Preiksaitis Carl, Abubshait Layla, Bowers Kaitlin, Landry Adaira, Lewis Kristin, Little Andrew G, Nash Christopher J, Gottlieb Michael
Department of Emergency Medicine Stanford School of Medicine Stanford California USA.
Department of Emergency Medicine Jefferson Einstein Montgomery Hospital East Norrington Pennsylvania USA.
AEM Educ Train. 2024 Aug 11;8(4):e11013. doi: 10.1002/aet2.11013. eCollection 2024 Aug.
The emergency medicine (EM) landscape has evolved due to the increasing number of programs paired with fewer applicants. This study analyzed the characteristics of EM residency programs associated with unfilled positions during the 2024 Match and compared them with data from the 2023 Match to identify persistent and emerging trends influencing these outcomes.
In this cross-sectional, observational study, we investigated factors associated with unfilled EM residency positions in the 2024 Match. We used publicly accessible data from the National Resident Matching Program. To identify program-level predictors of unfilled positions, we constructed a Bayesian hierarchical logistic regression model, incorporating data from the 2023 Match season.
In 2024, 54 out of 281 (19.2%) residency programs remained unfilled. Our Bayesian analysis reaffirmed that smaller program size, geographical location, prior osteopathic accreditation, and corporate ownership continue to be significant factors. Programs with vacancies in the previous year were also more likely to remain unfilled. Thus, several factors identified in 2023 remained associated with this year's Match outcomes, with the impact of previous unfilled positions being particularly pronounced.
This study identified several factors associated with a greater likelihood of having unfilled EM residency positions, with previous unfilled positions emerging as the most significant predictor. These findings offer critical insights for residency programs and governing bodies, providing a basis for enhancing recruitment strategies, addressing the cyclical nature of unfilled positions, and tackling workforce challenges in EM.
由于急诊医学（EM）项目数量增加而申请者数量减少，急诊医学领域已发生演变。本研究分析了与2024年住院医师匹配中未填补职位相关的急诊医学住院医师项目的特征，并将其与2023年住院医师匹配的数据进行比较，以确定影响这些结果的持续和新出现的趋势。
在这项横断面观察性研究中，我们调查了与2024年住院医师匹配中未填补的急诊医学住院医师职位相关的因素。我们使用了来自国家住院医师匹配计划的公开数据。为了确定未填补职位的项目层面预测因素，我们构建了一个贝叶斯分层逻辑回归模型，纳入了2023年匹配季的数据。
2024年，281个住院医师项目中有54个（19.2%）未填补。我们的贝叶斯分析再次证实，项目规模较小、地理位置、先前的整骨疗法认证和企业所有权仍然是重要因素。前一年有空缺的项目也更有可能未被填补。因此，2023年确定的几个因素仍然与今年的匹配结果相关，先前未填补职位的影响尤为明显。
本研究确定了几个与急诊医学住院医师职位未填补可能性较大相关的因素，其中先前未填补的职位是最显著的预测因素。这些发现为住院医师项目和管理机构提供了关键见解，为加强招聘策略、应对未填补职位的周期性以及解决急诊医学领域的劳动力挑战提供了依据。
