2011 HSR&D National Meeting Abstract
1004 — Implementation of a Text-Mining-Based Clinical Decision Support Tool to Improve the Management of Pulmonary Nodules
Garla VN (Yale University), Steinhardt S
(Evergreen Design, Guilford, CT), Levin F
(Evergreen Design, Guilford, CT), Mutalik P
(Connecticut VA Healthcare System), Brandt C
(Connecticut VA Healthcare System), Taylor C
(Connecticut VA Healthcare System)
To identify potentially malignant lung nodules in radiology reports to ensure their management according to clinical practice guidelines.
The West Haven VA has adopted the guidelines of the Fleischner society for the management of pulmonary nodules. Radiologists currently manually code as 'cancer alerts' reports with lung nodules that require surveillance. Cancer alerts are forwarded to cancer care coordinators who manage surveillance and treatment of the nodules. Internal audits have shown undercoding of cancer alerts, demonstrating the need for automated coding. We developed a text-mining system based on open-source Natural Language Processing tools to automatically code cancer alerts. The system extracts information from radiology reports, applies decision rules that represent the clinical practice guidelines to the extracted data, and flags reports with mentions of nodules that require surveillance. The system then forwards flagged reports to cancer care coordinators for review.
We applied the system to all chest CT reports from the West Haven VA from October 2008 to August 2010 (8360 reports). We manually reviewed a subset of these reports and estimated the precision (positive predictive value), recall (sensitivity), and F-Score of the system as 90%, 95%, and 92%, respectively. Common manual coding errors included undercoding of small nodules, stable nodules, and ground-glass opacities. The system identified 710 patients for which no report had previously been coded as a cancer alert.
The text-mining system we developed accurately identified lung nodules from radiology reports that require follow-up according to clinical practice guidelines.
Early detection of lung cancers significantly improves outcome and survival. The text-mining system we developed will improve the surveillance of lung nodules, facilitating early detection and effective treatment of cancerous lesions.