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Published on: October 13, 2023
Applying a Text-Search Algorithm to Radiology Reports Can Find More Patients With Pulmonary Nodules Than Radiology
Rolando Sanchez1, George Bailey1, Peter J Kaboli1
1is a Clinical Assistant Professor of Pulmonary and Critical Care Medicine; is a Professor of Internal Medicine; and is a Professor of Internal Medicine, all at the University of Iowa Carver College of Medicine in Iowa City. is a Research Data Manager; is a Registered Nurse and Research Coordinator; and Peter Kaboli is an Associate Investigator, all in the Center for Access and Delivery Research and Evaluation (CADRE) at the Iowa City VA Healthcare System. is a Research Professor of Public Health at the Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, VA Puget Sound Health Care System and the University of Washington School of Public Health in Seattle.
Introduction:
Chest imaging often incidentally finds indeterminate nodules that need to be monitored to ensure early detection of lung cancers. Health care systems need effective approaches for identifying these lung nodules. We compared the diagnostic performance of 2 approaches for identifying patients with lung nodules on imaging studies (chest/abdomen): (1) relying on radiologists to code imaging studies with lung nodules; and (2) applying a text search algorithm to identify references to lung nodules in radiology reports.
Methods:
We assessed all radiology studies performed between January 1, 2016 and November 30, 2016 in a single Veterans Health Administration hospital. We first identified imaging reports with a diagnostic code for a pulmonary nodule. We then applied a text search algorithm to identify imaging reports with key words associated with lung nodules. We reviewed medical records for all patients with a suspicious radiology report based on either search strategy to confirm the presence of a lung nodule. We calculated the yield and the positive predictive value (PPV) of each search strategy for finding pulmonary nodules.
Results:
We identified 12,983 imaging studies with a potential lung nodule. Chart review confirmed 8,516 imaging studies with lung nodules, representing 2,912 unique patients. The text search algorithm identified all the patients with lung nodules identified by the radiology coding (n = 1,251) as well as an additional 1,661 patients. The PPV of the text search was 72% (2,912/4,071) and the PPV of the radiology code was 92% (1,251/1,363). Among the patients with nodules missed by radiology coding but identified by the text search algorithm, 130 had lung nodules > 8 mm in diameter.
Conclusions:
The text search algorithm can identify additional patients with lung nodules compared to the radiology coding; however, this strategy requires substantial clinical review time to confirm nodules. Health care systems adopting nodule-tracking approaches should recognize that relying only on radiology coding might miss clinically important nodules.
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