Natural language processing using online analytic processing for assessing recommendations in radiology reports.
OBJECTIVE
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).


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 <.0001). Significant variations in I(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 < .0001). For most modalities, outpatients had a higher rate of recommendations when compared with inpatients.


CONCLUSIONS
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.
Dang
Pragya A
PA
Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.
Kalra
Mannudeep K
MK
Blake
Michael A
MA
Schultz
Thomas J
TJ
Stout
Markus
M
Lemay
Paul R
PR
Freshman
David J
DJ
Halpern
Elkan F
EF
Dreyer
Keith J
KJ
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000792:Angiography / Q000379:methods; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D003658:Decision Making, Computer-Assisted; D003952:Diagnostic Imaging / Q000379:methods* / Q000592:standards; D005260:Female; D006287:Health Planning Guidelines; D006801:Humans; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D009323:Natural Language Processing; D011786:Quality Control; D011871:Radiology / Q000592:standards; D011872:Radiology Department, Hospital; D012042:Registries; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012737:Sex Factors; D014057:Tomography, X-Ray Computed / Q000379:methods; D018608:Ultrasonography, Doppler / Q000379:methods; D014481:United States