چكيده به لاتين
Careful quality control in medical diagnostic laboratories is a key and essential requirement that accounts for a significant portion of the cost of testing. While the use of quality control kits and the use of statistical approaches can be costly and time consuming, using smart methods can reduce costs and improve the quality of quality control results. On the other hand, with the advent of electronic health records and the development of related technologies, there is a serious trend towards data-based approaches. Therefore, in this study, we provide a data model based on the improvement of the quality of clinical laboratory results, which facilitates the possibility of eliminating the costs associated with control kits, facilitates the decision on the quality of clinical test results by laboratory supervisors. This model uses the number of re-tests to determine whether it is under control or not. For this purpose, in this model, two methods of data mining and process mining have been used. In order to intelligently predict the re-test occurrence of the experiment, data mining was used to identify the process model and intelligent monitoring of the process. The data used to develop this model are related to patients in Rey City Laboratory, Tehran Province. The model also focuses on detecting FBS test error. Finally, in order to evaluate the performance of the model, we applied it to the real and new data of Rey City Laboratory. The results of the application of the model indicate its operational capability in laboratories. This study could be effective in reducing laboratory costs and improving the quality control of clinical laboratory results. Developing a model for other laboratories and laboratories can be a challenge for future work.
