Plenary Session II
(S007) HOW WE TALK AND TEACH IN THE OPERATING ROOM: USING LIVE OPERATIVE RECORDINGS TO EVALUATE RESIDENT AUTONOMY
Katharine E Caldwell, MD, MSCI, Blake T Beneville, MD, Jenna Bennett, Mohamed A Jama, Cory Fox, Mike Ferzoco, Lauren Lewis, Jonathan Tong, Michael A Awad, MD, PhD, MHPE; Washington University in Saint Louis
Introduction
Autonomy and feedback are essential to surgical training, yet programs must rely on brief end-of-case ratings, rather than direct analysis of what faculty and trainees actually say in the operating room. Trainees report wanting more specific, real-time feedback, and faculty lack objective measures of how much guidance they provide. We asked whether patterns of live operative dialogue map to resident autonomy on the Zwisch scale and could inform entrustability and coaching.
Methods
We audio-recorded attending-trainee interactions during 25 general surgery operations (cholecystectomy, hernia repair, colectomy; open, laparoscopic, robotic). Recordings were de-identified, transcribed, and corrected. A codebook labeled five categories (technical instruction, instrument requests, shared mental modelling (verbalizing anatomy/next step/strategy), feedback (positive or constructive), and off-target talk (non-procedural conversation). After each case, attendings rated resident autonomy by Zwisch scale (Show and Tell, Active Help, Passive Help, Supervision Only). We analyzed lower-autonomy cases (Show and Tell/Active Help) versus higher-autonomy cases (Passive Help/Supervision Only). Coding was supported by automated tools and verified by investigators.
Results
More autonomous learners contributed more intraoperative speech. (33.4% v 13%, p=0.03), approached attending levels in shared mental modelling (47.4% v 16.9%, p=0.01), initiated more instrument request (49.3% v 16.4%, p=0.03), and required fewer explicit technical instruction events per case (p=0.03). Total feedback statements per case were similar (p=0.14), but attendings in higher-autonomy cases delivered more positive feedback (70.0% v 35.9%, p=0.05). Dyads in higher-autonomy cases also showed more off-target talking (45.1% v 11.5%, p=0.04), consistent with decreased need for continuous coaching. After training, the AI model was able to correctly identify resident autonomy levels by Zwisch rating with 90.9% accuracy to attending ratings.
Conclusions
Distinct, quantifiable speech signatures mark higher Zwisch autonomy: less step-by-step technical direction, more resident-initiated control, more forward-looking shared mental modeling, and proportionally more positive feedback. Capturing OR dialogue provides a scalable, behavior-based supplement to Zwisch and EPA ratings, supports targeted faculty coaching, and enables structured post-case resident feedback, linking what is said in the OR to decisions about entrustment, graduated responsibility, and practice readiness.
(S008) FROM COGNITIVE OVERLOAD TO COGNITIVE RESILIENCE: IMPACT OF IMPOSTER SYNDROME IN THE OPERATING ROOM
Tasha Posid, MA, PhD1, Kyle Elko1, Kimberly M Hendershot, MD2, Cali E Johnson, MD, EdD3, Amanda B Cooper, MD4; 1The Ohio State University Wexner Medical Center, 2University of Alabama-Birmingham, 3University of Utah Health, 4Penn State College of Medicine
Introduction: Transitioning from trainee to independent surgeon requires rapid adaptation to new responsibilities in patient care, efficiency, and teaching. Many early-career faculty report persistent feelings of imposter syndrome, which may intensify cognitive (over)load during operative procedures. This study explores how imposter syndrome may interact with cognitive overload in the operating room (OR) and may amplify the cognitive strain among junior surgical faculty.
Methods: Semi-structured interviews were conducted one-on-one via Zoom with junior faculty (<6 years post-training; n=14) across surgical disciplines until thematic saturation was reached. Participants reflected on their preparedness for independent practice and leadership in the operating room, experiences with cognitive overload, and impact of additional factors such as imposter syndrome. Interviews were recorded via Zoom and transcripts were analyzed using inductive thematic analysis, with iterative coding to identify themes and sub-themes.
Results: 100% of junior faculty reported experiencing cognitive overload in the operating room and 13/14 (93%) reported feelings of imposter syndrome as well. 9/14 (64%) further stated that their imposter syndrome affected their ability to teach in the OR. Several key themes emerged from thematic analysis (Figure 1): (1) Persistent self-doubt characterized the early faculty experience, as junior faculty questioned their competence despite adequate training. This led to a (2) cognitive amplifier effect, or heightened attentional demands and fragmented focus, particularly during complex or time-pressured cases. (3) Faculty reported that this often impacted their teaching, including decreased instructional bandwidth and confidence when anxiety was elevated. (4) Junior faculty expressed that early in their career, they engaged in adaptive coping, relying on experience to recalibrate their confidence. (5) Mentorship and help from senior colleagues and team members normalized early uncertainty, supporting the development of cognitive resilience in the OR.
