Performance of a trigger tool for identifying adverse events in oncology Journal Article
| Authors: | Lipitz-Snyderman, A.; Classen, D.; Pfister, D.; Killen, A.; Atoria, C. L.; Fortier, E.; Epstein, A. S.; Anderson, C.; Weingart, S. N. |
| Article Title: | Performance of a trigger tool for identifying adverse events in oncology |
| Abstract: | Purpose Although patient safety is a priority in oncology, few toolsmeasure 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. Methods We performed a retrospective cohort study to assess the performance of an oncology medical record screening tool at a comprehensive cancer center. Thestudy 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. Results 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 highperforming 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 (. 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. Conclusion 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. © 2017 American Society of Clinical Oncology. All rights reserved. |
| Journal Title: | Journal of Oncology Practice |
| Volume: | 13 |
| Issue: | 3 |
| ISSN: | 1554-7477 |
| Publisher: | American Society of Clinical Oncology |
| Date Published: | 2017-03-01 |
| Start Page: | e223 |
| End Page: | e230 |
| Language: | English |
| DOI: | 10.1200/jop.2016.016634 |
| PROVIDER: | scopus |
| PUBMED: | 28095173 |
| PMCID: | PMC5482407 |
| DOI/URL: | |
| Notes: | Conference Paper -- Export Date: 2 May 2017 -- Source: Scopus |
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