Impact of Virtual Interviewing on Geographic Placement for Cardiology Fellowship Recruitment.
Usman A HasnieAmmar A HasnieCarlos A EstradaGaby WeissmanWinter L WilliamsSteven G LloydPublished in: Journal of the American Heart Association (2022)
Background Virtual interviewing for cardiology fellowship was instituted in the 2021 fellowship application cycle because of the COVID-19 pandemic and restricted travel. The impact on geographic patterns of fellow-training program matching is unknown. This study sought to determine if there was a difference in geographic placement of matched fellows for cardiology fellowship match after initiation of virtual interviews compared with in-person interviewing. Methods and Results All US-based accredited cardiovascular disease fellowship programs that participated in the 2019 to 2021 fellowship match cycles and had publicly available data with fellowship and residency training locations and training year were included. Each fellow was categorized based on whether their fellowship and residency programs were in the same institution, same state, same US census region, or different census region. Categories were mutually exclusive. Of 236 eligible programs, 118 (50%) programs were identified, composed of 1787 matched fellows. Compared with the previrtual cohort (n=1178 matched fellows), there was no difference in the geographic placement during the 2021 virtual cycle (n=609 matched fellows) ( P =0.19), including the proportion matched at the same program (30.6% versus 31.5%), same state but different program (13% versus 13.8%), same region but different state (24.2% versus 19.7%), or different region (35% versus 33.1%). There was also no difference when stratified by program size or geographic region. Conclusions The use of virtual interviewing in the 2021 cardiology fellowship application cycle showed no significant difference in the geographic placement of matched fellows compared with in-person interviewing. Further study is needed to evaluate the impact of virtual interviewing and optimize its use in fellowship recruitment.
Keyphrases
- cardiovascular disease
- public health
- cardiac surgery
- quality improvement
- type diabetes
- ultrasound guided
- machine learning
- metabolic syndrome
- coronary artery disease
- acute kidney injury
- deep learning
- electronic health record
- thoracic surgery
- artificial intelligence
- big data
- medical students
- cardiovascular risk factors