ArticleJournal of applied clinical medical physics2025
Radiation incident learning in multi-site centers.
Article in Journal of applied clinical medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Radiation incident learning in multi-site centers.Journal of applied clinical medical physics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIncident learning in large, multi-site hospital systems faces challenges from resource constraints, diverse site cultures, and deviations from system-wide policies. This study introduces a novel, resource-efficient hybrid approach to facilitate cross-site learning and proactive risk management, ultimately enhancing patient safety.
methodsA hybrid approach, integrating statistical, and root cause analyses was used to analyze safety events recorded from 2023 to 2024 at five sites. Events were categorized by time, site, type, and harm score with statistics tracking the event frequency per category. Failure modes (FMs) were identified with summative keywords. The frequency and cross-site correlation of keywords were innovatively displayed by word clouds. The hybrid approach was exemplified at a high-throughput satellite clinic, where treatments and incidents were tracked quarterly. A proactive model was also employed to predict the number of potentially affected patients at this site.
resultsAn analysis of 228 reported events revealed that most incidents occurred within the treatment planning category, where the keywords "carepath" and "contour" were identified as the top two frequently reported FMs, repeated 13 and 10 times respectively. The keywords "contour" and "documentation" appeared across four sites, followed by "carepath" and "couch" which appeared across three sites. At the featured satellite clinic, a total of 18 FMs were identified from yearly 8,714 treatments. The proactive model showed an average risk priority number decreased from 106 to 77, and the estimated number of affected patients decreased from 27 to 20 at this site.
conclusionsWe developed a comprehensive, system-wide approach that enhanced efficiency in incident learning across multi-site clinics. Site-specific shifts in safety culture were effectively benchmarked and cross-site learning was facilitated through measurable endpoints. A novel word cloud analysis was developed, enhancing visualization of cross-site root causes. Additionally, a proactive model was adopted to increase system-wide preparedness and improve patient safety outcomes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.