Objective:We describe and evaluate the mapping of computerized tomography (CT) terms from 40 hospitals participating in a health information exchange (HIE) to a standard terminology. Methods:Proprietary CT exam terms and corresponding exam frequency data were obtained from 40 participant HIE sites that transmitted radiology data to the HIE from January 2013 through October 2015. These terms were mapped to the Logical Observations Identifiers Names and Codes (LOINC®) terminology using the Regenstrief LOINC mapping assistant (RELMA) beginning in January 2016. Terms without initial LOINC match were submitted to LOINC as new term requests on an ongoing basis. After new LOINC terms were created, proprietary terms without an initial match were reviewed and mapped to these new LOINC terms where appropriate. Content type and token coverage were calculated for the LOINC version at the time of initial mapping (v2.54) and for the most recently released version at the time of our analysis (v2.63). Descriptive analysis was performed to assess for significant differences in content-dependent coverage between the 2 versions. Results:LOINC's content type and token coverages of HIE CT exam terms for version 2.54 were 83% and 95%, respectively. Two-hundred-fifteen new LOINC CT terms were created in the interval between the releases of version 2.54 and 2.63, and content type and token coverages, respectively, increased to 93% and 99% (P < .001). Conclusion:LOINC's content type coverage of proprietary CT terms across 40 HIE sites was 83% but improved significantly to 93% following new term creation.

The concept of likelihood ratios was introduced more than 40 years ago, yet this powerful metric has still not seen wider application or discussion in the medical decision-making process. There is concern that clinicians-in-training are still being taught an oversimplified approach to diagnostic test performance and have limited exposure to likelihood ratios. Even for those familiar with likelihood ratios, they might perceive them as mathematically cumbersome in application, if not difficult to determine for a particular disease process. This article takes a conceptual approach to likelihood ratios and applies them to two clinical settings: 1) severe intracranial injury after minor head trauma and 2) suspected pulmonary embolism with shortness of breath. Likelihood ratios are the most appropriate metric for efficient rational clinical examination and can prevent unnecessary and wasteful treatments and procedures.