises must pay attention to the influential factors and the requirements of our times and use the learning curve rationally.
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The introduction of new procedures in surgery usually implies the costs of the so called learning curve. However patients should not suffer the negative effects of surgeon's learning curves. We report our results with the first 15 Ross procedures, a single surgeon and institution experience, and how it was possible to build a safe learning curve without major consequences for the patients. We emphasize the need for a continuous program of surgical performance assessment throughout the learning curve period, as was demonstrated in our series.
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As surgical techniques continue to evolve, surgeons will have to integrate new skills into their practice. A learning curve is associated with the integration of any new procedure; therefore, it is important for surgeons who are incorporating a new technique into their practice to understand what the reported learning curve might mean for them and their patients. A learning curve should not be perceived as negative because it can indicate progress; however, surgeons need to understand how to optimize the learning curve to ensure progress with minimal patient risk. It is essential for surgeons who are implementing new procedures or skills to define potential learning curves, examine how a reported learning curve may relate to an individual surgeon's in-practice learning and performance, and suggest methods in which an individual surgeon can modify his or her specific learning curve in order to optimize surgical outcomes and patient safety. A defined personal learning contract may be a practical method for surgeons to proactively manage their individual learning curve and provide evidence of their efforts to safely improve surgical practice.
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There are a number of reasons why production, continually moving down a known learning curve will suddenly retrace its steps by moving up the learning curve. The cause of such a reversal needs to be recognized and the impact determined. Knowing how to compute the degree of “forgetting” is crucial in properly estimating future production times and costs. This paper will address how to effectively determine the impact of such events on the learning curve and how to make changes to future estimates.
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This article explains how to make the learning curve work for you. It covers what the learning curve is, where cost savings may come from, how you can measure your learning curve, and how you can maximize the cost savings you get from the learning curve.
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