@article{1967,
  author = {Allison Lipitz-Snyderman and David Classen and David Pfister and Aileen Killen and Coral L. Atoria and Elizabeth Fortier and Andrew S. Epstein and Christopher Anderson and Saul N. Weingart},
  title = {Performance of a Trigger Tool for Identifying Adverse Events in Oncology},
  abstract = {<jats:sec><jats:title>Purpose:</jats:title><jats:p> Although patient safety is a priority in oncology, few tools measure adverse events (AEs) beyond treatment-related toxicities. The study objective was to assemble a set of clinical triggers in the medical record and assess the extent to which triggered events identified AEs. </jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p> We performed a retrospective cohort study to assess the performance of an oncology medical record screening tool at a comprehensive cancer center. The study cohort included 400 patients age 18 years or older diagnosed with breast (n = 128), colorectal (n = 136), or lung cancer (n = 136), observed as in- and outpatients for up to 1 year. </jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p> We identified 790 triggers, or 1.98 triggers per patient (range, zero to 18 triggers). Three hundred four unique AEs were identified from medical record reviews and existing AE databases. The overall positive predictive value (PPV) of the original tool was 0.40 for total AEs and 0.15 for preventable or mitigable AEs. Examples of high-performing triggers included return to the operating room or interventional radiology within 30 days of surgery (PPV, 0.88 and 0.38 for total and preventable or mitigable AEs, respectively) and elevated blood glucose (&gt; 250 mg/dL; PPV, 0.47 and 0.40 for total and preventable or mitigable AEs, respectively). The final modified tool included 49 triggers, with an overall PPV of 0.48 for total AEs and 0.18 for preventable or mitigable AEs. </jats:p></jats:sec><jats:sec><jats:title>Conclusion:</jats:title><jats:p> A valid medical record screening tool for AEs in oncology could offer a powerful new method for measuring and improving cancer care quality. Future improvements could optimize the tool’s efficiency and create automated electronic triggers for use in real-time AE detection and mitigation algorithms. </jats:p></jats:sec>},
  year = {2017},
  journal = {J Oncol Pract},
  volume = {13},
  chapter = {e223-e230},
  month = {02/2017},
  publisher = {American Society of Clinical Oncology (ASCO)},
  issn = {1554-7477},
  doi = {10.1200/jop.2016.016634},
}