Image by Gerd Altmann from Pixabay
Teaching surgical residents has been a practice that is relatively unchanged over the course of decades and even, perhaps, centuries. Instruction by observation and eventual supervised hands-on practice forms the basis of the transfer of knowledge and skill between an experienced surgeon and a beginner. These practices have produced competent surgeons who, ideally, perfect their techniques and approaches over the years.
Training is ultimately limited by the perceptual limits of what a resident can observe and how a surgeon can critique the hands-on efforts of residents. The operating room setting also places some limits on what can be fully taken in by residents. Pressure, speed, and the involvement of a full surgical team create challenges and limitations for pure observation and learning. In addition, other factors have impacted the teaching and learning of surgical skills. In an article from the Journal of the Society of Laparoendoscopic Surgeons, "Teaching and Training Surgery to the Next Generation of Surgeons," Dr. Michael Kavic outlines a reduction in resident hours, lack of mentor availability particularly from private practitioners, and the breadth and complexity of surgical procedures and their nuances.
Learning based on observation and sight is limited to understanding courser motor skills. Nuanced, fine skills are hard to teach or learn other than from personal trial. Additionally, as pointed out in an article in MedEd Publish, an official AMEE journal, "Assessing Surgical Residents; Challenges and Future Options," these methods lack detailed and comprehensive assessment mechanisms to help perfect learned practices.
Now, this mode of instruction and tutelage is on the verge of substantial change. Through the use of new microsurgical robots, the precise hand movements of surgeons can be captured, stored, analyzed, and refined to provide highly effective real-time guidance for surgeries and a level of skill transference not otherwise possible. Now learning can no longer be limited by what the eye can take in. Actual muscle memory of skilled surgeons can be effectively transferred and assimilated by residents.
Robots have the capacity not to just observe but to also feel and experience or participate in surgical procedures with fidelity not otherwise possible. Taking advantage of robots for data capture for purposes of instruction and guidance has not materialized to date. Most of the benefits of surgical robots have dwelled on their augmentative capacity to surgeons and their ability to prevent or minimize the effects of hand tremor. Both values are exceedingly important, but these robots can also be used for so much more.
The challenges to robotic data capture and use has involved four factors. The first involves specific mechanisms for capturing hand movements while also conveying the context of such positional data and motions and attaching meaning to them. The second has been in the transmission of such data. Each procedure potentially generates vast amounts of data and poses a challenge of how to transmit it to some kind of centralized storage. The third involves the economics, capacity, and scalability of such storage. The fourth involves turning the data into useful information through the use of analytics and, potentially, the application of machine learning or artificial intelligence.
High-tech Silicon Valley has essentially solved the issues of transmission, storage, and analytics as exemplified in so many different markets and applications. Big data, cloud infrastructure, and high-speed, high-capacity networks make it feasible to gather large volumes of data, send it to cloud-based data centers, and apply analytics and artificial intelligence to derive meaningful, productive information. Marrying these advances with new advances in surgical robotics produces a system that offers tremendous possibilities.
Now, the training of surgical residents can take on a powerful, new dimension that will counter some of the challenges identified with instruction and provide a transfer of knowledge and know-how in ways not previously possible. In addition, through offering “best-of,” proven assistance or suggestive guidance, surgical robots can help surgeons in real-time perfect their practices, avoid mistakes, and incorporate the know-how of top-tier, experienced surgeons.
As this possibility comes closer to reality, the future of training residents as well as ensuring continual improvement for practicing surgeons seems especially bright.