Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques

Abstract

Modern radiology departments have enormous databases of images and text. Like any databases, which are rich in data content, but poor in information content. Data Mining is an effective tool that extracts useful information from this enormous database of images and text which helps decision makers in departments and hospitals to take proper decisions. In this research, the idea investigates some problems in radiology departments at hospitals based on applying Data Mining techniques and conducting Data Mining model to improve radiologists productivity by assigning the appropriate cases to appropriate radiologists within tele-radiology environment. Due to the heavy load of work assigned to radiologists, there is significant delay in writing radiology reports by them. Data with seven feature sets were collected from four hospitals in Saudi Arabia covering eight radiologists (two from each hospital) with varying productivity and specialisation with emphasis on CT, MRI and Mammography modalities. Four different classifiers were applied for the dataset to predict and assign the suitable cases for each radiologist to improve radiologists productivity. The model was evaluated by presenting its results to an expert in one of the four hospitals for his opinion. He declared that the results of the model are very good as they take into account the subspecialty of each procedure in assigning the cases. He also believes that applying the model in hospitals will achieve good results and improve the radiologists productivity. Accuracy and F-measure evaluation performance measures were applied to compare among the classifiers. The results show that the Naïve Bayes was the best classifier in improving the productivity of radiologists, it improved the productivity by up to 24% as it assigned the appropriate case to the appropriate radiologist. Naïve Bayes had the highest value in Accuracy and F-measure by up to 8% in accuracy and 4% in F-measure.