Communication of clinically important follow-up recommendations when abnormalities are identified on imaging studies is prone to error. The absence of an automated system to identify and track radiology follow-up recommendations is an important barrier to ensuring timely follow-up of patients, especially for non-acute but potentially life threatening and unexpected findings. The primary goal of this proposal is to develop a Natural Language Processing (NLP) system to extract clinically important recommendation information from free-text radiology reports. Each radiology report will be preprocessed at the structural, syntactic, and semantic level to generate features that will be used to extract the boundaries of sentences that include recommendation information as well as the details of reason for recommendation, requested imaging test, and recommendation time frame. We will use a large corpus of free-text radiology reports represented by a mixture of modalities (e.g., radiography, computed tomography, ultrasound, and magnetic resonance imaging (MRI)) from three different institutions. Using this dataset we will perform the following specific aims:
Aim 1. Create a multi- institutional radiology report corpus annotated for clinically important recommendation information;
Aim 2. Develop a novel NLP system to extract clinically important recommendations in radiology reports. The proposed research is innovative because it will generate a new text processing approach that can be used to flag reports visually and electronically so that separate workflow processes can be initiated to reduce the chance that necessary investigations or interventions suggested in the report are missed by clinicians. The proposed set of tools will be disseminated to the biomedical informatics community as open source tools.

Public Health Relevance

Communication of recommendations for necessary investigations and interventions when abnormalities are identified on imaging studies is prone to error. When recommendations are not systematically identified and promptly communicated to referrers, poor patient outcomes can result. We propose to build natural language processing tools to automatically extract clinically important recommendation information from radiology reports.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21EB016872-02
Application #
8804856
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Pai, Vinay Manjunath
Project Start
2014-03-01
Project End
2016-02-29
Budget Start
2015-03-01
Budget End
2016-02-29
Support Year
2
Fiscal Year
2015
Total Cost
$217,500
Indirect Cost
$92,500
Name
University of Washington
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195