Abstract
A lay-language glossary of radiology, built to help patients better understand the content of their radiology reports, has been analyzed for its coverage and readability, but not for its completeness. We present an iterative method to sample radiology reports, identify “missing” terms, and measure the glossary’s completeness. We hypothesized that the refinement process would reduce the number of missing terms to fewer than 1 per report. A random sample of 1000 radiology reports from a large US academic health system was divided into 10 cohorts of 100 reports each. Each cohort was reviewed in sequence by two investigators to identify terms (single words and multi-word phrases) absent from the glossary. Terms marked as new were added to the glossary and hence was shown as matched in subsequent cohorts. This HIPAA-compliant study was IRB-approved; informed consent was waived. The refinement process added a mean of 288.0 new terms per 100 reports in the first 5 cohorts vs. a mean of 66.0 new terms per 100 reports in the last 5 cohorts; the difference was statistically significant (p < .01). After reviewing 500 reports, the review process found fewer than 1 new term per report in each of 500 subsequent reports. The findings suggest that 500 to 1000 reports is adequate to test the completeness of a glossary, and that the glossary after iterative refinement achieved a high level of completeness to cover the vocabulary of radiology reports.
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Chan, P.Y.W., Kahn, C.E. Evaluating Completeness of a Radiology Glossary Using Iterative Refinement. J Digit Imaging 32, 417–419 (2019). https://doi.org/10.1007/s10278-018-0137-0
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DOI: https://doi.org/10.1007/s10278-018-0137-0