Influence of academic inbreeding on the performance of hospital personnel: an empirical study in a Chinese tertiary hospital

Background Academic inbreeding has a dual influence on performance by potentially enhancing collaboration or fostering exclusion. Therefore, we aimed to comprehensively analyze the impact of academic relationships on the research and clinical output of medical personnel in a tertiary hospital in China. Methods This study collected data from 2009 until 2020 from various departments of a tertiary training hospital. Information collected from various departments comprised of baseline characteristics, academic qualifications, research output, and clinical performance metrics. Independent t -test and linear regression were used to compare scientific and medical performance between staff with internal and external supervisors. Multivariate analyses were conducted, which included multiple linear regression on metrics of SCI-indexed publications and a CMI, and binary logistic regression on research project acquisition, using data from hospital personnel. Results We included data from 441 hospital staff members. Multiple linear regression analyses revealed that supervisors from current hospital, higher clinical title, and older age were all significant positive predictors of greater SCI-indexed paper output ( p 0.001). Similarly, these factors significantly increased the odds of securing research projects, with clinical title being the strongest predictor (OR = 7.74, p 0.001). In contrast, academic inbreeding and these predictors showed no significant association with the Case Mix Index (CMI) ( p = 0.32). Furthermore, a higher level of academic degree (e.g., Ph.D.) was uniquely associated with an increased CMI ( β = 0.15, p 0.05). Conclusion There is a clear dichotomy in the effects of academic inbreeding: while it boosts research productivity, it exerts a limited influence on clinical skills. Hospitals should comprehensively consider their actual development needs, establish more refined and scientific criteria for talent selection based on actual developmental needs, optimize resource allocation, and enhance scientific research support and incentive.