Natural Language Processing Using Online Analytic Processing for Assessing Recommendations in Radiology Reports

Purpose The study purpose was to describe the use of natural language processing (NLP) and online analytic processing (OLAP) for assessing patterns in recommendations in unstructured radiology reports on the basis of patient and imaging characteristics, such as age, gender, referring physicians, radiology subspecialty, modality, indications, diseases, and patient status (inpatient vs outpatient). Materials and Methods A database of 4,279,179 radiology reports from a single tertiary health care center during a 10-year period (1995-2004) was created. The database includes reports of computed tomography, magnetic resonance imaging, fluoroscopy, nuclear medicine, ultrasound, radiography, mammography, angiography, special procedures, and unclassified imaging tests with patient demographics. A clinical data mining and analysis NLP program (Leximer, Nuance Inc, Burlington, Massachusetts) in conjunction with OLAP was used for classifying reports into those with recommendations (I REC ) and without recommendations (N REC ) for imaging and determining I REC rates for different patient age groups, gender, imaging modalities, indications, diseases, subspecialties, and referring physicians. In addition, temporal trends for I REC were also determined. Results There was a significant difference in the I REC rates in different age groups, varying between 4.8% (10-19 years) and 9.5% (>70 years) ( P REC rates were observed for different imaging modalities, with the highest rates for computed tomography (17.3%, 100,493/581,032). The I REC rates varied significantly for different subspecialties and among radiologists within a subspecialty ( P Conclusion The radiology reports database analyzed with NLP in conjunction with OLAP revealed considerable differences between recommendation trends for different imaging modalities and other patient and imaging characteristics.