Conclusions: Imposter syndrome significantly contributes to cognitive overload among junior surgeons in the operating room by increasing anxiety and self-monitoring, with downstream effects such as reduced bandwidth to teach learners. Although experience mitigates this effect, structured education or interventions such as peer mentorship, debriefing or curricula during training, and broader cognitive load awareness training could help early-career faculty recalibrate their ability to handle cognitive overload in the operating room.
(S009) MULTI-INSTITUTIONAL REVIEW OF THE EFFICACY OF BESST LEADERSHIP COURSE IN TEACHING LEADERSHIP COMPETENCIES
Clare Lipscombe, MD1, Alejandro Chara1, Emily Witt2, Michael Kochis2, Alyssa Pradarelli3, Irene Zhang4, Colleen Kiernan1, Robert Sinyard, MD1; 1Vanderbilt University Medical Center, 2Masachusetts General Hospital, 3University of Michigan, 4University of Washington
Introduction
Becoming Effective Senior Surgical Trainees (BESST), a previously described leadership development curriculum currently undergoing evaluation via a multi-institutional multi-year trial, may offer insight into how surgical trainees are conceptualizing their non-technical skill development. The purpose of this study was to analyze the written goals set by trainees participating in the program to inform curricular development and trainee evaluation strategies.
Methods
Four participating institutions were invited to contribute post-course data from trainees who had completed the four sessions of the BESST curriculum between 2022-2025. Post-course feedback and goals were captured via anonymous REDCap survey. Qualitative responses were analyzed inductively using thematic analysis.
Results
Three institutions provided post-course survey results for a total of 42 participants, 39 (93%) of whom had established explicit goals for their non-technical and leadership skill development by the conclusion of the course. Four dominant themes were identified among the written goals: building a team culture characterized by truthful communication, normalizing failure, ‘getting in the trenches’ with junior residents, and setting explicit performance expectations. 38 (90%) participants considered the course a good use of their protected didactic time though 17 (40%) still expressed considerable anxiety about their abilities to appropriately lead teams as rising senior residents.
Conclusion
Surgical trainees transitioning to senior roles perceive dedicated time for non-technical skill development as valuable and most participants utilized course themes to set meaningful goals for their performance as team leaders. These findings should guide educators developing further curricula to train and evaluate senior surgical trainees regarding performance in these domains.
(S010) GENERATING AN ACGME-COMPLIANT SURGERY RESIDENCY CALL SCHEDULE SOFTWARE WITH THE ASSISTANCE OF AGENTIC CODING LARGE LANGUAGE MODELS
Isaac H Lies, MD, Meredith Rippy, BS, Syamal D Bhattacharya, MD, Wesley H Giles, MD; Department of Surgery, University of Tennessee, College of Medicine at Chattanooga, Chattanooga, TN, USA.
Background: Manual creation of a surgery residency call schedule is a labor-intensive process that adds to the administrative burden of a chief resident while often producing suboptimal fairness and work distribution. While commercial scheduling software exists, many programs struggle with inflexibility, high costs, and inability to accommodate program-specific constraints.
Methods: We developed a scheduling software platform using AI-assisted development tools (Codex, Claude Code). The system implements an algorithm with exhaustive enumeration of weekend permutations followed by iterative weekday assignment. The system is capable of tolerating vacation requests and discrete call shift swapping. A pool of fictionalized residents modeled a local general surgery residency program's call scheduling requirements. ACME compliance standards (4 days off in 28 day periods, less than 1 in 3 call shifts, and no consecutive 24-hour call) were observed. We validated the system with 100 independent schedule generation runs incorporating realistic assignments across a full academic year (July 1 - June 30).
Results: Across 100 validation runs, 100% of generated schedules achieved full ACGME compliance with no violations detected by the system's internal validation engine and independent manual review. For monthly fairness metrics, we evaluated the monthly range, defined as the difference between the maximum and minimum number of call shifts or weekends assigned to residents within the same call pool. Across all outputs, 40.3% of months achieved a range of less than or equal to 1 shift, with an overall mean range of 1.91 shifts (SD = 1.41). For monthly weekend distribution, 51.0% of outputs achieved perfect equity (range = 0 weekends), and 96.2% achieved a range of less than or equal to 1 weekend. Each full-year schedule generation required a mean processing time of 48.65 seconds (SD = 0.69). Annual workload metrics demonstrated balanced distribution, with residents averaging 21.07 weekends (SD = 3.31) and 83.93 call days per year (SD = 6.90).
Conclusions: This automated scheduling system generates ACGME-compliant resident call schedules while optimizing fairness across multiple dimensions. The approach demonstrates that optimization algorithms combined with careful constraint specification can eliminate the administrative burden of manual scheduling while improving schedule quality and equity.
