TU-D-201-04: Veracity of Data Elements in Radiation Oncology Incident Learning Systems

Autor: David J. Hoopes, Gary A. Ezzell, Ajay Kapur, Derek Brown, Cindy Tomlinson, Sonja Dieterich, Suzanne B. Evans, K Kapetanovic
Rok vydání: 2016
Předmět:
Zdroj: Medical Physics. 43:3743-3744
ISSN: 0094-2405
DOI: 10.1118/1.4957470
Popis: Purpose: Incident learning systems encompass volumes, varieties, values, and velocities of underlying data elements consistent with the V’s of big data. Veracity, the 5th V however exists only if there is high inter-rater reliability (IRR) within the data elements. The purpose of this work was to assess IRR in the nationally deployed RO-ILS: Radiation Oncology-Incident Learning System (R) sponsored by the American Society for Radiation Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM). Methods: Ten incident reports covering a wide range of scenarios were created in standardized narrative and video formats and disseminated to 67 volunteers of multiple disciplines from 26 institutions along with two published narratives from the International Commission of Radiological Protection to assess IRR on a nationally representative level. The volunteers were instructed to independently enter the associated data elements in a test version of RO-ILS over a 3-week period. All responses were aggregated into a spreadsheet to assess IRR using free-marginal kappa metrics. Results: 48 volunteers from 21 institutions completed all reports in the study period. The average kappa score for all raters across all critical data elements was 0.659 [range 0.326–1.000]. Statistically significant differences (p
Databáze: OpenAIRE
Externí odkaz: