An Automated Method for Identifying Individuals with a Lung Nodule Can Be Feasibly Implemented Across Health Systems

Introduction: The incidence of incidentally detected lung nodules is rapidly rising, but little is known about their management or associated patient outcomes. One barrier to studying lung nodule care is the inability to efficiently and reliably identify the cohort of interest (i.e. cases). Investigators at Kaiser Permanente Southern California (KPSC) recently developed an automated method to identify individuals with an incidentally discovered lung nodule, but the feasibility of implementing this method across other health systems is unknown.Methods: A random sample of Group Health (GH) members who had a computed tomography in 2012 underwent chart review to determine if a lung nodule was documented in the radiology report. A previously developed natural language processing (NLP) algorithm was implemented at our site using only knowledge of the key words, qualifiers, excluding terms, and the logic linking these parameters.Results: Among 499 subjects, 156 (31%, 95% confidence interval [CI] 27-36%) had an incidentally detected lung nodule. NLP identified 189 (38%, 95% CI 33-42%) individuals with a nodule. The accuracy of NLP at GH was similar to its accuracy at KPSC: sensitivity 90% (95% CI 85-95%) and specificity 86% (95% CI 82-89%) versus sensitivity 96% (95% CI 88-100%) and specificity 86% (95% CI 75-94%).Conclusion: Automated methods designed to identify individuals with an incidentally detected lung nodule can feasibly and independently be implemented across health systems. Use of these methods will likely facilitate the efficient conduct of multi-site studies evaluating practice patterns and associated outcomes.</jats:p

Using Natural Language Processing to Identify Health Plan Beneficiaries With Pulmonary Nodules

Background/Aims: The development of a portable, automated method for identifying individuals with lung nodules will facilitate the efficient conduct of population-based studies of nodule care and associated outcomes. We evaluated the performance of a previously developed natural language processing (NLP) algorithm for identifying health plan beneficiaries with pulmonary nodules. Methods: A cross-sectional study was performed of 500 randomly selected adult, in-network health plan beneficiaries with continuous enrollment at Group Health Cooperative who underwent a computed tomography (CT) of the chest in 2012, had no history of lung cancer and had not undergone a CT between 2009 and 2011. An NLP algorithm originally developed at Kaiser Permanente Southern California assessed electronic radiology reports using keywords and qualifiers relating to pulmonary nodules ranging in size from 5 to 30 mm among individuals who had undergone CT and had an International Classification of Diseases (ICD-9-CM) diagnostic code for a lung nodule. This algorithm was applied to our patient population and modified to identify pulmonary nodules regardless of size. A trained chart abstractor reviewed radiology reports to determine whether the radiologist reported a lung nodule. An experienced, board-certified thoracic surgeon adjudicated radiology reports with unclear documentation of a nodule. Results: The true prevalence of pulmonary nodules among individuals undergoing CT in 2012 — median age 65 years, 43% men, 84% white, 51% smokers — was 34%. Median nodule size was 6 mm (range 2–87 mm). NLP identified 218 (44%) individuals with a nodule. The accuracy of NLP was as follows: sensitivity 91%, specificity 81%, positive predictive value 72% and negative predictive value 95%. Discussion: An automated method of using NLP and electronic radiology text reports — originally developed at one Cancer Research Network (CRN) site — reasonably identifies health plan members with pulmonary nodules at another CRN site. This finding supports the notion that automated methods are portable across integrated health systems and institutions using electronic medical records. Ongoing work seeks to determine whether modifications to the NLP algorithm can improve performance. Given its current performance characterized by a high negative predictive value, NLP could be used to decrease the burden of chart abstraction in population-based studies of nodule care