Hospital clinical laboratories have been in the process of informatization gradually with the construction and application of hospital clinical information system and data management middleware system. Meanwhile, laboratory data information contents have been on the rise and data categories have been increasing. Therefore, an effective data management and analysis measure is a must for laboratory data management.
Clinical laboratory data has enormous size and various categories. A hospital with a daily sample quantity of 1,000 tubes will generate about over 3 million pieces of test result data annually. Furthermore, the middleware system will record its communication with the test equipment and the Laboratory Information system (LIS), rule execution, sample events, patient information, quality control information and so on. The data volume is surprisingly massive.
Various data categories and enormous data size provide clinical laboratory data analysis large space to develop. Informationalized laboratory data management can be realized by connecting the LIS and middleware management system with the test equipment. Data communication and transmission are conducted in every system, and finally, centralized store, processing and management will be carried out to integrate data from all equipment for the convenience of statistics and analysis so as to realize effective monitoring of the whole laboratory process management.
The middleware is able to provide strong support for the scientific and informationalized laboratory operation by obtaining, collating and analyzing data in the middleware data management system.
1. Statistical analysis of workload distribution. The laboratory workload and development tendency are analyzed by sample and test amount statistics to enable administrators to better allocate equipment and human resources.
2. Statistical analysis of TAT duration. Time consumed in every stage of sample receiving – sample on line – sample entering the equipment – result getting – result uploading will be obtained to analyze which stage needs to be improved.
3. Statistical analysis of automatic review rate. Evaluate specific effectiveness of the automatic review by statistically analyzing the automatic review rate of every project and every sample to find specific problems so as to determine the direction of the next adjustment.
4. Statistical analysis of rules. Evaluate the usage and modification of corresponding rules by execution rate of every rule set in the middleware system.
Not only that, more laboratory data analysis contents and approaches can be developed according to various demands of different hospitals.
Laboratory data management can assist administrators to manage and control laboratory operation conditions more comprehensively as well as employ laboratory resources more properly. In the information era that the Internet, HIS, LIS and middleware are basically universal, by connecting clinical information and test information with patient information to conduct extensive data statistical analysis of test results and patient conditions, data analysis can provide suspected symptom references of patients for doctors, and can even offer analyzed living standard indicators guide for medical journals and national health.
In conclusion, effective laboratory data management and analysis can greatly contribute to informationalized laboratories nowadays. Although the considerable potential of test data information has not been developed, there is large space for developing and utilizing aggregated statistical data. This field is filled with potential and opportunities, which will even have great impact on the development of laboratory medicine.