Predicting radiation therapy process reliability using voluntary incident learning system data
Publication
, Journal Article
Howell, C; Tracton, G; Amos, A; Chera, B; Marks, LB; Mazur, LM
Published in: Practical radiation oncology
2019
Duke Scholars
Published In
Practical radiation oncology
Publication Date
2019
Volume
9
Start / End Page
e210 / e217
Publisher
Elsevier
Related Subject Headings
- Risk Management
- Reproducibility of Results
- Radiotherapy Planning, Computer-Assisted
- Radiotherapy
- Radiation Oncology
- Quality Control
- Quality Assurance, Health Care
- Patient Safety
- Medical Errors
- Logistic Models
Citation
APA
Chicago
ICMJE
MLA
NLM
Howell, C., Tracton, G., Amos, A., Chera, B., Marks, L. B., & Mazur, L. M. (2019). Predicting radiation therapy process reliability using voluntary incident learning system data. Practical Radiation Oncology, 9, e210–e217.
Howell, Clark, Gregg Tracton, Alison Amos, Bhishamjit Chera, Lawrence B. Marks, and Lukasz M. Mazur. “Predicting radiation therapy process reliability using voluntary incident learning system data.” Practical Radiation Oncology 9 (2019): e210–17.
Howell C, Tracton G, Amos A, Chera B, Marks LB, Mazur LM. Predicting radiation therapy process reliability using voluntary incident learning system data. Practical radiation oncology. 2019;9:e210–7.
Howell, Clark, et al. “Predicting radiation therapy process reliability using voluntary incident learning system data.” Practical Radiation Oncology, vol. 9, Elsevier, 2019, pp. e210–17.
Howell C, Tracton G, Amos A, Chera B, Marks LB, Mazur LM. Predicting radiation therapy process reliability using voluntary incident learning system data. Practical radiation oncology. Elsevier; 2019;9:e210–e217.
Published In
Practical radiation oncology
Publication Date
2019
Volume
9
Start / End Page
e210 / e217
Publisher
Elsevier
Related Subject Headings
- Risk Management
- Reproducibility of Results
- Radiotherapy Planning, Computer-Assisted
- Radiotherapy
- Radiation Oncology
- Quality Control
- Quality Assurance, Health Care
- Patient Safety
- Medical Errors
- Logistic Models