Radiation Oncology Incident Learning System (RO-ILS): Increasing stakeholder participation for safety and quality improvement.
Bibliographic record
Abstract
232 Background: RO-ILS was launched in 2014 and is free, web-based and in more than 500 U.S. radiation facilities. After RO-ILS was implemented at University of Vermont Medical Center (UVMMC), reporting of radiation incidents decreased and participation by radiation staff was limited. To improve incident reporting and participation, RO-ILS was relaunched for all radiation staff members at UVMMC with emphasis on improved system access and education on RO-ILS programmatic goals. Methods: Prior to RO-ILS, safety/quality incidents at UVMMC were submitted by radiation therapists, dosimetrists and physics staff on paper forms and reviewed monthly by the Radiation Quality Committee. After implementation of RO-ILS in 2016, RO-ILS incidents were reviewed by the UVMMC RO-ILS administrators with no formalized staff feedback. Due to decreasing staff submissions, RO-ILS relaunched September 2018 with increased training, scheduled submission review to radiation staff and identification of department champions. Results: Between April 2014 and May 2019, 270 radiation incidents were reported. Prior to RO-ILS, a median 8 incidents were reported per quarter but decreased to 6 per quarter after RO-ILS. After RO-ILS relaunch, median reported incidents increased to 42 per quarter. Radiation “Near Miss” events pre RO-ILS, post RO-ILS and with RO-ILS relaunch were reduced from 78% to 34% to 9%, while “Operational/Process Improvement” submissions increased from 17% pre RO-ILS to 49% post RO-ILS to 81% after relaunch. After RO-ILS relaunch, staff participation expanded to physicians, nursing and administrative staff for the first time, and physician participation increased from 0 to 50%. Conclusions: Following implementation of RO-ILS at UVMMC, radiation incident reporting initially decreased and the proportion of “Near Miss” reports decreased. After relaunch of RO-ILS, there was substantial increase in incident reporting involving all staff. As RO-ILS evolves at UVMMC, there is continued decrease in near misses and greater emphasis on “Process Improvement”. Continued education, reporting and feedback from RO-ILS submissions is recommended to maintain this high staff participation level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".