The goal of this project is to create the foundations for an automated alerting system that will notify providers before ordering a computer tomography (CT) scan if a patient has had prior similar scans at any of the facilities participatin in a health information exchange (HIE). Achieving this goal will help avoid duplicate CT scans, which will address a major patient safety issue and decrease healthcare costs. The challenge addressed by this project is that it is difficult when duplicate, potentially avoidable CTs exist a other sites, and a building an alerting system is currently not possible because the diverse providers in the HIE, including hospitals and radiology facilities, use different proprietary CT codes. Mapping these codes to a common, controlled radiology terminology is essential if we are to understand how often CT scans are duplicated and create a system to reduce redundancy.
The specific aims are 1) Assess the reliability and comprehensiveness of mapping proprietary CT codes from 40 sites participating in an HIE to a newly combined LOINC/RadLex terminology; 2) Create/adapt an anatomical framework for body regions and map all LOINC CT codes to it; 3) Evaluate the use of HIE-wide data to detect CT crossover rates. The reliability o mapping to create a consistent terminology framework will be assessed by measuring kappa inter-rater reliability on a de-identified dataset drawn from the Healthix HIE. Concept-type and concept-token coverage will be measured to assess whether the combined LOINC/RadLex codes comprehensively cover CT terms at each site and across the entire Healthix HIE. The mapping table created in aim 2, which will map all CT codes to an anatomical framework validated by the LOINC/Radlex Radiology Committee, will be freely disseminated on the Internet. The potential impact of this work will be assessed in Aim 3, which will measure how many unique patients have same CT, similar CT (same body region), or proximate CT (adjacent body region) across multiple sites in the HIE, and demonstrate the increased ability to detect these categories of potentially duplicate CTs when using HIE-wide data compared to each provider's site-specific data.

Public Health Relevance

Thisprojectwillmapproprietarycomputerizedtomography(CT)namesfrom40provider organizationsparticipatinginahealthinformationexchangeintheNewYorkmetropolitanarea toauniversalsetofCTtermsintheLOINCcontrolledterminology,andmapallLOINCCT termstonewanatomicalvariables,inordertosupportaneventualduplicateCTalertingsystem. Thisfuturealertingsystemwillhelpreducecostsandradiationexposuretopatientswhen duplicate,potentiallyavoidableCTscansareperformedbecausetheproviderisunawareof preexistingCTsatanotherinstitution.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
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Icahn School of Medicine at Mount Sinai
Emergency Medicine
Schools of Medicine
New York
United States
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Slovis, Benjamin H; Lowry, Tina; Delman, Bradley N et al. (2017) Patient crossover and potentially avoidable repeat computed tomography exams across a health information exchange. J Am Med Inform Assoc 24:30-38
Beitia, Anton Oscar; Lowry, Tina; Vreeman, Daniel J et al. (2017) Standard Anatomic Terminologies: Comparison for Use in a Health Information Exchange-Based Prior Computed Tomography (CT) Alerting System. JMIR Med Inform 5:e49