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

Full Abstract

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 <.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.

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.

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Author information

Author/s: Dang, Pragya A (PA); 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);

Affiliation: Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA.

Journal and publication information

Publication Type: Journal Article

Journal: Journal of the American College of Radiology : JACR (J Am Coll Radiol), published in United States. (Language: eng)

Reference: 2008-Mar; vol 5 (issue 3) : pp 197-204

Dates: Created 2008/03/03; Completed 2008/05/08;

PMID: 18312968, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Adolescent
Adult
Age Factors
Aged
Angiography - methods
Child
Child, Preschool
Cross-Sectional Studies
Decision Making, Computer-Assisted
Diagnostic Imaging - methods; standards
Female
Health Planning Guidelines
Humans
Infant
Magnetic Resonance Imaging - methods

Magnetic Resonance Imaging - methods
Male
Middle Aged
Natural Language Processing
Quality Control
Radiology - standards
Radiology Department, Hospital
Registries
Retrospective Studies
Risk Factors
Sensitivity and Specificity
Sex Factors
Tomography, X-Ray Computed - methods
Ultrasonography, Doppler - methods
United States

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