Development and Application of Hospital Adverse Events Database and Management Information System

By managing adverse events in medical care, administrator can detect errors, analyze nature and causes of adverse events, and establish mechanisms to prevent them. However, most adverse event information management systems do not provide event prediction function, and most adverse event related research uses a small sample. Therefore, the present project develops an adverse event cloud database and management information system to provide clinical personnel for the management and prevention of adverse events. The specific methods and objectives include: (1) Designing and establishing an adverse event database with MySQL, including drugs events, falls events, tubing events, pressure injury and needlestick injury; (2) Building an application web service with PHP for user to manage data, search events, and generate statistical charts of adverse events; (3) Providing users explore important factors of damage of adverse events with decision tree model function in the web page; (4) Evaluate the user's satisfaction with the system. The implementation advantages of this project include: (1) the PI specializes in big data analysis and has experience in management of clinical and nursing information, thus can provide model algorithm development technology; (2) the partner is good at software development and has experience in development of both big data cloud database and information management system; (3) adverse event notification data from more than 1200 beds will be provided by a quasi-medical center, and they also help making system design meet clinical needs.In addition to facilitating medical personnel to report on adverse events in the hospital, this system also provide decision tree model results. These can be reference for clinical and have important contributions to the prevention and management of adverse events, and finally to enhance patient safety This project combines the techniques of care quality management, database design, and machine learning to transfer from system architecture implementation to materialized products. In the future, using Internet of Things technology, the system can be combined with other devices such as heart rate detectors, activity recorders, etc. In addition to expanding the predictive variables to increase the accuracy of predictions, it can also provide immediate warning services to achieve the function of adverse event prevention.