An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study

Abstract

Background 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. Objective 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. Methods 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. Results 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. Conclusions 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.

An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study

“…Over 1127 articles have been found using the keyword convolutional neural network [Title] searched in PMC on June 26, 2020 [ 19 ]. None of the articles used MS Excel to perform the CNN.…”

“…In this study, CNN was applied as supervised learning to build an ELMH prediction model to estimate 108 parameters (n=4×(10+17), with 4 sets of 10 parameters for featured maps and 17 parameters for pooled layers) because 4 categories are required in the CNN model ( Figure 1 and Multimedia Appendix 3 ). Detailed information about CNN [ 38 - 40 ] is available in the literature [ 19 , 20 ].…”

“…CNN in Microsoft (MS) Excel [ 19 , 20 ] was performed to estimate model parameters and compute the prediction accuracy rate (1−the number of misclassification/352). Comparisons of prediction accuracy were evaluated using discrimination analysis on (1) factor scores and outfit MNSQ and (2) factor scores alone in each subscale.…”

“…Although numerous studies have been conducted on MI apps [ 9 , 14 - 18 ], few have used the algorithm of artificial neural networks (ANNs) in classifying MI, particularly with convolutional neural networks (CNNs). Traditionally, cutoff scores are often used in the classification of MI [ 19 , 20 ].…”

“…Most of the classifications were based on the extent (or, say, strata) to which MI or other disorders are determined by summation scores or equivalent measures of a scale (eg, patients with a psychotic disorder [ 21 ] or those with 2 or 3 groups of nurse burnouts [or bullied victims] at the workplace [ 19 , 20 ]). To the best of our knowledge, none of the studies have applied the response pattern to classify the features of MI (or other disorders) with CNN modules to highlight less confident cases of MI characterized by the aberrant response pattern that deviated from normal cases.…”