Development and Evaluation of a Diagnostic Documentation Support System using Knowledge Processing

In this paper, we will introduce a system which supports creating diagnostic reports. Diagnostic reports are documents by doctors of radiology describing the existence and nonexistence of abnormalities from the inspection images, such as CT and MRI, and summarize a patient's state and disease. Our system indicates insufficiencies in these reports created by younger doctors, by using knowledge processing based on a medical knowledge dictionary. These indications are not only clerical errors, but the system also analyzes the purpose of the inspection and determines whether a comparison with a former inspection is required, or whether there is any shortage in description. We verified our system by using actual data of 2,233 report pairs, a pair comprised of a report written by a younger doctor and a check result of the report by an experienced doctor. The results of the verification showed that the rules of string analysis for detecting clerical errors and sentence wordiness obtained a recall of over 90% and a precision of over 75%. Moreover, the rules based on a medical knowledge dictionary for detecting the lack of required comparison with a former inspection and the shortage in description for the inspection purpose obtained a recall of over 70%. From these results, we confirmed that our system contributes to the quality improvement of diagnostic reports. We expect that our system can comprehensively support diagnostic documentations by cooperating with the interface which refers to inspection images or past reports.