Search

Objective Community health services are an emerging trend. We have found in practice that diagnosis and treatment of respiratory diseases in the community are distinct. The respiratory department’s daily work involves a number of outpatient registration items and a vast workload. The routine manual operation is inefficient and it is not convenient to make effective statistical analysis of the outpatient data to identify the risk factors closely related to diseases. Therefore, it is imperative to process the outpatient information of patients with respiratory diseases effectively and efficiently in a unified manner by means of computer technology. Methods The design and realization of the Community Health Service-oriented computer-assisted Information System for Diagnosis and Treatment of Respiratory Diseases (CHS-DTRD) was completed as part of the community intervention study on bronchial asthma that was carried out jointly by the Nanjing First Hospital Affiliated to Nanjing Medical University and the Hospital of Nanjing University of Science & Technology, and based on 2 years of experience and the needs of an overall analysis. Results The computer-assisted information system for diagnosis and treatment was developed using Java Server Page (JSP) technology and introducing the advanced Asynchronous JavaScript XML (AJAX) technique and MS-SQL Server was used in the background database. CHS-DTRD was composed of eight functional modules (outpatient data maintenance, outpatient appointment, intelligent analysis for disease risk factors, query and statistics, data dictionary maintenance, database manipulation, access control, and system configuration). CHS-DTRD featured a friendly interface, convenient operation, and stability and reliability. Conclusion Community health-oriented diagnosis and treatment of respiratory diseases is simple, programmable, and intuitive, thus the workload of physicians is significantly reduced and the work efficiency is improved. This system facilitates an intelligent analysis of disease risk factors using data mining technology, and provides physicians with suggestions on intelligent analysis for diagnosis of disease and conclusion of disease causes.