The American College of Surgeons (ACS) developed the Quality In-Training Initiative (QITI) to link surgeon trainee participation with patient outcomes within ACS National Surgical Quality Improvement Program (NSQIP). As surgical education shifts toward competency-based and outcomes-informed training, QITI may offer a platform not only for data collection, but also for resident learning and preparation for practice. We evaluated QITI as a system for collecting resident-linked clinical outcomes, supporting meaningful clinical education, and preparing trainees for data-driven surgical practice. This is a retrospective study of NSQIP cases with completed QITI fields from July 2013 through July 2025. Descriptive analyses evaluated case volume, participation across institutions, operative case mix, and postoperative outcomes by PGY level. Multivariable regression models were used to assess the association between PGY level and postoperative outcomes after adjustment for standard NSQIP preoperative risk factors. From 190 participating institutions, 1,412,068 cases met inclusion criteria, including 1,098,264 PGY1–5 cases. While operative case mix increased in complexity across PGY levels, some procedures such as laparoscopic appendectomy and cholecystectomy remained common at all levels. Unadjusted complication rates increased stepwise with PGY level (all p < 0.001). After risk adjustment, most differences attenuated, although cases involving PGY5 residents remained significantly associated with higher odds of select outcomes, including intubation, prolonged ventilation, renal complications, cardiac complications, readmission, morbidity, and mortality. Sensitivity analysis performed for years 2022–2024 showed fewer significant associations. These findings suggest that QITI is a feasible and sustainable system for collecting resident-linked clinical outcomes at national scale. The data provide meaningful educational value by characterizing progression in operative complexity and linking cases with outcomes. In addition, risk-adjusted comparisons and benchmarking reflect the types of outcome reports surgeons encounter in practice, creating an opportunity to expose residents to the types of data-driven quality reports they may encounter in independent practice. QITI may therefore serve as a valuable platform for competency-based surgical education and future trainee feedback systems.
General surgery residency positions are disproportionally concentrated in urban centers, potentially limiting resident case exposure and operative preparedness. As the national per capita supply of general surgeons declines, rural regions have a disproportionately older workforce with insufficient replacement. Residency programs with rural training exposure improves rural retention yet recent trends in regional allocation of the general surgery resident workforce relative to population growth remains poorly defined. Here, we perform a retrospective review to investigate whether regional differences in general surgery resident workforce allocation align with population changes across U.S. regions. We performed a retrospective review of publicly available data from the National Resident Matching Program and the United States Census Bureau spanning 2013–2024. General surgery resident workforce is approximated by entering residency positions per capita. Regions are defined according to U.S. Census Bureau divisions. The strength and direction of a linear relationship between percentage change in entering general surgery residency positions per capita and percentage change in population from 2013 to 2024 was assessed by Pearson Correlation Coefficient. Differences in these percentage changes were assessed by paired t-tests. The national mean percentage change in entering residency positions per capita and population from 2013 to 2024 was 1.35
With the growing incorporation of digitally mediated learning in higher education, the effectiveness of instructional approaches in surgical training has become a key issue in veterinary education. This debate has intensified in Brazil following recent regulatory changes that allow Veterinary Medicine programs to be offered in a semipresential format, potentially permitting up to 60
Successful completion of the Fundamentals of Laparoscopic Surgery (FLS) is a prerequisite for the American Board of Surgery Qualifying Exam. Residents preparing for FLS rely on faculty observation for feedback, which can limit frequency and objectivity. In this study, we developed and validated a computer vision-based artificial intelligence (AI) model to autonomously evaluate performance on the laparoscopic peg transfer task. General surgery residents and medical students at an academic medical center were recorded performing the FLS peg transfer task. Videos were independently scored by two adjudicators as beginner, intermediate, or expert based on task duration and perceived technical performance quality. A computer vision pipeline was constructed and trained under five-fold cross-validation to distinguish skill levels using task duration, instrument path length, and peg displacement as input features. Of 132 total recorded videos, 100 were used for analysis while 32 were excluded due to tracking dropout. The model achieved an overall classification accuracy of 84
