An Intelligent Tumors Coding Method Based on Drools

Abstract

In order to solve the problems of low efficiency and heavy workload of tumor coding in hospitals, we proposed a Drools-based intelligent tumors coding method. At present, most tumor hospitals use manual coding, the trained coders follow the main diagnosis selection rules to select the main diagnosis from the discharge diagnosis of the tumor patients, and then code all the discharge diagnoses according to the coding rules. Owing to different coders have different familiarity with the main diagnosis selection rules and ICD-10 disease coding, it will reduce the efficiency of the artificial coding results and affect the quality of the whole medical record. We first analyze the ICD library information, doctor's diagnostic information, radiotherapy information or chemotherapy information, surgery information, hospitalization information and other related information, and then generated Drools rule files based on the main diagnostic selection principles and coding principles, we also combined the text similarity analysis algorithm to construct an intelligent diagnostic information coding method. Practice shows that the coding method can be used to make the work efficiently and at the same time obtain the coding results which meet the standard and have high accuracy, so that the coders can be free from the repeated work and pay more attention to coding quality control and the coding logic adjustment.