The Future of Basic Science in Academic Surgery

Identifying Barriers to Success for Surgeon-Scientists

Sundeep G. Keswani, MD; Chad M. Moles, BSPH; Michael Morowitz, MD; Herbert Zeh, MD; John S. Kuo, MD, PhD; Matthew H. Levine, MD, PhD; Lily S. Cheng, MD; David J. Hackam, MD, PhD; Nita Ahuja, MD; Allan M. Goldstein, MD


Annals of Surgery. 2017;265(6):1053-1059. 

In This Article


Data from Blue Ridge Institute for Medical Research were gathered regarding NIH funding at surgical departments in the top 25 funded institutions from 2006 to 2014. Additionally, the number of total and basic science abstracts submitted to the Academic Surgical Congress (ASC) annual meeting was determined for 2011 to 2015.

Survey and Sample Population

A questionnaire was developed to obtain demographic data, assess perceptions regarding challenges facing surgeon-scientists, and address potential barriers to research productivity. Survey topics included the funding environment, perceptions about basic research, external factors impacting ability to pursue research, training, mentorship, and protected time. The questions were developed by a group of 5 active and experienced surgeon-scientists, and validated for clarity, content, and adequacy through a presurvey test on a group of 8 surgeon-scientists representing each of the 4 subgroups (trainees, junior faculty, senior faculty, and chairman/division chiefs). The survey was distributed via e-mail on January 30, 2015 to 2504 members of the Association of Academic Surgery (AAS) and the Society of University Surgeons (SUS). Data were collected anonymously through SurveyMonkey until April, 2015. The expected response rate was approximately 50% based on past surveys. However, we recognized that there is no scientifically proven minimally acceptable response rate, and the surveys were more focused on obtaining a representative sample. Identifiable information was omitted to ensure anonymity. Seniority of respondents was categorized as follows: trainee (medical student, resident, fellow), junior faculty (1–5 years post-training), senior faculty (>5 years post-training), and division chief or department chair.

Statistical Analysis

Filter and compare rules were used to identify subsets of the population. Filter rules allow data analysis for a group based on defined criteria. Compare rules are a joint distribution between 2 discrete variables, for example, participants conducting basic versus clinical research. SurveyMonkey has a feature that determines statistically significant differences between response groups when the compare rule is applied to a survey question. Raw data were exported Microsoft Excel, and Student t test and N-1 Two Proportion test were used for statistical analysis, with significance defined as P < 0.05. Trainees were excluded, unless specified.