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A Radiation Oncology-Specific Automated Trigger Indicator Tool for High-Risk, Near-Miss Safety Events.
Hartvigson, Pehr E; Gensheimer, Michael F; Spady, Phil K; Evans, Kimberly T; Ford, Eric C.
Afiliação
  • Hartvigson PE; Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington; Department of Radiation Medicine, Oregon Health and Science University, Portland, Oregon. Electronic address: hartvigs@ohsu.edu.
  • Gensheimer MF; Department of Radiation Oncology, Stanford University, Stanford, California.
  • Spady PK; Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington.
  • Evans KT; Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington.
  • Ford EC; Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington.
Pract Radiat Oncol ; 10(3): 142-150, 2020.
Article em En | MEDLINE | ID: mdl-31783170
PURPOSE: Error detection in radiation oncology relies heavily on voluntary reporting, and many adverse events and near misses likely go undetected. Trigger tools use existing data in patient charts to identify otherwise-unaccounted-for events and have been successfully employed in other areas of medicine. We developed an automated radiation oncology-specific trigger tool and validated it against near-miss data from a high-volume incident learning system (ILS). METHODS AND MATERIALS: Twenty triggers were derived from an electronic radiation oncology information system. Data from the systems over an approximately 3.5-year period were split randomly into training and test sets. The probability of a high-grade (grade 3-4) near miss for each treatment course in the training set was estimated using a regularized logistic regression model. The predictive model was applied to the test set. Records for 25 flagged treatment courses with an ILS entry were reviewed to explore the association between triggers and near misses, and 25 flagged courses without an ILS entry were reviewed to detect unreported near misses. RESULTS: Of the 3159 treatment courses analyzed, 357 had a grade 3 to 4 ILS entry; 2210 courses composed the training set, and the test set had 949 courses. Areas under the curve on the training and test sets were 0.650 and 0.652, respectively. Of 20 triggers, 9 reached statistical significance on univariate analysis. Fifty percent of the 25 treatment courses in the test set with the highest predicted likelihood of a high-grade near miss with an ILS entry had a direct relationship between the triggers and the near miss. Review of the 25 treatment courses with the highest predicted likelihood of high-grade near miss without an ILS entry found 2 unreported near-miss events. CONCLUSIONS: The radiation oncology-specific automated trigger tool performed modestly and identified additional treatment courses with near-miss events. Radiation oncology trigger tools deserve further exploration.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Radioterapia (Especialidade) / Near Miss Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Pract Radiat Oncol Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Gestão de Riscos / Radioterapia (Especialidade) / Near Miss Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Child / Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Pract Radiat Oncol Ano de publicação: 2020 Tipo de documento: Article