.png){kind=icon}
.png){kind=icon}
Return
Predicting Radiation Therapy Process Reliability Using Voluntary Incident Learning System Data
.png){kind=icon}
PRE
AI
DOI：10.1016/j.prro.2018.11.012.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Purpose: This study aimed to present an innovative approach to quantify, visualize, and predict radiation therapy (RT) process reliability using data captured from a voluntary incident learning system, with an overall aim to improve patient safety outcomes. Methods and Materials: We analyzed 111 reported deviations that were tripped and caught within 159 mapped RT process steps included within 7 major stages of RT delivery, 94 of which were any type of quality assurance (QA) controls. This allowed for us to compute the trip rate and fail-to-catch-rate (FCR) per each QA control with the available data. Next, we used a logistic regression model to identify significant variables predictive of FCRs, predicted FCRs for each QA control without available data, and thus, attempted to quantify RT process reliability expressed as percentage of patients with uncaught deviations after treatment planning, before their first treatment, and during treatment delivery. Results: Using the predicted FCRs, we computed the upper 95% likelihood that a deviation remains uncaught in a patient's course of treatment at the following RT process stages: immediately after treatment planning at 10.26%; before the first treatment at 0.0052%; and throughout treatment delivery at 0.0276%. Conclusions: The results suggest that RT process reliability can be predicted and visualized using data from incident learning systems. If implemented and used as a safety metric, this could help RT clinics to proactively maintain their preoccupation with patient safety. RT process reliability may also help guide future work on standardization and continuous improvement of the design of RT QA programs. (C) 2018 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.
Keywords:
QUALITY IMPROVEMENT
PATIENT SAFETY
ERROR
RISK
RADIOTHERAPY
ASSURANCE
HOSPITALS
DELIVERY
IMPACT
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
1.7K
Citations:
3.4K