Perceptual Expertise: How Is It Achieved?

Transforming Surgical Education through a Resident Robotic Curriculum

Objective

Here, we describe a systematic approach to design, implement, and assess a robotic surgery curriculum for surgical residents. By describing our process, including identifying and addressing institutional challenges, we illustrate successful development of a robust curriculum.

Summary Background Data

As robotic-assisted surgeries increase, educational challenges have emerged and illustrate an alarming impact on medical training. Robotic curricula are frequently grounded in the industry's educational materials resulting in a variety of existing resident curricula that lack cognitive components and critical evaluation. As such, surgical educators struggle to identify the curricular restructuring needs that likely accompany emerging technologies. It is essential to develop a curricular framework for the surgical education community to approach the ongoing and inevitable integration of new technologies.

Methods

Our process parallels the widely accepted approach to curricular development in medical education described by Kern et al. Using this 6-step model, we describe derivation of a curriculum that was data driven, features multimodal educational strategies, and provides documentation methods that allow for continued evaluation and assessment at the individual and departmental level.

Results

This study highlights the systematic process of design, implementation and assessment of a robotic surgery curriculum for surgical residents. Built on a robust national and local needs assessment, and further strengthened by preemptive identification of institutional challenges, this curricular model includes a structured documentation system that allows for ongoing evaluation, assessment, and monitoring of curricular progress.

Conclusions

We illustrate a robustly built curricular structure that can be adopted, adapted, and successfully implemented at other training institutions around the world.

The effect of expertise, target usefulness and image structure on visual search

Experts outperform novices on many cognitive and perceptual tasks. Extensive training has tuned experts to the most relevant information in their specific domain, allowing them to make decisions quickly and accurately. We compared a group of fingerprint examiners to a group of novices on their ability to search for information in fingerprints across two experiments-one where participants searched for target features within a single fingerprint and another where they searched for points of difference between two fingerprints. In both experiments, we also varied how useful the target feature was and whether participants searched for these targets in a typical fingerprint or one that had been scrambled. Experts more efficiently located targets when searching for them in intact but not scrambled fingerprints. In Experiment 1, we also found that experts more efficiently located target features classified as more useful compared to novices, but this expert-novice difference was not present when the target feature was classified as less useful. The usefulness of the target may therefore have influenced the search strategies that participants used, and the visual search advantages that experts display appear to depend on their vast experience with visual regularity in fingerprints. These results align with a domain-specific account of expertise and suggest that perceptual training ought to involve learning to attend to task-critical features.

What do radiologists look for? Advances and limitations of perceptual learning in radiologic search

Supported by guidance from training during residency programs, radiologists learn clinically relevant visual features by viewing thousands of medical images. Yet the precise visual features that expert radiologists use in their clinical practice remain unknown. Identifying such features would allow the development of perceptual learning training methods targeted to the optimization of radiology training and the reduction of medical error. Here we review attempts to bridge current gaps in understanding with a focus on computational saliency models that characterize and predict gaze behavior in radiologists. There have been great strides toward the accurate prediction of relevant medical information within images, thereby facilitating the development of novel computer-aided detection and diagnostic tools. In some cases, computational models have achieved equivalent sensitivity to that of radiologists, suggesting that we may be close to identifying the underlying visual representations that radiologists use. However, because the relevant bottom-up features vary across task context and imaging modalities, it will also be necessary to identify relevant top-down factors before perceptual expertise in radiology can be fully understood. Progress along these dimensions will improve the tools available for educating new generations of radiologists, and aid in the detection of medically relevant information, ultimately improving patient health.