Quality Management System for Clinical Nutrition: On the processing of the Artificial Intelligence into Quality Assessment

Objective: To critically evaluate the Quality Management System (QMS) for Clinical Nutrition (CN) in Jiangsu. Monitor its performance in quality assessment as well as human resource management from nutrition aspect. Investigate the appliance and development of Artificial Intelligence (AI) in medical quality control. Subjects: The study source of this research was all the staffs of 70 Clinical Nutrition Department (CND) of the tertiary hospitals in Jiangsu Province, China. These departments are all members of the Quality Management System of Clinical Nutrition in Jiangsu (QMSNJ). Methods: An online survey was conducted on all 341 employees within all these CNDs based on the staff information from the surveyed medical institutions. The questionnaire contains 5 aspects, while data analysis and AI evaluation were focused on human resource information. Results: 330 questionnaires were collected with the respondent rate of 96.77%. The QMS for CN has been build up for CNDs in Jiangsu, which achieved its target in human resource improvements, especially among dietitians. The increasing number of participated departments (42.8%) and the significant growth of dietitians (p=0.02, t=-0.42) are all expressions of the advancements of QMSNJ. Conclusion: As the first innovation of an online platform for QM in Jiangsu, JPCNMP has been successfully implemented among QMS from this research. This multidimensional electronic system can help QMSNJ and CND achieve quality assessment from various aspects, so as to realize the continuous improvement of clinical nutrition. The instrument of online platform, as well as AI technology for quality assessment is worth to be recommended and promoted in the future. Strengths This is the first evaluation of the online QM platform after its implementation in daily disciplinary management among the QMS in china. This research has been designed to investigate the status of CND multidimensionally. This analysis is emphasizing on the human resource approvement after the designation and application of QMS. A clearer forecast of AI in medical quality assessment and disciplinary construction was achieved, while some modifications are recommended in human resource management to improve its efficiency and accuracy.