Modeling Learning in Surgical Practice

Publication date: Available online 30 June 2017
Source:Journal of Surgical Education
Author(s): Epaminondas Markos Valsamis, Rajna Golubic, Thomas E. Glover, Henry Husband, Adnan Hussain, Amir-Reza Jenabzadeh
ObjectiveMethods that model surgical learning curves are frequently descriptive and lack the mathematical rigor required to extract robust, meaningful, and quantitative information. We aimed to formulate a method to model learning that is tailored to dealing with the high variability seen in surgical data and can readily extract important quantitative information such as learning rate, length of learning, and learnt level of performance.MethodsWe developed a method where progressively more complex models are fitted to learning data. These include novel models that split the learning data into 2 linear phases and fit adjoining lines using least squares regression. The models were compared and the least complex model was selected unless a more complex one was significantly better. Significance was tested by Fischer tests. We applied this method to total hip and knee replacements using imageless navigation, analyzing the operative time for a surgeon׳s first 50 and 60 operations, respectively. This method was then tested against 4 sets of simulated learning data.ResultsThe proposed method of progressive model complexity successfully modeled the learning curve among real operative data. It was also effective in reducing the underlying trends in simulated scenarios, created to represent atypical situations that can practically arise in any learning process.ConclusionsThe novel modeling method can be used to extract meaningful and quantitative information from learning data displaying high variability seen in surgical practice. By using simple and intuitive models, the method is accessible to researchers and educators without the need for specialist statistical knowledge.



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