Predicting physicians' satisfaction with electronic medical records using artificial neural network modeling

Abstract

Background: Studies involving the measurement of physicians' satisfaction with electronic medical records (EMRs) using a branch of artificial intelligence known as machine learning is rare in Saudi Arabia. Most of the studies have relied on traditional statistical methods. This study focuses on comparing an artificial neural network (ANN) model with linear regression model to predict physicians' satisfaction with their EMR. Aims: This study aims to compare the performance of ANN versus logistic regression (LR) modeling in predicting physician satisfaction with their institution's EMRs and compare sensitivity analysis results for both models. Methodology: Data were collected through a self-administered survey that was distributed to physicians working in inpatient departments at a major Saudi Arabian hospital (360-bed capacity). Using machine learning software, ANN and LR models were developed to compare performance power and identifying factors affecting physician's satisfaction through running sensitivity analyses. Results: The analysis included 115 physicians who answered the survey. ANN model produced a more accurate prediction when compared to the prediction produced by LR. ANN correctly classified the instances with 86.09% accuracy, compared to LR, which achieved 82.61% accuracy. In addition, sensitivity was higher in ANN model (0.86) compared to LR model (0.83). Specificity was lower in ANN (0.39) compared to the LR model (0.44), and the receiver operating characteristic curve was higher (0.79) for ANN, (0.76) for LR. ANN model identified three factors affecting physician's satisfaction: System integration with workflow; system features to enable physicians to perform their work well; and training. Conclusion: The results show the ANN model performed better than LR due to its nonlinear characteristics and discovered three new factors affecting physician's satisfaction. Therefore, ANN model should be used in physician's satisfaction prediction studies.