Implementing Radiation Oncology Care Plans as a foundation for process improvement
Bibliographic record
Abstract
Abstract Background At The Radiation Medicine Program described, the entire radiation therapy (RT) workflow was previously conducted through the use of two electronic programs. It duplicated workflow and created a situation where it was difficult to measure the RT process. Recent enhancements to the electronic medical record facilitated the consolidation of RT planning and treatment workflows into one electronic system. Purpose This report will describe the clinical implementation of electronic Radiation Oncology (RO) Care Plans at a Regional Cancer Centre, and how they can be applied as a foundation for RT process improvements. Impact and outcome A total of 51 Care Plans and 95 IQ Scripts were successfully implemented. The benefits of RO Care Plans include a more streamlined process, removed ambiguity, improved communication, standardised workflow and automation of tasks. In addition, multiple performance indicators can be obtained from the RO Care Plans, such as caseload reports, workflow reports and a ‘white board’. Conclusion The implementation of RO Care Plans serves as a foundation for data-driven process improvement at a local Regional Cancer Centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.105 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".