Why we should be sharing our operations: a game theoretic analysis of surgical learning
Soares, A. S.; Chand, M.
Show abstract
IntroductionSurgical training has traditionally relied on the master-apprentice model, emphasizing supervised repetition and immediate feedback within the operating room. With the advent of minimally invasive surgical techniques, the capability to record and digitally store surgical procedures has introduced new opportunities for detailed analysis and enhanced feedback mechanisms. Despite this potential, there is a lack of comprehensive systems to analyse recorded surgical procedures at scale. MethodsIn this study, we propose a cooperative game-theoretic model to examine the dynamics of surgical training, specifically focusing on the interactions between a master surgeon and an apprentice. The model incorporates both internal knowledge growth--stemming from direct collaboration--and external knowledge growth from supplementary educational resources. A characteristic function is proposed to quantify the utility (knowledge) generated by different coalitions of participants. ResultsOur findings demonstrate that collaboration between the master and apprentice leads to a synergistic increase in total knowledge value, surpassing the sum of their individual contributions. The integration of external resources significantly amplifies this effect, showing an exponential impact on knowledge acquisition over time. Proficiency analysis indicates that combining practical experience with structured external learning resources not only accelerates the apprentices progression to proficiency but also enhances the overall knowledge within the surgical community. ConclusionThe study underscores the potential of applying game-theoretic principles to optimize surgical education. By quantifying the influence of mentor-ship quality and external learning resources, we highlight actionable strategies to enhance surgical training outcomes. Embracing a combination of hands-on practice and external resources accelerates individual skill development and enriches the collective knowledge base. We anticipate that this conceptual framework will inform future educational models and encourage the adoption of collaborative and technology-enhanced learning practices in surgery.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Development and Validation of ‘Patient Optimizer’ (POP) Algorithms for Predicting Surgical Risk with Machine Learning 93%
- On the predictability of postoperative complications for cancer patients: a Portuguese cohort study 92%
- Combining symbolic regression with the Cox proportional hazards model improves prediction of heart failure deaths 91%
Similar papers in this journal
- Modeling social distancing strategies to prevent SARS-CoV2 spread in Israel- A Cost-effectiveness analysis 88%
- The roadmap for implementing value based healthcare in European university hospitals - consensus report and recommendations 88%
- Statistical Decision Properties of Imprecise Trials Assessing COVID-19 Drugs 84%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.