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In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients. In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods. Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy. We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care. [ABSTRACT FROM AUTHOR]

Background: This study aims to investigate the effects of a nursing quality control and audit application (app) on the autonomous learning of nursing staff and nursing quality management by nursing supervisors. A multilevel interactive app is developed to assist nursing staff in conducting online autonomous learning and nursing supervisors in identifying problems and creating nursing quality improvement plans. The app could also present the different evaluation results of wards in visual charts for supervisors to review., Methods: A single-group pre- and post-test design was applied. Data were collected from 131 nurses between October 2019 and October 2020 to analyze the differences between nursing staffs' willingness to engage in autonomous learning and the integrity of nursing quality improvement plan writing before and after the intervention. The structured questionnaires included open-ended questions that cover aspects of nursing quality control, the audit app, and the information acceptance intention of nurses., Results: The participants' age and job title are negatively correlated with the app's usability, while the ability to use 3C (Computer, Communication, and Consumer Electronics products including mobile phones and laptops) equipment is positively correlated with the willingness to use the app. Nurses' satisfaction with the convenience of the online autonomous learning method is 92%, which indicates that the app could improve their willingness to learn. Following the intervention of the app, nursing supervisors' satisfaction with the integrity of nursing quality improvement plan writing increased from 41 to 88%., Conclusions: Using information technology products to assist in nursing quality management in clinical practice has a significant effect on nurses' load reduction and head nurses' satisfaction. Multilevel interactive nursing quality control and audit apps can improve nursing staff's willingness to learn independently, nursing quality, and the integrity of plan writing. Thus, nursing quality control and audit apps can be considered as suitable nursing quality control tools., (© 2021. The Author(s).)