Lee Yi-Lien, Chou Willy, Chien Tsair-Wei, Chou Po-Hsin, Yeh Yu-Tsen, Lee Huan-Fang
Department of Medical Affairs, Chi Mei Medical Center, Tainan, Taiwan.
Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chayi, Taiwan.
JMIR Med Inform. 2020 May 7;8(5):e16528. doi: 10.2196/16528.
Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute's well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature.
The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage.
We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO-, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model's 38 estimated parameters for a website assessment.
We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model's predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study.
The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.
职业倦怠（BO）是一种严重的综合征，对医疗环境中的护士影响尤为显著，它会极大地影响护士的身心健康、机构的福祉，还会间接影响患者的治疗结果。然而，在文献中尚未对BO水平进行客观分类并引起关注。
本研究旨在构建一个使用卷积神经网络（CNN）的模型，开发一款应用程序，利用马氏职业倦怠量表-人类服务调查（MBI-HSS）对护士的职业倦怠进行自动检测和分类，以帮助在早期阶段评估护士的职业倦怠。
2016年8月，我们招募了1002名在台湾一家医疗中心工作的护士，让他们完成中文版的20项MBI-HSS。k均值法和CNN分别作为无监督和有监督学习方法，将护士分为两类（可疑职业倦怠阳性组n = 531和职业倦怠阴性组n = 471），并建立职业倦怠预测模型以估计38个参数。数据按70%:30%的比例分为训练集和测试集，前者用于预测后者。我们计算了各项研究的敏感性、特异性和受试者工作特征曲线（曲线下面积）进行比较。开发了一个预测受访者职业倦怠的应用程序，该程序涉及模型的38个估计参数，用于网站评估。
我们观察到：（1）基于1002个案例，20项模型的准确率较高（0.95），曲线下面积为0.97（95%CI 0.94 - 0.95）；（2）名为匹配个人反应以适应模型中正确分类的方案使先前模型的预测准确率达到100%；（3）700个案例的训练集准确率为0.96，预测302个案例的测试集准确率达到0.91；（4）在本研究中成功开发并展示了一款可供护士使用的预测职业倦怠的MBI-HSS应用程序。
在Excel（微软公司）中特别展示了使用CNN估计38个参数的20项模型，该模型提高了护士职业倦怠评估的准确性。未来需要开发一款应用程序，帮助护士在早期阶段自我评估工作中的职业倦怠。
