Automatic extraction of PIOPED interpretations from ventilation/perfusion lung scan reports

Free-text documents are the main type of data produced by a radiology department in a hospital information system. While this type of data is readily accessible for clinical data review it can not be accessed by other applications to perform medical decision support, quality assurance, and outcome studies. In an attempt to solve this problem, natural language processing systems have been developed and tested against chest x-rays reports to extract relevant clinical information and make it accessible to other computer applications. We have used a natural language processing tool called SymText to extract relevant clinical information from a different type of radiology report, the Ventilation/Perfusion lung scan report. Results of this effort can be analyzed in terms of precision and recall. The overall precision was 0.88 and recall was 0.92. In addition, the natural language processing system functions differently in reports with and without an impression section. If this type of information can be successfully extracted from radiology reports, one can develop quality monitors for the diagnostic performance of the radiologist by correlating the impressions with gold standard data present in a hospital information system. Avoiding the manual effort previously necessary to create quality assurance data, can lead to a higher frequency of quality review in a radiology department.