BACKGROUND
From the perspective of medical ethics, patient safety is the core before any other factors within health science. As the application of health science, medical services are inseparable from the safety of patients' lives and medical ethics. Scope of its practice is composed of statutory and individual components, includes codes of ethics and other resources. As the Quality Management Center of Clinical Nutrition in Jiangsu, it is the system`s responsibility to standardize and improve the professional performance within the province`s tertiary hospitals. Their quality assessment comes from the reported data of all CND among these hospitals, such as human and material resource, professional practice, foodservice operation, patient`s satisfaction, and nutrition education presentation for hospital and community.
Within this information era, most of the current information is network-based, as well as the data of healthcare. An electronic system that automatically collects medical information can realize timely monitoring of patient health, improve the effectiveness and accuracy of medical treatment. From a medical quality perspective, a reliance intelligent management system can improve data curation, reduce human resource costs, and contribute to facilitating continuous improvement. As one of the inventions in the information era, AI shows its strong adaptability to the network-based health-care system. It can be introduced into clinical behavior detection accurately and automatically, and of great significance for reducing the incidence of treatment errors and ensuring patients safety. However, the amount of digital data has increased dramatically after the appliance of online system5. A crucial consequence is that data management has become more complex, which has increased the necessity for methods that are able to deal with the quality assessment of digital information. From the perspective of QM and the discipline development of a medical specialty, such as CN, the specific indicators and the application evaluation of AI are important to achieving quality control goals.
To our knowledge, the application of AI into medical service quality assessment has merely been evaluated, especially for CND. There existed a unified platform for all the QM centers of various medical specialties set up by the Jiangsu Provincial Health Commission. After its broken-down in October 2017, the QMCNJ became the first center to independently develop and promote the application of its online platforms named “Jiangsu Province Clinical Nutrition Management Platform”. It was officially launched in the QMSNJ in 2019 and successfully promoted to 70 CND within the quality control system. They are required to fill in relevant information regularly in accordance with the regulations of “Strengthening the Management of Provincial Medical Quality Management System” formulated by JPHC, which has revised in September 2020. Since the stable application of this platform for two years, its effect in QM required to be validated. At the same time, the application value of AI and the development of the CND in Jiangsu can also be clarified.
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.
METHODS
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.
CONCLUSIONS
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.
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