ABSTRACT
The wide use of computed tomography (CT) scanning for patients with blunt abdominal trauma can reveal incidental findings that vary in their importance. We evaluated these findings, how it was reported by radiologists and its implication on the trauma care. In 30 out of 154 patients, 32 incidental findings were discovered (19.5%). Out of these 32 findings, only 3 cases (9.4%) were considered significant and required immediate attention from the managing team. In all these 3 cases, the findings were described in the body of the report and highlighted in the conclusion section at the end of the radiology report. However, similar reporting style was used in only 58.4% of cases with moderate clinical concern and 23.5% of cases with little clinical concern. In 41.2% of cases with little concern, the incidental findings were not mentioned in the radiology report. In conclusion, incidental findings in CT scan performed for blunt abdominal trauma were common but many were clinically insignificant. There is little consistency in radiology reporting of these findings especially those with moderate and little clinical concern.
ABSTRACT
OBJECTIVE: Electronic Medical Record contains the majority of clinical data in unstructured text. The information in the textual document can be stored in conceptual format and used to support clinical care by text summarization technique. In this study, we present Information Extraction(IE) using Concept Node(CN) which is extraction rule in case frame from brain radiology reports in SNUH(Seoul National University Hospital) for summarization. METHOD: Following steps are performed: design conceptual model to define semantic entities as extraction templates of brain radiology report, build CN dictionary based on statistical syntactic pattern and development of parser to extract relevant information based on defined templates. RESULTS: The three evaluation results shows that 19% precision improvement after post processing supplemental specified complex verb construction and 19.24~21.25% accurate semantic effectiveness with extracting additional Korean noun. The average of precision is 85.18%, average of recall is 93.71% and F-measure is 0.89. CONCLUSION: Our approach has advantageous elements for different language at the same sentence. We expect this IE technology can summarize vast amount radiology texts material for clinical decision support system effectively and hope this study helps the evolution of clinical data representation in Korean medical records and its integration into the EMR in the future.
Subject(s)
Brain , Electronic Health Records , Hope , Information Storage and Retrieval , Medical Records , SemanticsABSTRACT
Based on the analysis of dataflow and workflow of hospital HIS?RIS and PACs,this article projects a system integration model in the digital hospital construction.By the design of a typical hospital information system,a method of all the information system workflow integration is proposed and validated,which is based on the HL7 and DICOM.