| With the rapid development of information technology, it is necessary to accelerate hospital information construction in which EMR is the core task. EMR, a daily tool to carry out health care activities, which has important significance to assist doctors with their work and improve the quality of medical treatment, has evolved into the doctor’s workstation. To promote the development of EMR, a solution is proposed, which optimize the EMR logic with the help of OLAP and data-mining techniques. The solution combines OLAP, data-mining technique in BI and other DMAs to analyze the data in HIS database, and feedbacks the results to EMR in real time to strengthen the role of EMR in assisting doctor’s work.In this paper, two specific applications are designed and implemented based on BI platform DeepSee and a structured EMR. (1) Prescription order details are analyzed in real time with OLAP, which cut the statistics of drug using frequency from multiple dimensions such as time, department, doctor, order category and drug. Based on the rank of drug frequency, a drop-down drug menu for each doctor’s order system is realized, so that frequently used drugs of each doctor can be higher-ranking, saving their time in searching frequently used drugs. (2) By combining OLAP and Apriori algorithms, correlative prescriptions for patient’s diagnosis result can be searched in HIS database and displayed automatically in the order UI with assurance for system respond speed, which serves as order reminders to relieve memory burden and working pressure on doctors.Therefore, drug menu with personalized rank and reminder function of correlative prescription can assist doctors with their work, and OLAP as well as data-mining can provide EMR with the ability of "self-learning". In addition, efficient data processing capability of BI makes EMR extract information quickly, ensuring the system respond swiftly under some complex algorithms so that optimization is balanced. |
PDF Full Text Request