Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America.
Computational Biology and Bioinformatics, Yale School of Medicine, New Haven, Connecticut, United States of America.
PLoS One. 2023 Feb 1;18(2):e0280251. doi: 10.1371/journal.pone.0280251. eCollection 2023.
Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model's prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions.
医生离职给医疗保健行业、患者、医生及其家庭带来了沉重的负担。建立一种识别有离职风险的医生的机制，可以帮助确定适当的干预措施，以防止离职。我们从一个大型的非教学医生的门诊实践中收集了医生特征、电子病历（EHR）使用模式和临床生产力数据，以建立一个预测模型。我们使用了几种技术来识别可能的干预变量。具体来说，我们使用梯度提升树来预测医生在 6 个月内离职的概率。一些变量对预测医生离职有显著贡献，包括任期（自雇佣日期起的时间）、患者群体的复杂性、医生需求、医生年龄、收件箱和文档时间。这些变量是通过训练、验证和测试模型来确定的，然后计算 SHAP（SHapley Additive exPlanation）值，以调查哪些变量对模型的预测影响最大。我们发现这些重要变量与其他变量有很大的相互作用，表明它们的重要性。由于这些变量可能是医生离职的预测因素，因此它们可能有助于识别有风险的医生，以便对他们进行有针对性的干预。