(S011) ARTIFICIAL INTELLIGENCE-ENABLED EVALUATION OF LAPAROSCOPIC PEG TRANSFER PERFORMANCE
Terrance Peng, MD, MPH, Armin Alipour, MS, Derek Chen, Grace Huang, Raul J Rosenthal, MD, Yijun Chen, MD, Peyman Benharash, MD; UCLA
INTRODUCTION: Successful completion of the Fundamentals of Laparoscopic Surgery (FLS) is a prerequisite for the American Board of Surgery Qualifying Exam and remains a cornerstone of general surgery residency training in the United States. Current evaluation for FLS relies heavily on faculty observation, which can limit the frequency and objectivity of feedback. We aimed to develop and validate a computer vision-based AI model to autonomously evaluate performance and score the laparoscopic PEG transfer task.
METHODS: General surgery residents and medical students at an academic medical center were recorded performing the FLS PEG transfer task. Using a rubric accounting for instrument handling, economy of motion, and efficiency, videos were independently scored by two adjudicators into three skill levels: Beginner, intermediate, and expert. A computer vision pipeline was constructed using the You Only Look Once (YOLO) algorithm for object detection and ByteTrack for instrument tracking. Raw trajectory coordinates were processed through feature engineering to generate motion-based metrics reflecting technical precision and fluidity, including total path length, jitter, dwell time, and efficiency. These features were used to train machine learning classifiers for automated skill classification.
RESULTS: A total of 157 videos of the laparoscopic PEG transfer task were recorded, of which 110 were used to develop the model. The computer vision pipeline achieved 85-95% detection confidence and 87-95% tracking uptime in high-quality recordings. All classifiers demonstrated strong discriminative performance, accurately distinguishing skill levels. Random Forest achieved the highest area under the curve (AUC = 0.951), followed by Support Vector Machines (0.936), Gradient Boosting (0.926), and Logistic Regression (0.912), for an overall mean AUC of 0.930 (Figure 1). The superior performance of ensemble methods suggested that non-linear feature interactions were critical for accurate skill classification.
CONCLUSION: Our AI-driven computer vision model accurately tracked instrument motion and reliably distinguished skill levels during the FLS PEG transfer task. This model may offer a scalable alternative to traditional expert-based evaluation with potential to provide instant feedback without additional faculty burden, thereby promoting more efficient and equitable technical skill development across training programs.
(S012) FACULTY PERSPECTIVES ON PERSONAL AND INSTITUTIONAL BARRIERS TO EDUCATION:
“I APPARENTLY STRESS PEOPLE OUT IN A VERY UNINTENTIONAL WAY”
Jonathan D'Angelo, PhD, MAEd1, Oviya Giri, MBBS1, Aashna Mehta, MD1, Mohamed Baloul, MD1, Mariela Rivera, MD1, Rebecca Busch, MD2, Anne-Lise D'Angelo, MD, MSEd1; 1Mayo Clinic - Rochester, 2University of Wisconsin
Introduction
Significant research has focused on how surgical faculty can enhance their teaching skills, but less on identifying barriers to achieving quality instruction. In fact, no research to our knowledge has examined the degree to which faculty are aware of how their personal behaviors may cause trainees to struggle. The aim of this research was to identify personal and institutional barriers to surgical education.
Methods
A survey was distributed to surgical faculty at three institutions focusing on the learning environment. This analysis considered a series of questions on personal behaviors or institutional barriers that may impede the learning environment (open-ended questions) and demographics. A thematic analysis was conducted on the qualitative responses.
Results
Fifty-six surgeons responded to the survey (52% female; M=9.48, SD=8.07 years in practice).
Fifty surgeons (89%) identified at least one institutional barrier to education (M=1.53±1.33). Most frequently cited barriers were time constraints and workload (63%), followed by trainee continuity and preparedness (16%), institutional culture/pressure (12%), case complexity/acuity (8%), and faculty role strain (2%).
Thirty-two surgeons (57%) identified at least one way in which they may intentionally or unintentionally impede resident learning (M=0.78±0.61). The most frequently cited item was communication failure (38%) (“I do use shame sometimes to get trainees to understand what I expect and that does not always read well”). This was followed by the need to be efficient (22%) (“I focus on efficiency and timeliness, sometimes limiting their experience with uncertainty”), personal characteristics (22%) (“I am relatively quiet and introverted”), one’s own training level (16%) (“I am junior and still like to have a lot of control”), being in a state of exhaustion or high stress (13%) (“By the end of the day I am tired, so may be less engaged”) and setting resident expectations too high (13%) (“High expectations”).
Notably, 46% of surgeons identified resident behaviors that impede learning even though this topic was not prompted.
Conclusion
This research adds to the limited body of work examining institutional barriers to surgical education while newly identifying surgeon self-identified behaviors that may impede resident learning. Future research should consider interventions and systematic changes to reduce these barriers to enhance resident education.